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TRANSCRIPT
IAEA, TECDOC, Chapter 6
Pecker, Johnson, Jeremi´c Draft Writeup (in progress, total up to 50 pages)
version: 27. September, 2016, 22:15
Contents6 Methods and models for SSI analysis4
6.1 Basic steps for SSI analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
6.2 Direct methods (Jeremic) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
6.2.1 Linear and Nonlinear Discrete Methods . . . . . . . . . . . . . . . . . . . . . . 7
Finite Element Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Equilibrium Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Finite Element Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Finite Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Finite Difference Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Finite Difference Solution Technique . . . . . . . . . . . . . . . . . . . . 10
Nonlinear discrete methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Inelasticity, Elasto-Plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Material Models for Dynamic Modeling . . . . . . . . . . . . . . . . . . . 12
Nonlinear Dynamics Solution Techniques . . . . . . . . . . . . . . . . . . 14
6.3 Sub-structuring methods (Pecker and Johnson) . . . . . . . . . . . . . . . . . . . . . . 14
6.3.1 Sub-Structuring Methods, Principles and Numerical Implementation (Pecker) . . 14
6.3.2 Soil Structure Interaction – CLASSI: A Linear Continuum Mechanics Approach
(Johnson) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.3.3 Discrete methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.3.4 Foundation input motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.4 SSI computational models (Jeremic and Pecker) . . . . . . . . . . . . . . . . . . . . . . 15
6.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.4.2 Soil/Rock Linear and Nonlinear Modelling . . . . . . . . . . . . . . . . . . . . . 15
Effective and Total Stress Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 15
Dry Soil. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Partially Saturated Soil. . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Saturated Soil. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Drained and Undrained Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Drained Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Undrained Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Linear and Nonlinear Elastic Models . . . . . . . . . . . . . . . . . . . . . . . . 19
Equivalent Linear Elastic Models . . . . . . . . . . . . . . . . . . . . . . 20
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Elastic-Plastic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
A Note on Constitutive Level and Global Level Equilibrium. . . . . . . . . 20
6.4.3 Structural models, linear and nonlinear: shells, plates, walls, beams, trusses, solids 22
6.4.4 Contact Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Contact Modeling Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Elastic behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Plastic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Geometry description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.4.5 Structures with a base isolation system . . . . . . . . . . . . . . . . . . . . . . 26
Base Isolation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Base Dissipator Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.4.6 Foundation models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Shallow and Embedded Slab Foundations . . . . . . . . . . . . . . . . . 28
Piles and Shaft Foundations . . . . . . . . . . . . . . . . . . . . . . . . . 28
Deeply Embedded Foundations . . . . . . . . . . . . . . . . . . . . . . . 29
Foundation Flexibility and Base Isolator/Dissipator Systems. . . . . . . . 30
6.4.7 Small Modular Reactors (SMRs) . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.4.8 Buoyancy Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Dynamic Buoyant Stress/Force Modeling. . . . . . . . . . . . . . . . . . 33
6.4.9 Domain Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.4.10 Seismic Load Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
The Domain Reduction Method . . . . . . . . . . . . . . . . . . . . . . . . . . 36
A Note on Free Field Input Motions for DRM. . . . . . . . . . . . . . . . 38
6.4.11 Liquefaction and Cyclic Mobility Modeling . . . . . . . . . . . . . . . . . . . . . 39
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Liquefaction Modeling Details and Discussion . . . . . . . . . . . . . . . . . . . 40
6.4.12 Structure-Soil-Structure Interaction . . . . . . . . . . . . . . . . . . . . . . . . 40
6.4.13 Simplified models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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Simplified Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Simplified, Discrete Soil and Structural Models . . . . . . . . . . . . . . . . . . 43
P-Y and T-Z Springs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Simplified, Continuum Soil Models . . . . . . . . . . . . . . . . . . . . . . . . . 43
Linear Elastic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Stiffness Reduction (G/Gmax) and Damping Curve Models. . . . . . . . 44
6.4.14 General guidance on soil structure interaction modelling and analysis . . . . . . . 44
Model Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Model Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
6.5 Probabilistic response analysis (Jeremic and Johnson) . . . . . . . . . . . . . . . . . . . 45
6.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6.5.2 Probabilistic Response Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.5.3 Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
6.5.4 Random Vibration Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
6.5.5 Stochastic Finite Element Method . . . . . . . . . . . . . . . . . . . . . . . . . 47
Chapter 6
Methods and models for SSI analysis
Jeremi´c et al. (1989-2016)
(50 Pages) (Pecker & Jeremic as Chapter leads)
6.1 Basic steps for SSI analysis
To identify candidate SSI models, model parameters, and analysis procedures, assess:
• The purposes of the SSI analysis (design and/or assessment):
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– Seismic response of structure for design or evaluation (forces, moments, stresses or
deformations, such as story drift, number of cycles of response)
– Input to the seismic design, qualification, evaluation of subsystems supported in the structure
(in-structure response spectra ISRS; relative displacements, number of cycles)
– Base-mat response for base-mat design
– Soil pressures for embedded wall designs
• The characteristics of the subject ground motion (seismic input motion):
– Amplitude (excitation level) and frequency content (low vs. high frequency)
∗ Low frequency content (2 Hz to 10 Hz) affects structure and subsystem design/capacity;
high frequency content (> 20 Hz) only affects operation of mechanical/electrical
equipment and components;
– Incoherence of ground motion;
– Are ground motions 3D? Are vertical motions coming from P or S (surface) waves. If from
S and surface waves, we have full 3D motions. What to do about it (model?) – Refer to
Chapters 3, 4, and 5 for free-field ground motion and seismic input discussions
• The characteristics of the site:
– Idealized site profile is applicable (Section 5.3.3.1)
∗ Linear or equivalent linear soil material model applicable (visco- elastic model
parameters assigned)
∗ Nonlinear (inelastic, elastic-plastic) material model necessary?
– Non-idealized site profile necessary?
– Sensitivity studies to be performed to clarify model requirements for site characteristics
(complex site stratigraphy, inelastic modeling, etc.)?
• The structure characteristics:
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– Expected behavior of structure (linear or nonlinear);
– Based on initial linear model of the structure, perform preliminary seismic response analyses
(response spectrum analyses) to determine stress levels in structure elements;
– If significant cracking or deformations possible (occur) such that portions of the structure
behave nonlinearly, refine model either approximately introducing cracked properties or
model portions of the structure with nonlinear elements;
– For expected structure behavior, assign material damping values;
• The foundation characteristics:
– Effective stiffness is rigid due to base-mat stiffness and added stiffness due to structure being
anchored to base-mat, e.g., honey-combed shear walls anchored to base-mat;
– Effective stiffness is flexible, e.g. if additional stiffening by the structure is not enough to claim
rigid; or for strip footings;
The end result is to identify the important elements of SSI to be considered in the analysis of the
subject structure:
• Seismic input as defined in Chapters 4 and 5;
• Equivalent linear vs. nonlinear (inelastic, elastic-plastic) soil behavior; equivalent linear –
substructure approach to SSI acceptable;
• Linear, equivalent linear, or nonlinear (inelastic) structure behavior; equivalent linear implements
approximate stiffness degradation for structures; linear/equivalent linear - substructure approach
to SSI acceptable;
• Foundation to be modeled as behaving rigidly (e.g., first stage of multi-stage analysis) or flexibly;
Select SSI model and analysis procedure.
For the SSI model and analysis to be implemented, confirm existence of
• Verification for all models, elements, etc.
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• Validation for all (as many as possible) models, etc.
• Determine the application domain for all models, elements, etc. (Application domain is discussed in
some detail in section on Verification and Validation, in Chapter 8).
Before initial results are available, make estimates of what type of behavior you expect to see
(accomplished in steps above and confirmed herein). In (both) cases, if results are similar or not similar to
your pre-analysis expectations do the following investigations:
• investigate alternative parameters, in order to understand sensitivity of results to parameter varia-
tions,
• investigate alternative models (with different degree of fidelity, simplifications, etc.), in order to
understand sensitivity of results to (simplifying) modeling assumptions
Modeling sequence should be:
• Linear elastic, model components first then slowly complete the model:
– soil only, static loads (point, self-weight, etc.); dynamic loads (point loads, etc.); free field
ground motions (see chapter 4 and 5)
– components of structural model only (for example containment only, internal structure only,
etc.), and then full structural model (just the structure, no soil), static loads (self weight in
three directions to verify model and load paths, point loads to verify model and load paths);
then dynamic loads (point loads, and seismic loads)
– complete structure and foundation, (apply same load scenarios as above)
– complete structure, foundation, soil system, (apply same load scenarios as above)
• Equivalent linear modeling, and observe changes in response, to determine possible plastification
effects. It is very important to note that it is still an elastic analysis, with reduced (equivalent) linear
stiffness. Reduction in secant stiffness really steams from plastification, although plastification is
not explicitly modeled, hence an idea can be obtained of possible effects of reduction of stiffness.
One has to be very careful with observing these effects, and focus more on verification of model
(for example wave propagation through softer soil, frequencies will be damped, etc...).
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• Nonlinear/inelastic modeling, slowly introduce nonlinearities to test models, convergence and
stability, in all the components as above.
• Investigate sensitivities for both linear elastic and nonlinear/inelastic simulations!
6.2 Direct methods (Jeremic)
6.2.1 Linear and Nonlinear Discrete Methods
Linear and nonlinear mechanics of solids and structures relies on equilibrium of external and internal
forces/stresses. such equilibrium can be expressed as
σij,j = fi − uρ ¨i (6.1)
where σij,j is a small deformation (Cauchy) stress tensor, fi are external (body (fiB) and surface (fi
S) ) forces,
ρ is material density and u¨i are accelerations. Inertial forces uρ ¨i follow from d’Alembert’s principle
(D’Alembert, 1758).
The above equation forms a basis for both Finite Element Method (FEM) and Finite Difference
Method (FDM). Above equation can be pre-multiplied with virtual displacements uδ i and then integrated
by parts to obtain the weak form, as further elaborated below in section 6.2.1. This equation can also be
directly solved using finite differences, as noted in section 6.2.1.
It is important to note that equation 6.1 is usually not satisfied in either FEM or FDM. Rather is is
satisfied in an approximate fashion, with a smaller or large deviation, depending on type of FEM or FDM
used.
Finite Element Method
Equilibrium Equations Development of finite element equations is efficiently done by using principle of
virtual displacements. This principle states that the equilibrium of the body requires that for any
compatible, small virtual displacements, which satisfy displacement boundary conditions imposed onto
the body, the total internal virtual work is equal to the total external virtual work.
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Finite Element Equations After some manipulations (Zienkiewicz and Taylor, 1991a,b), we can write the
finite element equations as:
MPQ u¨¯P + CPQ u¯˙P + KPQ u¯P = FQ P,Q = 1,2,...,(#ofDOFs)N (6.2)
where MPQ is a mass matrix, CPQ is a damping matrix, KPQ is a stiffness matrix and FQ is a force vector.
Damping matrix CPQ cannot be directly developed from a formulation for a single phase solid or structure.
In other words, viscous damping is a results of interaction of fluid and solid/structure and is not part of
this formulation (Argyris and Mlejnek, 1991). Viscous damping can, however, be added through
viscoelastic constitutive material models and through Rayleigh damping, or a more general, Caughey
damping. Viscous damping can also be added through viscoelastic constitutive material models.
In general Caughey damping is defined as (Semblat, 1997):m−1
C = [M] X
aj([M]−1[K])j (6.3)j=0
where the order used depends on number of modes to be considered for damping in the problem. The
second order Caughey damping, is also known as a Rayleigh damping, with j = 1 in Equation (6.3).
In reality, damping matrix (more precisely, damping resulting from viscous effects) results from an
interaction of soils and/or structures with surrounding fluids (Argyris and Mlejnek, 1991). For porous
solid with pore space filled with fluid, a direct derivation of damping matrix is possible (Jeremi´c et al.,
2008).
Stiffness matrix KPQ can be linear (elastic) or nonlinear, elastic-plastic.
Finite element analysis comprises a discretization of a solid and/or structure into an assemblage of
discrete finite elements. Finite elements are connected at nodal points.
It is very important to note that the finite element method is an approximate method. Generalized
displacement solutions at nodes are approximate solutions. A number of factors controls the quality of
such approximate solutions. For example it can be shown (Zienkiewicz and Taylor, 1991a,b; Hughes,
1987; Argyris and Mlejnek, 1991) that an increase in a number of nodes, finite elements (refinement of
discretization) and a reduction of increments (loads steps or time step size) will lead to a more accurate
solution. However, this refinement in mesh discretization and reduction of step size, will lead to longer
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run times. A fine balance needs to be achieved between accuracy of the solution and run time. This is
where verification procedures (described in some details in section 8.) become essential. Verification
procedures provide us magnitudes of errors that we can expect from our finite element (approximate)
solutions. Results from verification procedures should thus be used to decide appropriate discretization
(in space (mesh) and load/time) to achieve desired accuracy in solution.
Finite Elements There exist different types of finite elements. They can be broadly classified into:
• Solid elements (3D brick, 2D quads etc.)
• Structural elements (truss, beam, plate, shell, etc.)
• Special Elements (contacts, etc.)
Solid finite elements usually feature displacement unknowns in nodes, 3 displacements for 3D
elements, and 2 unknowns displacements for 2D elements. The most commonly used 3D solid finite
elements are bricks, that can have 8, 20, and 27 nodes. In 3D, tetrahedral elements (4 and 10 nodes) are
also popular due to their ability to be meshed into any volume, while solid brick elements sometimes can
have problems with meshing. In 2D most common are quads, with 4, 8 or 9 nodes (Zienkiewicz and
Taylor, 1991a,b; Bathe, 1996a). Triangular elements are also popular (3, 6 and 10 nodes), due to the
same reason, that is triangles can be meshed in any plane shape, unlike quads. Two dimensional finite
elements can approximate plane stress, plane strain or axisymmetric continuum. It is important to note
that 3 node triangular elements feature constant strain field, and thus lead to discontinuous strains, and
possibility of mesh locking.
Solid finite elements are also used to model coupled problems where porous solid (soil skeleton) is
coupled with pore fluid (water), as described by Zienkiewicz and Shiomi (1984); Zienkiewicz et al. (1990,
1999). These elements and the underlying formulation will be described in some detail in section 6.4.11.
Structural finite elements use integrated section stress to develop section generalized forces (normal,
transversal and moments). Truss elements can have 2 or 3 nodes. Beams usually have 2 nodes, although
3 node beam elements are also used (Bathe, 1996a). Most beam elements are based on a Euler-Bernoulli
beam theory, which means that they do not take into account shear deformation, and thus should only
be used for slender beams, where the ratio of beam length to (larger) beam cross section dimension is
more than 10 (some authors lower this number to 5) (Bathe, 1996a). For beams that are not slender,
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Timoshenko beam element is recommended (Challamel, 2006), as it explicitly takes into account shear
deformation.
Plate, wall and shell elements are usually quads or triangles. Plate finite elements model plate
bending without taking into account forces in the plane of the plane plate. Main unknowns are
transversal displacement and two bending (in plane) moments.
In plane forcing and deformation is modeled using wall elements that are very similar to plane stress
2D elements noted above. In plane nodal rotations are usually not taken into account. If possible, it is
beneficial to include rotational (drilling) degree of freedom (Bergan and Felippa, 1985), so that wall
elements has three degrees of freedom per node (two in plane displacements and out of plane rotation).
Shell element is obtained by combining plate bending and wall elements.
Special elements are used for modeling contacts, base isolation and dissipation devices and other
special structural and contact mechanics components of an NPP soil-structure system (Wriggers, 2002).
Finite Difference Method
Finite different methods (FDM) operate directly on dynamic equilibrium equation 6.1, when it is
converted into dynamic equations of motion. The FDM represents differentials in a discrete form. It is
best used for elasto-dynamics problems where stiffness remains constant. In addition, it works best for
simple geometries (Semblat and Pecker, 2009), as finite difference method requires special treatment
boundary conditions, even for straight boundaries that are aligned with coordinate axes.
Finite Difference Solution Technique The FDM solves dynamic equations of motion directly to obtain
displacements or velocities or accelerations, depending on the problem formulation. Within the context
of the elasto-dynamic equations, on which FDM is based, elastic-plastic calculations are performed by
changes to the stiffness matrix, in each step of the time domain solution.
Nonlinear discrete methods
Nonlinear problems can be separated into (Felippa, 1993; Crisfield, 1991, 1997; Bathe, 1996a)
• Geometric nonlinear problems, involving smooth nonlinearities (large deformations, large strains),
and
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• Material nonlinear problems, involving rough nonlinearities (elasto-plasticity, damage)
Main interest in modeling of soil structure interaction is with material nonlinear problems. Geometric
nonlinear problems are involve large deformations and large strains and are not of much interest here.
It should be noted that sometimes contact problems where gaping occurs (opening and closing or
gaps) are called geometric nonlinear problems. They are not geometric nonlinear problems for cases of
interest here, namely, gap opening and closing between foundations. Problems where gap opens and
closes are material nonlinear problems where material stiffness (and internal forces) vary between very
small values (zeros in most formulations) when the gap is opened, and large forces when the gap is
closed.
Material nonlinear problems can be modeled using
• Linear elastic models, where linear elastic stiffness is the initial stiffness or the equivalent elastic
stiffness (Kramer, 1996; Semblat and Pecker, 2009; Lade, 1988; Lade and Kim, 1995).
– Initial stiffness uses highest elastic stiffness of a soil material for modeling. It is usually used
for modeling small amplitude vibrations. These models can be used for 3D modeling.
– Equivalent elastic models use secant stiffness for the average high estimated strain (typically
65% of maximum strain) achieved in a given layer of soil. Eventual modeling is linear elastic,
with stiffness reduced from initial to approximate secant. These models should really be only
used for 1D modeling.
• Nonlinear 1D models, that comprise variants of hyperbolic models (described in section 3.2), utilize
a predefined stress-strain response in 1D (usually shear stress τ versus shear strain γ) to produce
stress for a given strain.
There are other nonlinear elastic models also, that define stiffness change as a function of stress
and/or strain changes (Janbu, 1963; Duncan and Chang, 1970; Hardin, 1978; Lade and Nelson,
1987; Lade, 1988)
These models can successfully model 1D monotonic behavior of soil in some cases. However, these
models cannot be used in 3D. In addition, special algorithmic measures (tricks) must be used to
make these models work with cyclic loads.
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• Elastic-Plastic material modeling can be quite successfully used for frys frys both monotonic, and
cyclic loading conditions (Manzari and Dafalias, 1997; Taiebat and Dafalias, 2008; Papadimitriou et
al., 2001; Dafalias et al., 2006; Lade, 1990; Pestana and Whittle, 1995). Elastic plastic modeling can
also be used for limit analysis (de Borst and Vermeer, 1984).
Inelasticity, Elasto-Plasticity
Inelastic, elastic-plastic modeling relies on incremental theory of elasto-plasticity to solve elastic-plastic
constitutive equations, with appropriate/chosen material model. Most solutions are strain driven, while
there exist techniques to exert stress and mixed control (Bardet and Choucair, 1991). There are two
levels of nonlinear/inelastic modeling when elasto-plasticity is employed:
• Constitutive level, where nonlinear constitutive equations with appropriate material models are
solved for stress and stiffness (tangent or consistent) given strain increment
• Global, finite element level, where nonlinear dynamic finite element equations are solved for given
dynamic loads and current (elastic-plastic) stiffness (tangent or consistent).
Material Models for Dynamic Modeling At the constitutive level, general 3D strain increments
(incremental strain tensor, or in other words, increments in all six independent components of strain,
normal (σxx, σyy, and σyy) and shear (σxy, σyz, and σzx)) is driving the nonlinear constitutive solution.
Proper elastic-plastic material models must be chosen to obtain results. Elastic-plastic material models,
consist of four main components:
• Elasticity, that governs the elastic response, before material yields.
• Yield function, a function in stress and internal variables (shear strength, friction angle, back-stress,
etc.) space, that separates elastic region from the elastic-plastic region.
• Plastic flow directions, that provide directions of plastic strain, once material plastifies. Magnitude
of plastic strain is obtained from the solution of constitutive equations.
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• Hardening/softening rules, that control evolution of yield surface and plastic flow direction, during
plastic deformation. There are four main types of hardening/softening rules, that can be combined
between each other (for example isotropic and kinematic hardening models can be combined):
– Perfect plastic material behavior, where yield function and plastic flow directions do not
change during plastic deformation. There is no internal variable for this type of harden-
ing/softening.
– Isotropic hardening/softening material behavior, where yield function and plastic flow
directions change isotropically (proportionally). This type of hardening/softening is only good
for monotonic loading and should not be used for cyclic loading. Internal variables are of
scalar type, for example friction angle, shear strength, maximum isotropic confinement, etc.
– Kinematic hardening where yield function and plastic flow direction either translate (works
well for metals and total stress analysis of undrained, soft clays), or rotate (works well for
soils, concrete, rock and other pressure sensitive materials). This type of hardening (usually it
is only used for hardening, there is no softening) is good for cyclic loading. Internal variables
are the back stress.
– Distortional hardening where yield function and plastic flow direction can have a general
change in stress and internal variable space. This type of hardening/softening is the most
general case and contains all the previous hardening/softening cases, however it is rarely
used, as it requires a large number of tests.
Dynamic modeling, where stresses and strain cyclically change requires models that feature
kinematic hardening. In case of pressure sensitive materials, like soil, concrete and rock, rotational
kinematic hardening is used. For materials that do not have pressure sensitivity (metals, and saturated
clays when modeled with a total stress approach (as opposed to effective stress approach (Jeremi´c et al.,
2008)), translational kinematic hardening is used.
There exist a number of models developed recently that can produce satisfactory modeling of
dynamic response of geomaterials (Dafalias and Manzari, 2004; Taiebat and Dafalias, 2008; Dafalias et al.,
2006; Mr´oz et al., 1979; Mroz and Norris, 1982; Prevost and Popescu, 1996). Of particular importance is
availability of calibration tests, and addressing the issue of uncertainty and sensitivity of material
response to changes in parameters.
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It is also important to address the issue of spatial variability and uncertainty in material parameters
for soils, as the ensuing response can also be quite uncertain. The issue of spatial variability and
uncertainty in material modeling will be addressed in more detail in section will be addressed in section
6.5.
Nonlinear Dynamics Solution Techniques On the global, finite element level, finite element equations are
solved using time marching algorithms. Most often used are Newmark algorithm (Newmark, 1959) and
Hilber-Hughes-Taylor (HHT) α algorithm (Hilber et al., 1977). Other algorithms (Wilson θ, l’Hermite, etc.)
also do exist Argyris and Mlejnek (1991); Hughes (1987); Bathe and Wilson (1976), however they are
used less frequently. Both Newmark and HHT algorithm allow for numerical damping to be included in
order to damp out higher frequencies that are introduced artificially into FEM models by discretization of
continua into discrete finite elements.
Solution to the dynamic equations of motion can be done by either enforcing or not enforcing
convergence to equilibrium. Enforcing the equilibrium usually requires use of Newton or quasi Newton
methods to satisfy equilibrium within some tolerance. This results in a (much) longer running times,
however, provided that the convergence tolerance is small enough, analyst is assured that his/her
solution is within proper material response and equilibrium. Solutions without enforced equilibrium are
faster, and if they are done using explicit solvers, there is a requirement of small time step, which can
then slow down the solution process.
6.3 Sub-structuring methods (Pecker and Johnson)
6.3.1 Sub-Structuring Methods, Principles and Numerical Implementation (Pecker)
NOTE: THIS is where Alain’s section 6.3 is to be merged!
6.3.2 Soil Structure Interaction – CLASSI: A Linear Continuum Mechanics Approach (Johnson)
NOTE This is where Jim’s section 6.3 CLASSI is to be merged
6.3.3 Discrete methods
(finite elements and finite difference)
15
6.3.4 Foundation input motion
(Reference to section 4.5 and 5.1)
6.4 SSI computational models (Jeremic and Pecker)
6.4.1 Introduction
Soil structure interaction computational models are developed with a focus on three components of the
problem:
• Earthquake input motions, encompassing development of 1D or 2D or 3D motions, and their
effective input in the SSI model,
• Soil/rock adjacent to structural foundations, with important geological (deep) and site (shallow)
conditions near structure, contact zone between foundations and the soil/rock, and
• Structure, including structural foundations, embedded walls, and the superstructure
It is advisable to develop models that will provide enough detail and accuracy to be able to address
all the important issues. For example, for modeling higher frequencies of earthquake motions, analyst
needs to develop finite element mesh that will be capable to propagate those frequencies and to
document influence of numerical/mesh induced dissipation/damping of frequencies.
6.4.2 Soil/Rock Linear and Nonlinear Modelling
Effective and Total Stress Analysis
Soil and rock adjacent to structural foundations can be either dry or fully (or partially) saturated
(Zienkiewicz et al., 1990; Lu and Likos, 2004).
Dry Soil. In the case of dry soil, without pore fluid pressures, it is appropriate to use models that are only
dependent on single phase stress, that is, a stress that is obtained from applying all the loads (static
and/or dynamic) without any consideration of pore fluid pressures.
16
Partially Saturated Soil. For partially saturated soil, effective stress principle (see equation 6.4 below)
must also the include influence of gas (air) present in pore of soils. There are a number of different
methods to do that (Zienkiewicz et al., 1999; Lu and Likos, 2004), however computational frameworks
that incorporate those methods are not yet well developed. Main approaches to modeling of soil
behavior within a partially saturated zone of soil (a zone where water rises due to capillary effects) are
dependent on two main types of partial saturation
• Voids of soil fully saturated with fluid mixed with air bubbles, water in pores is fully connected and
can move and pressure in the mixture of water and air can propagate, with reduced bulk stiffness
of water-air mixture.
This type of partial saturation can be modeled using fully saturated approaches, given in section
6.4.11 below. It is noted that bulk modulus of fluid-air mixture is (much) lower that that of fluid
alone, and to be tested for. Therefor, only methods that assume fluid to be compressible should be
used (u − p − U, u − U, see section 6.4.11 for details). In addition, permeability will change from a
case of just fluid seeping through the soil, and additional testing for permeability of water-air
mixture is warranted. It is also noted, that since this partial saturation is usually found above water
table, (capillary rise), hydrostatic pore pressure can be suction.
• Voids of soil are full of air, with water covering thin contact zone between particles, creating water
menisci, and contributing to the apparent cohesion of cohesionless soil material (think of wet sand
at the beach, there is an apparent cohesion, until sand dries up).
This type of partial saturation can be modeled using dry (unsaturated) modeling, where
elasticplastic material models used are extended to include additional cohesion, that arises from
thin water menisci connecting soil particles.
Saturated Soil. In the case of full saturate, effective stress principle (Terzaghi et al., 1996) has to be
applied. This is essential as for porous material (soil, rock, and sometimes concrete) mechanical behavior
is controlled by the effective stresses. Effective stress is obtained from total stress acting on material (σij),
with reductions due to the pore fluid pressure:
(6.4)
17
where is effective stress tensor, σij is total stress tensor, δij is Kronecker delta (a diagonal matrix with
numbers 1 on a diagonal and numbers 0 on non-diagonal positions, that is δij = 1, when i=j, and δij = 0,
when i 6= j), and p is the pore fluid pressure. We use standard mechanics of materials convention that
tensile components of stress are positive, and so the pore fluid pressure p is negative when in
compression (Zienkiewicz et al., 1999). All the mechanical behavior of soils and rock is a
0 function of the effective stress σij, which is affected by a full coupling with the pore fluid, through a pore fluid pressure p.
A Note on Clays. Clay particles (platelets) are so small that their interaction with water is quite
different from silt, sand and gravel. Clays feature chemically bonded water layer that surrounds clay
platelets. Such water does not move freely and stays connected to clay platelets under working loads.
Usually, clays are modeled as fully saturated soil material. In addition, clays feature very small
permeability, so that, while the effective stress principle (from Equation 6.4) applies, pore fluid pressure
does not change during fast (earthquake) loading. Hence clays should be analyzed using total stress
stress analysis, where the initial total stress is a stress that is obtained from an effective stress calculation
that takes into account hydrostatic pore fluid pressure. In other words, slays are modeled using
undrained, total stress analysis, using effective stress (total stress reduced by the pore fluid pressure) for
initializing total stress at the beginning of loading.
Drained and Undrained Modeling
Depending on the permeability of the soil, on relative rate of loading and seepage, and on boundary
conditions (Atkinson, 1993), a decision needs to be made if analysis will be performed using drained or
undrained behavior. Permeability of soil (k) can range from k > 10−2m/s for gravel, 10−2m/s > k >
10−5m/s for sand, 10−5m/s > k > 10−8m/s for silt, to k < 10−8m/s for clay. If we assume a unit hydraulic
gradient (reduction of pore fluid pressure/head of 1m over the seepage path length of 1m), then for a
dynamic loading of 10 − 30 seconds (earthquake), and for a semi-permeable silt with k = 10−6m/s, water
can travel few millimeters. However, pore fluid pressure will propagate (much) faster (further) and will
affect mechanical behavior of soil skeleton. This is due to high bulk modulus of water (Kw = 2.15×105
kN/m2), which results in high speed of pressure waves in saturated soils. Thus a simple rule is that for
earthquake loading, for gravel, sand and permeable silt, relative rate of loading and seepage requires use
18
of drained analysis. For clays, and impermeable silt, it might be appropriate to use undrained analysis for
such short loading.
Drained Analysis Drained analysis is performed when permeability of soil, rate of loading and seepage,
and boundary conditions allow for full movement of pore fluid and pore fluid pressures during loading
event. As noted above, use of the effective stress for the analysis is essential, as is modeling of full
coupling of pore fluid pressure with the mechanical behavior of soil skeleton. This is usually done using
theory of mixtures (Green and Naghdi, 1965; Eringen and Ingram, 1965; Ingram and Eringen, 1967;
Zienkiewicz and Shiomi, 1984; Zienkiewicz et al., 1999) and will be elaborated upon in some detail in
section 6.4.11. During loading events, pore fluid pressures will dynamically change (pore fluid and pore
fluid pressures will displace) and will affect the soil skeleton, through effective stress principle. All
nonlinear (inelastic) material modeling applies to the effective stresses ( ). Appropriate inelastic
material models that are used for modeling of soil (as noted in section 6.4.2) should be used.
Undrained Analysis Undrained analysis is performed when permeability of soil, rate of loading and
seepage, and boundary conditions do not allow movement of pore fluid and pore fluid pressures during
loading event. This is usually the case for clays and for low permeability silt. There are three main
approaches to undrained analysis:
• Total stress approach, where there is no generation of excess pore fluid pressure (pore fluid
pressure in addition to the hydraulic pressure), and soil is practically impermeable (clays and low
permeability silt). In this case hydrostatic pore fluid (water) pressures are calculated prior to
analysis, and effective stress is established for the soil. This approach assumes no change in pore
fluid pressure. This usually happens for clays and low permeability silt, and due to very low
permeability of such soils, a total stress analysis is warranted, using initial stress that is calculate
based on an effective stress principle and known hydrostatic pore fluid pressure. Since pore fluid
pressure does not affect shear strength (Muir Wood, 1990), for very low permeability soils
(impermeable for all practical purposes), it is convenient to perform elastic-plastic analysis using
undrained shear strength (cu) within a total strain setup. Since only shear strength is used, and all
the change in mean stress is taken by the pore fluid, material models using von Mises yield criteria
can be used.
19
• Locally undrained analysis where excess pore fluid pressure (change from hydrostatic pore
pressure) can be created. Excess pore fluid pressures can be created, due to compression effects
on low permeability soil (usually silt). On the other hand, pore fluid suction can also be created due
to dilatancy effects within granular material (silt). Due to very low permeability, pore fluid and pore
fluid pressure does not move during loading, and hence, effective stress will change, and will affect
constitutive behavior of soil. Analysis is essentially undrained, however, pore fluid pressure can and
will change locally due to compression or dilatancy effects in granular soil. Appropriate inelastic
(elastic-plastic) material models that are used for modeling of soil (as noted in section 6.4.2) should
be used, while constitutive integration should take into account local undrained effects and
convert any change in voids into excess pore fluid pressure change (excess pore pressure).
• Very low permeability soils, that can, but to not have to develop excess pore fluid pressure can also
be analyzed as fully drained continuum, while using very low, realistic permeability. In this case,
although analysis is officially drained analysis, results will be very similar if not the same as for
undrained behavior (one of two approaches above) due to use of very low, realistic permeability.
Effective stress analysis is used, with explicit modeling of pore fluid pressure and a potential for
pore fluid to displace and pore fluid pressure to move. However, due to very low permeability, and
fast application of load (earthquake) no fluid will displace and no pore fluid pressure will
propagate.
This approach can be used for both cases noted above (total stress approach and locally undrained
approach). While this approach is actually explicitly allowing for modeling of pore fluid movement,
results for pore fluid displacement should show no movement. In that sense, this approach is
modeling more variables than needed, as some results are known before simulations (there will be
no movement of water nor pore fluid pressure). However, this approach can be used to verify
modeling using the first two undrained approaches, as it is more general.
It is noted that globally undrained problems, where for example soil is permeable, but boundary
conditions prevent water from moving, should be treated as drained problems, while appropriate
boundary conditions should prevent water from moving across impermeable boundaries.
20
Linear and Nonlinear Elastic Models
Linear and nonlinear elastic models are used for soil, rock and structural components. Linear elastic
model that are used are usually isotropic, and are controlled by two constants, the Young’s modulus E
and the Poisson’s ratio ν, or alternatively by the shear modulus G and the bulk modulus K.
Nonlinear elastic models are used mostly in soil mechanics, There are a number of models proposed
over years, tend to produce initial stiffness of a soil for given confinement of over-consolidation ratio
(OCR) (Janbu, 1963; Duncan and Chang, 1970; Hardin, 1978; Lade and Nelson, 1987; Lade, 1988).
Anisotropic material models are mostly used for modeling of usually anisotropic rock material
(Amadei and Goodman, 1982; Amadei, 1983).
Equivalent Linear Elastic Models Equivalent elastic models are linear elastic models where the elastic
constants were determined from nonlinear elastic models, for a fixed shear strain value. They are secant
stiffness 1D models and usually give relationship between shear stress ( τ = σxz) and shear strain (
). Determination of secant shear stiffness is done iteratively, by performing 1D wave
propagation simulations, and recording average high estimated strain (65% of maximum strain) for each
level/depth. Such representative shear strain is then used to determine reduction of stiffness using
modulus reduction curves (G/Gmax and the analysis is re-run. Stable secant stiffness values are usually
reached after few iterations, typically 5-8. It is important to emphasize that equivalent elastic modeling is
still essentially linear elastic modeling, with changed stiffness. More details are available in sections
3.2 and 4.5.
Elastic-Plastic Models
Elastic plastic modeling can be used in 1D, 2D and full 3D. A number of material models have been
developed over years for both monotonic and cyclic modeling of materials. Material models for soil
(Manzari and Dafalias, 1997; Taiebat and Dafalias, 2008; Papadimitriou et al., 2001; Dafalias et al.,
2006; Lade, 1990; Pestana and Whittle, 1995; Prevost and Popescu, 1996; Mroz and Norris, 1982), rock
(Lade and Kim, 1995; Hoek et al., 2002; Vorobiev, 2008) have been developed over last many years.
It should be noted that 3D elastic plastic modeling is the most general approach to material modeling
of soils and rock. If proper models are used (see section 6.2.1) it is possible to achieve modeling that is
done using simplified modeling approaches described above (linear elastic, equivalent linear elastic,
21
modulus reduction curves, etc.). However, calibration of models that can achieve such modeling
sophistication requires expertise. The payoff is that important material response effects, that are usually
neglected if simplified models are used, can be taken into account and properly modeled. As an example,
soil volume change during shearing is a first order effects, however it is not taken into account if modulus
reduction curves are used.
A Note on Constitutive Level and Global Level Equilibrium. There are two main types of algorithms
for constitutive integrations:
• Explicit or Forward Euler, is an algorithm that produces tangent stiffness tensor on the constitutive
level. This algorithm does not enforce equilibrium and error in constitutive integrations (drift from
the yield surface) is accumulated. This algorithm is simpler and faster than the implicit algorithm
(next item) and is implemented and used in most (all) computer programs.
• Implicit or Backward Euler) is an algorithm that produces algorithmic (consistent) stiffness tensor
(matrix) that can produce very fast convergence (quadratic for Newton scheme) on the global,
finite element equilibrium iterations. This algorithm is iterative and does enforce equilibrium
(within user specified tolerance). It is usually slower than the explicit algorithm (see above) and
implementation can be quite complicated, particularly for elastic plastic material models for soil
and concrete (Crisfield, 1987; Jeremi´c and Sture, 1997; Jeremi´c, 2001).
On the global, finite element level, there are two ways to advance the solutions
• Solution advancement without enforcing the equilibrium. In this case, solutions is produced using
current tangent stiffness matrix (relying on the tangent stiffness tensor, developed on the
constitutive level, as noted above). For each step of loading (static or dynamic) difference between
applied loads and internal loads (stresses) is not checked for. This means that error in unbalanced
forces is accumulating as computations progress. Usual remedy is to make steps small enough so
that error is also reduced. However this reduction in step size (or time step size) can significantly
increase computational times. In one specific instance, if lumped mass matrix is used, instead of a
consistent mass matrix (which is theoretically more accurate), solution of a large system of
equations can be completely circumvented. For particular explicit dynamic computations, only
inverse of a diagonal mass matrix is required, which is trivial to obtain.
22
• Solution advancement with enforcement of the equilibrium. In this case, equilibrium is explicitly
checked for, and if unbalanced forces are not balanced within certain (user specified) tolerance, an
iterative scheme is used until equilibrium is achieved (within tolerance). Alternative method for
ending iterations (instead of achieving equilibrium) is to check for iterative displacements and
place a low limit below which iterations are not worth while any more and therefor end them. The
most commonly used iteration methods are based on Newton iterative scheme Crisfield (1984).
This approach is computationally demanding, however it does benefit the solution as it yields
(close to) equilibrium solutions. In addition, if consistent (algorithmic) stiffness is used on the
constitutive level (see Implicit constitutive algorithm above), a fast convergence (sometimes even
close to quadratic) is achieved.
Concluding note for both constitutive and global level solution advancement is that simpler methods
(explicit, no equilibrium check) will lead to accumulating error (unbalance stress and force) and will thus
render solutions that are not in equilibrium and are possibly quite wrong. This can be remedied by
reducing step size (time increment), however computational times are then becoming long On the other
hand, methods that enforce equilibrium (within tolerance) are (much) more complicated to develop,
implement and execute, yet they enforce equilibrium (again within tolerance).
6.4.3 Structural models, linear and nonlinear: shells, plates, walls, beams, trusses, solids
Linear and nonlinear structural models are not used as much in the industry for modeling and simulation
of behavior of nuclear power plants (NPP). One of the main reasons is that NPPs are required to remain,
effectively elastic during earthquakes. Nevertheless modeling of nonlinear effects in structures remains a
viable proposition. It is important to note that inelastic behavior of structural components
(trusses, beams, walls, plates, shells) features a localization of deformation (Rudnicki and Rice, 1975).
While localization of deformation is also present in soils, soils are more ductile medium (unless they are
very dense) and so inelastic treatment of deformation in soils, with possible localization, is more benign
than treatment of localization of deformation in brittle concrete. Significant work has been done in
modeling of nonlinear effects in mass concrete and concrete beams, plates, walls and shells (Feenstra,
1993; Feenstra and de Borst, 1995; de Borst and Feenstra, 1990; de Borst, 1987, 1986; de Borst, 1987; de
Borst et al., 1993; Bi´cani´c et al., 1993; Kang and Willam, 1996; Rizzi et al., 1996; Menetrey and Willam,
23
1995; Carol and Willam, 1997; Willam, 1989; Willam and Warnke, 1974; Etse and Willam, 1993; Scott et
al., 2004, 2008; Spacone et al., 1996a,b; Scott and Fenves, 2006).
The main issue is still that concrete structural elements still develop plastic hinges (localized
deformation zones). Finite element results with localized deformation are known to be mesh dependent
(change of mesh will change the result), and as such are hard to verify. Recent work on rectifuing this
problem (Larsson and Runesson, 1993) shows promises, however these methods are still not widely
accepted. One possible, rather successful solution relies on classical developments of Cosserat continua
(Cosserat, 1909), where results looked very promising (Dietsche and Willam, 1992), however
sophistication required by such analysis and lack of programs makes this approach still very exotic.
6.4.4 Contact Modeling
In all soil-structure systems, there exist interfaces between structural foundations and the soil or rock
beneath. There are two main modes of behavior of these interfaces, contacts:
• Normal contact where foundation and the soil/rock beneath interact in a normal stress mode. This
mode of interaction comprises normal compressive stress, however it can also comprise gap
opening, as it is assumed that contact zone has zero tensile strength.
• Shear contact where foundation and the soil/rock beneath can develop frictional slip.
Contact description provided here is based on recent work by Jeremi´c (2016) and Jeremi´c et al.
(1989-2016).
Modeling of contact is done using contact finite elements. Simplest contact elements are based on a
two node elements, the so called joint elements which were initially developed for modeling of rock
joints. Typically normal and tangential stiffness were used to model the pressure and friction at the
interface (Wriggers, 2002; Haraldsson and Wriggers, 2000; Desai and Siriwardane, 1984).
The study of two dimensional and axisymmetric benchmark examples have been done by Olukoko et
al. (1993) for linear elastic and isotropic contact problems. Study was done considering Coulomb’s law for
frictional behavior at the interface. In many cases the interaction of soil and structure is involved with
frictional sliding of the contact surfaces, separation, and re-closure of the surfaces. These cases depend
on the loading procedure and frictional parameters. Wriggers (2002) discussed how frictional contact is
important for structural foundations under loading, pile foundations, soil anchors, and retaining walls.
24
Two-dimensional frictional polynomial to segment contact elements are developed by Haraldsson
and Wriggers (2000) based on non-associated frictional law and elastic-plastic tangential slip
decomposition. Several benchmarks are presented by Konter (2005) in order to verify the the results of
the finite element analyses performed on 2D and 3D modelings. In all proposed benchmarks the results
were approximated pretty well with a 2D or an axisymmetric solutions. In addition, 3D analyses were
performed and the results were compared with the 2D solutions.
Contact Modeling Formulation
The formulation for contact is represented by a discretization which establishes constraint equations and
contact interface constitutive equations on a purely nodal basis. Such a formulation is called node-
tonode contact (Wriggers, 2002). The variables adopted to formulate the model are shown in Figure 6.1:
the force (F) and displacement vectors (u).
Figure 6.1: Forces and relative displacements of the element.
Each vector is composed of three terms: the first one acts along the longitudinal direction (nlocal) whereas the other two components lie on the orthogonal plane (mlocal and llocal). The total relative displacement additively decomposed into elastic and plastic components (6.6).
F = [p ; t]T ; u = [v; gs]T (6.5)
el h
el eliT
pl h
pl pliT
el pl(6.6)
25
u = v ; gs ; u = v ; gs ; u = u + u
Elastic behavior. The elastic behavior (contact and no slip) is defined by the relation:
KN(p) 0 0
el dF = E · du ; E = 0 KT
0
0 0 KT (6.7)The normal displacement-normal force relationship, valid for loading and unloading conditions, can be
either
• Constant contact stiffness (penalty stiffness, hard contact). One simple rule of thumb in choosing
this stiffness is to prescribe contact penalty stiffness K = 1000EA/h, where E is a stiffness modulus
of one of the materials adjacent to contact zone (hence there is a possibility that this stiffness
might be coming from soil (relatively low stiffness) or from concrete (relatively large stiffness), A is
a tributary area for that contact element, and h is a thickness of a contact zone (usually a small
number, on the order of few centimeters). It is important to note that above recommended
penalty stiffness can vary orders of magnitude, and that numerical experiments need to be
performed in order to test contact element performance with chosen stiffness, or
• Nonlinear function (soft contact). Functional relationship that works well for concrete – soil contact
is
,if ux < 0(6.8)
,if ux < 0
where kn is a constant and ux is a relative displacement of two contact nodes. Force–displacement
equation given in equation 6.8 is a parabola that has a non-zero tangent at ux = 0. The value of
stiffness 0.0001kn is chosen as stiffness at 1/10 of millimeter (0.0001m) of penetration.
The tangential stiffness KT is assumed to be constant. Alternative contact stiffness functions were
proposed by Gens et al. (1988, 1990) for a more stiff contact between two rock (or concrete) surfaces.
26
Plastic model The plastic model is defined in terms of yield surface and plastic potential surface, as shown
in Figure 6.2. Yield surface is a frictional cone (with friction coefficient µ = tanφ), with no cohesion.
Plastic potential, that defines plastic flow directions, preserves volume, that is, there is no dilation of
compression due to plastic slip.
Figure 6.2: Yield surface fs, plastic potential Gs and incremental plastic displacement δup.
Geometry description
Figure 6.3 shows geometry of the two node contact element and its main deformations modes. It is
Definition of local axes
Nodes in contact Movement in normal direction
Movement in tangent direction
Figure 6.3: Description of contact geometry and displacement responses
important to note the importance of properly numbering element nodes, consistently with the definition
of normal x1. Node I is the first node, node J is the second node and normal goes from node I toward
node J. If reversed, elements behaves like a hook.
6.4.5 Structures with a base isolation system
Base isolation system are used to change dynamic characteristics of seismic motions that excite structure
and also to dissipate seismic energy before it excites structure. Therefor there are two main types of
27
J
I
J
I
J
I
contact plane
lz
ly
lxJ
I
z
y
x
systemcoordinateGlobal
devices:
• Base Isolators (Kelly, 1991a,b; Toopchi-Nezhad et al., 2008; Huang et al., 2010; Vassiliou et al.,
2013) are usually made of low damping (energy dissipation) elastomers and are primarily meant to
change (reduce) frequencies of motions that are transferred to the structural system. They are not
designed nor modeled as energy dissipators.
NOTE for Alain: what I meant is that base isolators will modify frequency of motions that to into the structure, I changed above item to reflect that.However if it is still not clear, I can add more explanation...
• Base Dissipators Kelly and Hodder (1982); Fadi and Constantinou (2010); Kumar et al. (2014) are
developed to dissipate seismic energy before it excites the structure. There two main types of such
dissipators:
– Elastomers made of high dissipation rubber, and
– Frictional pendulum dissipators
Both isolators and dissipators are usually developed to work in two horizontal dimensions, while
motions in vertical direction are not isolated or dissipated. This can create potential problems, and need
to be carefully modeled.
Modeling of base isolation and dissipation system is done using two node finite elements of relatively
short length.
Base Isolation Systems are modeled using linear or nonlinear elastic elements. Stiffness is provided from
either tests on a full sized base isolators, or from material characterization of rubber (and steel plates if
used in a sandwich isolator construction). Depending on rubber used, a number of models can be used to
develop stiffness of the device (Ogden, 1984; Simo and Miehe, 1992; Simo and Pister, 1984).
Particularly important is to properly account for vertical stiffness as vertical motions can be amplified
depending on characteristics of seismic motions, structure and stiffness of the isolators Hijikata et al.
(2012); Araki et al. (2009). It is also important to note that assumption of small deformation is used in
most cases. In other words, stability of the isolator, for example overturning or rolling is not modeled. It
is assumed that elastic stiffness will not suddenly change if isolator becomes unstable (rolls or overturns).
28
Base Dissipator Systems are modeled using inelastic (nonlinear) two node elements. There are three
basic types of dissipator models used:
• High damping rubber dissipators
• Rubber dissipators with lead core
• Frictional pendulum (double or triple) dissipators
Each one is calibrated using tests done on a full dissipator. It is important to be able to take into
account influence of (changing) ambient temperature and increase in temperatures due to energy
dissipation (friction) on resulting behavior. Ambient temperature can have significant variation,
depending on geographic location of installed devices and such variation will affects base dissipator
system response. In addition, energy dissipation results in heating of devices, and resulting increase in
temperature will influences base dissipators response as well.
6.4.6 Foundation models
Foundation modeling can be done using variable level of sophistication. Earlier models assumed rigid
foundation slabs. This was dictated by the use of modeling methods that rely on analytic solution, which
in turn have to rely on simplifying assumptions in order to be solved. For example, soil and rock beneath
and adjacent to foundations was usually assumed to be an elastic half space.
Foundation response plays an important role in overall soil-structure interaction (SSI) response.
Major energy dissipation happens in soil and contact zone beneath the foundation. Buoyant forces
(pressures) act on foundation if water table is above bottom of the lowest foundation level.
Shallow and Embedded Slab Foundations Foundation slabs and walls are flexible. Their thickness can
range from 3 − 5 meters, but they extend for up to 100 meters. Containment and shield buildings are
rigidly connected to foundation slabs, and will stiffen it up. In addition, auxiliary buildings, will also stiffen
up foundation slabs. However, even with all these stiffening effects, slabs and walls should be modeled
using flexible models.
Flexible modeling of foundation slabs is best done using either shell elements (plate bending and in
plane wall) or solids.
29
For shell element models, it is important to bridge over half slab or wall thickness to the adjacent soil.
This is important as shell elements are geometrically representing plane in the middle of a solid (slab or
wall) with a finite thickness, so connection over half thickness of the slab or wall is needed. It is best if
shell elements with drilling degrees of freedom are used (Ibrahimbegovi´c et al., 1990; Militello and
Felippa, 1991; Alvin et al., 1992; Felippa and Militello, 1992; Felippa and Alexander, 1992), as they
properly take into account all degrees of freedom (three translations, two bending rotations and a drilling
rotation).
For solid element models, it is important to use proper number of solids so that they represent
properly bending stiffness. For example, a single layer of regular 8 node bricks will over-predict bending
stiffness over 2 times (200%). Hence at least 4 layers of 8 node bricks are needed for proper bending
stiffness. If 27 node brick elements are used, a single layer is predicting bending stiffness within 4% of
analytic solution.
Piles and Shaft Foundations For nuclear facilities built on problematic soils, piles and shafts are usually
used for foundation system. Piles can carry loads at the bottom end, and in addition to that, can also
carry loads by skin friction. Shafts usually carry majority of load at the bottom end.
Piles (including pile groups) and shafts have been modeled using three main approaches:
• Analytic approach (Sanchez-Salinero and Roesset, 1982; Sanchez-Salinero et al., 1983; Myionakis
and Gazetas, 1999; Law and Lam, 2001; Sastry and Meyerhof, 1999; Abedzadeh and Pak, 2004; Mei
Hsiung et al., 2006; Sun, 1994) where a main assumptions is that of a linear elastic behavior of a
pile and the soil represented by a half space, while contact zone is fully connected, and no slip or
gap is allowed. More recently there are some analytic solution where mild nonlinear assumptions
are introduced (Mei Hsiung, 2003), however models are still far from realistic behavior. These
approaches are very valuable for small vibrations of piles, pile groups and shafts as well as for all
modes of deformation where elasto-plasticity will be very mild if it exist at all.
• P-Y and T-Z approach where experimentally measured response of piles in lateral direction (P-Y)
and vertical direction (T-Z) is used to construct nonlinear springs that are then used to replace soil
(Stevens and Audibert, 1979; Brown et al., 1988; Bransby, 1996, 1999; Tower Wang and Reese,
1998; Reese et al., 2000; Georgiadis, 1983). This approach is very popular with practicing
engineers. However this approach does make some simplifying assumptions that make its use
30
questionable for use with cases where calibrations of P-Y and T-Z curves do not exist. For example,
for layered soils, of for piles in pile groups, this approach can produce problematic results.
Moreover, in dynamic applications, dynamics of soils surrounding piles and piles groups is poorly
approximated using springs.
• Nonlinear finite element models in 3D have been developed recently for treatment of piles, pile
groups and shafts, in both dry and liquefiable soils (Brown and Shie, 1990, 1991; Yang and Jeremi´c,
2003; Yang and Jeremi´c, 2005a,b; McGann et al., 2011; S.S.Rajashree and T.G.Sitharam, 2001;
Wakai et al., 1999). In these models, elastic-plastic behavior of soil is taken into account, as well as
inelastic contact zone within pile-soil interface. Layered soils are easily modeled, while proper
modeling of contact (see section 6.4.4) resolves both horizontal and vertical shear (slip) behavior.
Moreover, with proper modeling, effects of piles in liquefiable soils can be evaluated as well
(Cheng and Jeremi´c, 2009).
Deeply Embedded Foundations In case of Small Modular Reactors, foundations are deeply embedded,
and the foundation walls, in addition to the base slab, contribute significantly more to supporting
structure for static and dynamic loads. Main issues are related to proper modeling of contact (see more
in sections 6.4.4 and 6.4.7), as well inelastic behavior of soil adjacent to the slab and walls. Of particular
importance for deeply embedded foundations is proper modeling of buoyant stresses (forces) as it is
likely that ground water table will be above base slab.
Foundation Flexibility and Base Isolator/Dissipator Systems. There are special cases of foundations where
base isolators and dissipators are used. In this case there are two layers of foundations slabs, one at the
bottom, in contact with soil and one above isolators/dissipators, beneath the actual structure. Those two
base slabs are connected with dissipators/isolators. It is important to properly (accurately) model
stiffness of both slabs as their relative stiffness will control how effective will isolators and dissipators be
during earthquakes.
6.4.7 Small Modular Reactors (SMRs)
Small Modular Reactors (SMR) are becoming popular due to a number of reasons. Earthquake Soil
Structure Interaction of deeply embedded SMRs requires special considerations. For modeling of SMR, it
31
is important to note extensive contact zone of deeply embedded SMR walls and base slab with
surrounding soil. This brings forward a number of modeling and simulation issues for SMRs. listed below,
In addition, noted are suggested modeling approaches for each listed issue.
• Seismic Motions: Seismic motions will be quite variable along the depth and in horizontal direction.
This variability of motions is a results of mechanics of inclined seismic wave propagation, inherent
variability (incoherence) and the interaction of body waves (SH, SV and P) with the surface, where
surface waves are developed. Surface waves do extend somewhat into depth (about two wave
lengths at most (Aki and Richards, 2002)). This will result in different seismic motion wave lengths
(frequencies, depending on soil/rock stiffness), propagating in a different way at the surface and at
depth of SMR. As a results, an SMR will experience very different motions at the surface, at the
base and in between.
Due to a number of complex issues related to seismic motions variability, as noted above, it is
recommended that a full wave fields be developed and applied to SSI models of SMR.
– In the case of 1D wave propagation modeling, vertically propagating shear waves are to be
developed (deconvolution and/or convolution) and applied to SSI models.
– For 3D wave fields, there are two main options:
∗ use of incoherence functions to develop 3D seismic wave fields. This option has a
limitation as incoherent functions in the vertical direction are not well developed.
∗ develop a full 3D seismic wave field from a wave propagation modeling using for example
SW4. This option requires knowledge of local geology and may require modeling on a
regional scale, encompassing causative faults, while another option is to perform stress
testing using a series of sources/faults (Abell et al., 2015).
• Nonlinear/Inelastic Contact: Large contact zone of SMR concrete walls and foundation slab, with
surrounding soil, with its nonlinear/inelastic behavior will have significant effect on dynamic
response of a deeply embedded SMR.
Use of appropriate contact models, that can model frictional contact as well as possible gap
opening and closing (most likely in the near surface region) is recommended. In the case of
presence of water table above SMR foundation base, effective stresses approach needs to be used,
32
as well as modeling of (possibly dynamically changing) buoyant forces, as described in section 6.4.8
and also below.
• Buoyant Forces: With deep embedment, and (a possible) presence of underground water (water
table that is within depth of embedment), water pressure on walls of SMR will create buoyant
forces. During earthquake shaking, those forces will change dynamically, with possibility of cyclic
mobility and liquefaction, even for dense soil, due to water pumping during shaking (Allmond and
Kutter, 2014).
Modeling of buoyant forces can be done using two approaches, namely static and dynamic
buoyant force modeling, as described in section 6.4.8.
• Nonlinear/Inelastic Soil Behavior: With deep embedment, dynamic behavior an SMR is significantly
influenced by the nonlinear/inelastic behavior of soil adjacent to adjacent SMR walls and
foundation slab.
Use of appropriate inelastic (elastic-plastic) 3D soil models is recommended. Of particular
importance is proper modeling of soil behavior in 3D as well as proper modeling of volume change
due to shearing (dilatancy). One dimensional equivalent elastic models, used for 1D wave
propagation are not recommended for use, as they do not model properly 3D effects and lack
modeling of volume change.
• Uncertainty in Motions and Material: Due to large contact area and significant embedment,
significant uncertainty and variability (incoherence) in seismic motions will be present. Moreover,
uncertainties in properties of soil material surrounding SMR will add to uncertainty of the
response.
Uncertainty in seismic motions and material behavior can be modeled using two approaches, as
described in section 6.5. One approach is to rely on varying input motions and material parameters using
Monte Carlo approach, and its variants. This approach is very computationally demanding and not too
accurate. Second approach is to use analytic stochastic solutions for components or the full problem. For
example, stochastic finite element method, with extension to stochastic elasto-plasticity with random
loading. More details are given in section 6.5.
33
Figure 6.4 illustrates modeling issues on a simple, generic SMR finite element model (vertical cut
through middle of a full model is shown).
Figure 6.4: Four main issues for realistic modeling of Earthquake Soil Structure Interaction of SMRs:
variable weave field at depth and surface, inelastic behavior of contact and adjacent soil, dynamic
buoyant forces, and uncertain seismic motions and material.
It is important to develop models with enough fidelity to address above issues. It is possible that
some of the issues noted above will not be as important to influence results in any significant way,
however the only way to determine importance (influence) of above phenomena on seismic response of
an SMR is through modeling.
6.4.8 Buoyancy Modeling
For NPP structures for which lowest foundation level is below the water table, there exist a buoyant
pressure/forces on foundation base and walls. For static loads, buoyant force B can be calculated using
Archimedes principle: ”Any object immersed in water is buoyed up by a force equal to the weight of
water displaced by the object”, B = ρwgV where ρw = 999.972 kg/m3 (for salty sea/ocean water values of
density are higher ρw = 1020.0 − −1029.0 kg/m3) is the mass density of water (at temperature of +4oC
with small changes of less than 1% up to +40oC), g = 9.81 m/s2 is the gravitational acceleration, and V is
the volume of displaced fluid (volume of foundation under water table). Buoyant force can be applied as
a single force or a small number of resultant forces directed upward around the stiff center of
foundation. This is strictly applicable if foundation is rigid, but it can probably work in most cases for
static loads.
During dynamic loading, buoyant force (buoyant pressures) can dynamically change, as a results of a
dynamic change of pore fluid pressures in soil adjacent to the foundation concrete. This is particularly
34
true for soils that are dense, where shearing will lead increase of inter-granular void space (dilatancy),
and reduction in buoyant pressures or for soils that are loose, where shearing will lead to reduction of
inter-granular void space (compression), and increase in buoyant pressures.
For strong shaking, it also expected that gaps will open between soil and foundation walls and even
foundation slab. This will lead to pore water being sucked into the opening gap and pumped back into
soil when gap closes. This ”pumping” of water will lead to large, dynamic changes of buoyant pressures.
Different dynamic scenarios, described above, create conditions for dynamic, nonlinear changes in
buoyant force.
Dynamic Buoyant Stress/Force Modeling. Fully coupled finite elements (u-p or u-p-U or u-U, as described
in section 6.4.11) are used for modeling saturated soil adjacent to foundation walls and base. Modeling
of contact between soil and the foundation concrete needs to take into account effects of pore fluid
pressure – buoyant stress within the contact zone, on order to properly model normal stress for frictional
contact. This modeling can be done using
• coupled contact elements, that explicitly model water displacements and pressures and allows for
explicit gap opening, filling of gap with water, slipping (frictional) when the gap is closed, and
pumping of water as gap opens and closes. This contact element takes into account the pore water
pressure information from saturated soil finite elements, as well as the information about the
displacement (movement) of pore water within a gap. It is based on a dry version of the contact
element and incorporates effective contact (normal) stress, based in the effective stress principle
(see equation 6.4). Potential for modeling of pumping of water during gap opening and closing is
important as it might influence dynamic response. Noted pumping of water, can also lead to
liquefying of even dense soil, in the zone where gap opening happens (Allmond and Kutter, 2014).
• coupled finite elements in contact with impermeable concrete finite elements (solids or
shells/plates). In this case, impermeable concrete finite elements create a natural barrier for water
flow, thus allowing generation of excess pore fluid pressure in the contact zone during dynamic
shaking. This approach precludes formation of gaps and pumping of water.
35
6.4.9 Domain Boundaries
One of the biggest problems in dynamic ESSI in infinite media is related to the modeling of domain
boundaries. Because of limited computational resources the computational domain needs to be kept
small enough so that it can be analyzed in a reasonable amount of time. By limiting the domain however
an artificial boundary is introduced. As an accurate representation of the soil-structure system this
boundary has to absorb all outgoing waves and reflect no waves back into the computational domain.
The most commonly used types of domain boundaries are presented in the following:
• Fixed or free
By fixing all degrees of freedom on the domain boundaries any radiation of energy away from the
structure is made impossible. Waves are fully reflected and resonance frequencies can appear that
don’t exist in reality. The same happens if the degrees of freedom on a boundary are left ’free’, as
at the surface of the soil.
A combination of free and fully fixed boundaries should be chosen only if the entire model is
largeenough and if material damping of the soil prevents reflected waves to propagate back to the
structure.
For cases where compressional and/or shear waves travel very fast, in this case boundaries have to
be placed very far, thus significantly increasing the size of models.
• Absorbing Lysmer Boundaries
A way to eliminate waves propagating outward from the structure is to use Lysmer boundaries.
Thismethod is relatively easy to implement in a finite element code as it consists of simply
connecting dash pots to all degrees of freedom of the boundary nodes and fixing them on the
other end (Figure
6.5).
Lysmer boundaries are derived for an elastic wave propagation problem in a one-dimensional
semiinfinite bar. It can be shown that in this case a dash pot specified appropriately has the same
dynamic properties as the bar extending to infinity (Wolf, 1988). The damping coefficient C of the
dash pot equals
C = A cρ (6.9)
36
where A is the section of the bar, ρ is the mass density and c the wave velocity that has to be
selected according to the type of wave that has to be absorbed (shear wave velocity cs or
Figure 6.5: Absorbing boundary consisting of dash pots connected to each degree of freedom of a
boundary node
compressional wave velocity cp).
In a 3d or 2d model the angle of incidence of a wave reaching a boundary can vary from almost 0
up to nearly 180. The Lysmer boundary is able to absorb completely only those under an angle of
incidence of 90. Even with this type of absorbing boundary a large number of reflected waves are
still present in the domain. By increasing the size of the computational domain the angles of
incidence on the boundary can be brought closer to 90 and the amount of energy reflected can be
reduced.
• Infinite elements
• More sophisticated boundaries modeling wave propagation toward infinity (boundary elements)
For a spherical cavity involving only waves propagating in radial direction a closed form solution for
radiation toward infinity, analogous to the Lysmer boundary for wave propagation in a prismatic
rod, exists (Sections 3.1.2 and 3.1.3 in Wolf (1988)). Since this solution, in contrast to the Lysmer
boundary, includes radiation damping it can be thought of as an efficient way of eliminating
reflections on a semi-spherical boundary surrounding the computational domain.
37
More generality in terms of absorption properties and geometry of the boundary are provided by
the various boundary element methods (BEM) available in the literature.
6.4.10 Seismic Load Input
A number of methods is used to input seismic motions into finite element model. Most of them are
based on simple intuitive approaches, and as such are not based on rational mechanics. Most of those
currently still widely used methods cannot properly model all three components of body waves as well as
always present surface waves. There exist a method that is based on rational mechanics and can model
both body and surface seismic waves input into finite element models with high accuracy. That method is
called the Domain Reduction Method (DRM) and was developed fairly recently Bielak et al. (2003);
Yoshimura et al. (2003)). It is a modular, two-step dynamic procedure aimed at reducing the large
computational domain to a more manageable size. The method was developed with earthquake ground
motions in mind, with the main idea to replace the force couples at the fault with their counterpart
acting on a continuous surface surrounding local feature of interest. The local feature can be any geologic
or man made object that constitutes a difference from the simplified large domain for which
displacements and accelerations are easier to obtain. The DRM is applicable to a much wider range of
problems. It is essentially a variant of global–local set of methods and as formulated can be used for any
problems where the local feature can be bounded by a continuous surface (that can be closed or not).
The local feature in general can represent a soil–foundation–structure system (bridge, building, dam,
tunnel...), or it can be a crack in large domain, or some other type of inhomogeneity that is fairly small
compared to the size of domain where it is found.
The Domain Reduction Method
A large physical domain is to be analyzed for dynamic behavior. The source of disturbance is a known
time history of a force field Pe(t). That source of loading is far away from a local feature which is
dynamically excited by Pe(t) (see Figure 6.6).
The system can be quite large, for example earthquake hypocenter can be many kilometers away
from the local feature of interest. Similarly, the small local feature in a machine part can be many
centimeters away from the source of dynamic loading which influences this local feature. In this sense
the term large domain is relative to the size of the local feature and the distance to the dynamic forcing
source.
38
It would be beneficial not to analyze the complete system, as we are only interested in the behavior
of the local feature and its immediate surrounding, and can almost neglect the domain outside of some
relatively close boundaries. In order to do this, we need to somehow transfer the loading from the source
Figure 6.6: Large physical domain with the source of load Pe(t) and the local feature (in this case a
soil-structure system.
to the immediate vicinity of the local feature. For example we can try to reduce the size of the domain to
a much smaller model bounded by surface Γ as shown in Figure 6.6. In doing so we must ensure that the
dynamic forces Pe(t) are appropriately propagated to the much smaller model boundaries Γ.
It can be shown (Bielak et al., 2003) that the consistent dynamic replacement for the dynamic source
forces Pe is a so called effective force, Peff:
eff
Pi 0
Peff = Pbeff = − MbeΩ+u¨e0 − KbeΩ+u0e (6.10)
Peeff MebΩ+u¨0b + KebΩ+u0b
where MbeΩ+ and Meb
Ω+ are off-diagonal components of a mass matrix, connecting boundary (b) and
external (e) nodes, KbeΩ+ and Keb
Ω+ are off-diagonal component of a stiffness matrix, connecting boundary
(b) and external (e) nodes, u¨0e and u¨0
b are free field accelerations of external (e) and boundary (b) nodes,
39
Seismic source
Ω
Ω+
Local feature
Large scale domain
Γ
eu
buiu
)t(eP
respectively and, and u0b are free field displacements of external (e) nodes, and boundary (b) nodes,
respectively and. The effective force Peff consistently replace forces from the seismic source with a set of
forces in a single layer of finite elements surrounding the SSI model. The DRM is quite powerful and has a
number of features that makes an excellent choice for SSI modeling:
• Single Layer of Elements used for Peff. Effective nodal forces Peff involve only the submatrices Mbe,
Kbe, Meb, Keb. These matrices vanish everywhere except the single layer of finite elements in domain
Ω+ adjacent to Γ. The significance of this is that the only wave-field (displacements and
accelerations) needed to determine effective forces Peff is that obtained from the simplified
(auxiliary) problem at the nodes that lie on and between boundaries Γ and Γe
• Only residual waves outgoing. Solution to the DRM problem produces accurate seismic
displacements inside and on the DRM boundary. On the other hand, the solution for the domain
outside the DRM layer represents only the residual displacement field. This residual displacement
field is measured relative to the reference free field displacements. Residual wave field has low
energy when compared to the full seismic wave field, as it is a results of oscillations of the
structure only. It is thus fairly easy to be damped out. This means that DRM can very accurately
model radiation damping.
This is significant for two more reasons:
– Large models can be reduced in size to encompass just a few layers of elements outside DRM
boundary,
– Residual unknown field can be monitored and analyzed for information about the dynamic
characteristics of the soil structure system
• Inside of DRM boundary can be nonlinear/inelastic. This is a very important conclusion, based on a
fact that only change of variables was employed in DRM development, and solution does not rely
on superposition.
• All types of realistic seismic waves are modeled. Since the effective forcing Peff consistently replaces
the effects of the seismic source, all appropriate (real) seismic waves are properly (analytically )
modeled, including body (SV, SH, P) and surface (Rayleigh, Love, etc...) waves.
40
A Note on Free Field Input Motions for DRM. Seismic motions (free field) that are used for input into a
DRM model need to be consistent. In other words, a free field seismic wave that is used needs to fully
satisfy equations of motion. For example, if free field motions are developed using a tool (SHAKE, or EDT
or SW4, or fk, &c.) using time step ∆t = 0.01s and then you decide that you want to run your analysis
with a time step of ∆t = 0.001s, simple interpolation (10 additional steps for each of the original steps)
might create problems. Simple linear interpolation actually might (will) not satisfy wave propagation
equations and if used will introduce additional, high frequency motions into the model. It is a very good
idea to generate free field motions with the same time step as it will be used in ESSI
simulation.
Similar problem might occur if spacial interpolation is done, that is if location of free field model
nodes is not very close to the actual DRM nodes used in ESSI model. Spatial interpolation problems are
actually a bit less acute, however one still has to pay attention and test the ESSI model for free conditions
and only then add the structure(s) on top.
6.4.11 Liquefaction and Cyclic Mobility Modeling
Introduction
Saturated soils should be analyzed using information about pore fluid pressure. Modeling of saturated
soils, where pore fluid is able to move during loading (see note on effective and total stress analysis
conditions in section 6.4.2) is best done using an effective stress approach.
Effective stress analysis, where stress in soil skeleton and pore fluid pressures are treated separately
is appropriate for both drained and undrained (low permeability) cases. This approach is necessary if
permeability of soil is such that one can expect movement of pore fluid and pore fluid pressures during
dynamic event duration. For example, saturated sandy soils, will feature a fully coupled behavior of pore
fluid and soil skeleton. It is expected that pore fluid and pore fluid pressure will move and, through the
effective stress principle, such pore fluid pressure change will affect (significantly) response of soil
skeleton. There are three main approaches to modeling fully coupled pore fluid – solid skeleton systems
(Zienkiewicz and Shiomi, 1984):
u − p − U, where the main unknowns are displacements of porous solid skeleton (ui), pore fluid
pressures (p), and displacements of pore fluid (Ui),
41
u−U where the main unknowns are displacements of porous solid skeleton (ui), and displacements
of pore fluid (Ui),
u−p where the main unknowns are displacements of porous solid skeleton (ui), pore fluid pressures
(p).
Zienkiewicz and Shiomi (1984) notes pros and cons of each approach, and concludes that the u−p−U
is the most flexible and accurate for dynamic events where there is a significant rate of change in pore
fluid pressures and pore fluid and solid skeleton displacements, as is a case for soil structure interaction
problems. In the case of u − U formulation, stumbling block is the high bulk stiffness of the fluid, which
can create numerical problems. Those numerical problems are elegantly resolved in the u−p−U
formulation through the inclusion of pore fluid pressure into the unknown field (although pore fluid
pressures and pore fluid displacements are dependent variables). The u − p formulation is the simplest
one, but has issues in treating problems with high rate of change of pore fluid pressure in space and
time.
Liquefaction Modeling Details and Discussion
Modeling of liquefaction and its effects requires availability of computer programs that model behaviour
of fully saturated, fully coupled pore fluid – porous solid materials (soils). Chapter 8 list some of the
available programs. Modeling and simulation of liquefaction requires significant test data for soil. Both
laboratory and in situ test data is required. Recent publications (Peng et al., 2004; Elgamal et al.,
2002, 2009; Arduino and Macari, 1996, 2001; Jeremi´c et al., 2008; Shahir et al., 2012; Taiebat et al.,
2010b,a; Cheng and Jeremi´c, 2009; Taiebat et al., 2010a) describe various possibilities in modeling of
cyclic mobility and liquefaction effects. It is important to properly verify and validate modeling tools for
coupled (liquefaction) modeling, as multi–physics modeling of these problems can be difficult and results
interpretation requires full confidence in simulation programs (Tasiopoulou et al., 2015a,b).
6.4.12 Structure-Soil-Structure Interaction
(in phase, out phase, distance between structures relative to the surface wave length, etc.)
Soil-Structure-Soil-Structure Interaction (SSSSI) sometimes needs to be taken into account, as it might
change levels of seismic excitation for adjacent NPPs. There are a number of approaches to model SSSSI.
42
• Direct Models. The simplest and most accurate approach is to develop a direct model all (two or
more) structures on subsurface soil and rock, develop input seismic motions and analyze results.
While this approach is the most involved, it is also the most accurate, as it allows for proper
modeling of all the structure, foundation and soil/rock geometries and material without making
any unnecessary simplifying assumptions.
The main issue to be addressed with this approach is development of seismic motions to be used
for input. Possible approach to developing seismic motions is to use incoherent motions with
appropriate separation distance. Alternatively, regional seismic wave modeling can be used to
develop realistic seismic motions and use those as input through, for example the Domain
Reduction Method (see section 6.4.10).
• Symmetry and Anti-Symmetry Models. These models are sometimes used in order to reduce
complexity and sophistication of the direct model (see recent paper by Roy et al. (2013) for
example). However, there are a number of concerns regarding simplifying assumptions that need
to be made in order for these models to work. These models have to make an assumption of a
vertically propagating shear waves and as such do not take into account input surface waves
(Rayleigh, Love, etc). Surface waves will additionally excite NPP for rocking and twisting motions,
which will then be transferred to adjacent NPP by means of additional, induced surface waves. If
only vertically propagating waves are used for input (as is the case for symmetry and antisymmetry
models) energy of input surface waves is neglected. It is noted that depending on the surface wave
length and the distance between adjacent structures, a simple analysis can be performed to
determine if particular surface waves, emitted/radiated from one structure toward the other one
(and in the opposite direction) can influence adjacent structures. It is noted that the wave length
can be determined using a classical equation λ = v/f where λ is the length of the (surface) wave, v
is the wave speed1 and f is the wave frequency of interest. Table6.1 below gives Rayleigh wave
lengths for four different wave frequencies (1,5,10,20 Hz and for three different Rayleigh (very
close to shear) wave velocities (300,1000,2500 m/s):
Table 6.1: Rayleigh wave length as a function of wave speed [m/s] and wave frequency [Hz].
1 For Rayleigh surface waves, a wave speed is just slightly below the shear wave speed (within 10%, depending on elastic
properties of material), so a shear wave speed can be used for making Rayleigh wave length estimates.
43
1.0Hz 5.0Hz 10.0Hz 20Hz
300m/s 300m 60m 30m 15m
1000m/s 1000m 200m 100m 50m
2500m/s 2500m 500m 250m 125mIt is apparent that for given separation between NPP buildings, different surface wave
(frequencies) will be differently transmitted with different effects. For example, for an NPP building
that has a basic linear dimension (length along the main rocking direction) of 100m, the low
frequencies surface wave (1Hz) in soft soil (vs ≈ 300m/s) will be able to encompass a complete
building within a single wave length, while for the same soil stiffness, the high frequency (20Hz)
will produce waves that are too short to efficiently propagate through such NPP structure. On the
other hand, for higher rock stiffness (vs ≈ 2500m/s), waves with frequencies up to approximately
5Hz, can easily affect a building with 100m dimension.
Further comments on are in order for making symmetric and antisymmetric assumptions (that is
modeling a single building with one boundary having symmetric or antisymmetric boundary
condition so as to represent a duplicate model, on the other side of such boundary):
– Symmetry: motions of two NPPs are out phase and this is only achievable, if the wave length
of surface wave created by one NPP (radiating toward the other NPP) is so large that half
wave length encompasses both NPPs. This type of motions (symmetry) is illustrated in figure
6.4.12 below
Figure 6.7: Symmetric mode of deformation for two NPPs near each other.
– Antisymmetry: motions of two NPPs are in phase. This is achievable if distance between two
NPPs is perfectly matching wave lengths of the radiated wave from one NPP toward the other
one, and if the dimension of NPPs is not affecting radiated waves. This type of SSSSI is
illustrated in figure 6.4.12 below
44
Figure 6.8: Anti-symmetric mode of deformation for two NPPs near each other.
Both symmetry and antisymmetry assumptions place very special requirements on wave lengths
that are transmitted/radiated and as such do not model general waves (various frequencies) that
can be affecting adjacent NPPs.
6.4.13 Simplified models
Simplified Models
Simplified models are used for fast prototyping and for parametric studies, as they have relatively low
computational demand. It is very important to note that a significant expertise is required from analyst in
order to develop appropriate modeling simplifications that retain mechanical behavior of interests, while
simplifying out model components that are not important (for particular analysis).
Simplified, Discrete Soil and Structural Models
Using discrete models can provide useful results for some aspects of mechanical behavior while requiring
relatively small computational effort. Discrete, simplified models can be used for analyzing many aspects
of SSI behavior of NPPs, provided that simplifications are made in a consistent way and that thus
developed models can be verified for required modeling purposes.
P-Y and T-Z Springs. Simplified models using P-Y and T-Z springs (Bransby, 1999; Allen, 1985; Georgiadis,
1983; Stevens and Audibert, 1979; Bransby, 1996) have been used to model response of piles, pile groups
and other types of foundations in elastic-plastic soil. The ideas is based on Winkler foundation springs
that are now defined as nonlinear springs. Radial (transversal) behavior is modeled using P-Y springs,
while axial (longitudinal) behavior is modeled using T-Z spings. It is important to note that calibration of
P-Y and T-Z springs is done in full scale tests, for a given pile or foundation stiffness and for a given soil
type. In that sense, P-Y and T-Z springs can be understood as post-processing (recording) of the actual
45
response, that is then transferred to nonlinear springs behavior, through a combination of nonlinear
springs, dashpots, sliders and other components that are combined in order to mimic recorded P-Y and
T-Z behavior.
While P-Y and T-Z springs have been successfully used for modeling monotonic loading of single piles
in uniform soils (sand and clay), their use for loading in different directions, in layered soils and for
dynamics applications cannot be fully verified. Particularly problematic is P-Y behavior in layered soils and
for large pile groups Yang and Jeremi´c (2002, 2003); Yang and Jeremi´c (2005a)
Simplified, Continuum Soil Models
While modeling of SSI is best done using continuum models, there are number of simplifications that can
be done as well.
Linear Elastic. One of the most commonly made simplification is to use linear elastic models for modeling
of soil. While this might be appropriate for small seismic excitations, it is likely that models with more
significant seismic load levels, where plastification will have larger effects, cannot be validated and that
these models will miss significant aspects of behavior that are important for understanding
response.l
Stiffness Reduction (G/Gmax) and Damping Curve Models. Plastification (not significant) can be taken into
account using modulus reduction and damping curves. However, as noted in section 4.5, such results
have to be carefully used as this material modeling simplification introduces a number of artifacts. For
example amplification factors for 1D models using this approximations are known to be biased (Rathje
and Kottke, 2008).
6.4.14 General guidance on soil structure interaction modelling and analysis
Model Development Model development requires development of a hierarchical set of models with
gradual (!) in sophistication. As hierarchy of models are developed, each model set needs to be verified
and be capable to (properly, accurately) model phenomena of interest.
Model Verification Model verification is used to verify that mechanical features that are of interest are
indeed properly modeled. In other words, model verification is required to prove that results obtained
46
for a given (developed) model are accurately modeling features of interest. For example, if propagation
of higher frequency motions is required (analyzed), it is necessary to verify that developed (used) model
is capable of propagating waves of certain wave lengths and frequency. Model verification is different
than code and solution verification and validation, described in some details in Chapter 8 and also in
much more detail in a number of references (Roache, 1998; Oberkampf et al., 2002; Oberkampf, 2003;
Oden et al., 2005; Babuˇska and Oden, 2004; Oden et al., 2010a,b; Roy and Oberkampf, 2011). Code and
solution verification is necessary (required) to prove that code (program) solves correctly used models.
Validation provides evidence that the correct model is solved. Model verification has to be performed for
each developed model in order to gain confidence that modeling results are usable for design, licensing,
etc. Section 4.5 describes in some details procedures for model verification.
6.5 Probabilistic response analysis (Jeremic and Johnson)
6.5.1 Introduction
Uncertainty is present in all phases of modeling and simulation of an earthquake soil structure interaction
problem for nuclear facilities. There are two main sources of uncertainty:
• Modeling Uncertainty is introduced when simplifying modelling assumptions are made. In general,
only simplifying modelling assumptions that do not introduce significant uncertainties, and
inaccuracies in results should be used. However, that is not always the case. Significance of the
influence of modeling uncertainties on results can only be assessed if higher sophistication models
can be simulated and results compared with lower sophistication models (where modeling
uncertainties were introduced).
• Parametric uncertainty is introduced when there are uncertainties in
– Material modeling parameters, and –
Loads (earthquakes).
Uncertainty in material modelling parameters are a result of measuring errors and transformation
errors (Phoon and Kulhawy, 1999a,b; Fenton, 1999; Baecher and Christian, 2003), as well as spatial
variability that contributes to uncertainties as averaging of material volume has to be done in order to
determine material properties.
47
Parametric (material modeling) uncertainties can be significant. For example Figure 6.9 shows
experimental data for a relationship between the Standard Penetration Test (SPT) and elastic modulus
(Ohya et al., 1982). In addition, shown is a mean trend, as well as a histogram of a scatter with respect to
the mean. It is important to note that significant uncertainty will be introduced in results if a mean
relationship is used alone. For example, if one uses the above relationship, and has a soil with a blow-
count of 10, elastic stiffness will be (according to the mean equation/relationship) approximately
8,000 kPa, while test data shows that values can be as low as 1,000 kPa and as high as 33,000 kPa.
Hence, assuming deterministic material property (elastic stiffness in this case), analyst will not model
(probable) very soft or very stiff material response. In other words, parametric uncertainty has to be
taken into account in order to provide full information about (probabilities of) response.
There are a number of approaches to take probabilistic response into account.
5 10 15 20 25 30 35
SPT N Value Residual (w.r.t Mean) Young’s Modulus (kPa)
Figure 6.9: Left: Transformation relationship between Standard Penetration Test (SPT) N-value and
pressure-meter Young’s modulus, E. Right: Histogram of the residual with respect to the deterministic
transformation equation for Young’s modulus, along with fitted probability density function (PDF). From
Sett et al. (2011b).
6.5.2 Probabilistic Response Analysis
NOTE: This is where Jim’s section 6.5 is to be merged.
6.5.3 Monte Carlo
Monte Carlo approach is used to estimate probabilistic site response, when both input motions (rock
motions at the bottom) and the material properties are uncertain. For a simplified approach, using
48
Young’s Modulus, E (kPa)
equivalent linear (EqL) approach (strain compatible soil properties with (viscous) damping, a large
number of combinations (statistically significant) of equivalent linear (elastic) stiffness for each soil layer
are analyzed in a deterministic way. In addition, input loading can also be developed into large number
(statistically significant) rock motions. Large number of result surface motions, spectra, etc. can then be
used to develop statistics (mean, mode, variance, sensitivity, etc.) of site response. Methodology is fairly
simple as it utilizes already existing EqL site response modeling, repeated large number of times. There
lies a problem, actually, as for proper (stable) statistics, a very large number of simulations need to be
performed, which makes this method very computationally intensive.
While Monte Carlo method can sometimes be applied to a 1D EqL site response analysis, any use for
2D or 3D analysis (even linear elastic) creates an insurmountable number of (now more involved, not 1D
any more) of simulations that cannot be performed in reasonable time even on large national
supercomputers. Problems becomes even more overwhelming if instead of linear elastic (equivalent
linear) material models, elastic-plastic models are used, as they feature more independent (or somewhat
dependent) material parameters that need to be varied using Monte Carlo approach.
6.5.4 Random Vibration Theory
Random Vibration Theory (RVT) is used for evaluating probabilistic site response. Instead of performing a
(statistically significant) large number of deterministic simulations of site response (all still in 1D), as
described above, RVT approach can be used Rathje and Kottke (2008). RVT uses Fourier Amplitude
Spectrum (FAS) of rock motions to develop FAS of surface motions. Developed FAS of surface motions
can them be used to develop peak ground acceleration and spectral acceleration at the surface.
However, time histories cannot be developed, as phase angles are missing.
6.5.5 Stochastic Finite Element Method
Instead of using Monte Carlo repetitive computations (with high computational cost), uncertainties in
material parameters (left hand side (LHS) and the loads (right hand side, RHS), can be directly taken into
account using stochastic finite element method (SFEM). There are a number of different approaches to
SFEM computations. Perturbation approach was popular in early formulations of SFEM (Kleiber and Hien,
1992; Der Kiureghian and Ke, 1988; Mellah et al., 2000; Gutierrez and De Borst, 1999), while spectral
stochastic finite element method (SSFEM) proved to be more versatile (Ghanem and Spanos, 1991; Keese
49
and Matthies, 2002; Xiu and Karniadakis, 2003; Debusschere et al., 2003). Review of various issues with
different SFEM approaches were recently provided by Matthies et al. (1997); Stefanou (2009); Deb et al.
(2001); Babuska and Chatzipantelidis (2002); Ghanem (1999); Soize and Ghanem (2009). Most previous
approaches pertained to linear elastic uncertain material. Recently, (Sett et al., 2011a) developed
Stochastic Elastic Plastic Finite Element Method (SEPFEM), that can be used for modeling of seismic wave
propagation through inelastic (elastic-plastic) stochastic material (soil).
Both SFEM and the SEPFEM rely on discretization of finite element equations in both stochastic and
spatial dimensions. In particular, (a) Karhunen–Lo`eve (KL) expansion (Karhunen, 1947; Lo`eve, 1948;
Ghanem and Spanos, 1991) is used to discretize the input material properties random field into a number
of independent basic random variables, (b) Polynomial chaos (PC) expansion (Wiener, 1938; Ghanem and
Spanos, 1991) is used to discretize degrees of freedom into stochastic space(s), and (c) (classical) shape
functions (Zienkiewicz and Taylor, 2000; Bathe, 1996b; Ghanem and Spanos, 1991) are used to discretize
the spatial components of displacements into the (above) polynomial chaos expansion.
In addition to the above, SEPFEM relies on Probabilistic Elasto Plasticity Jeremi´c et al. (2007); Sett et al.
(2007); Jeremi´c and Sett (2009); Sett and Jeremi´c (2010); Sett et al. (2011b,a); Karapiperis et al. (2016)
to solve for probabilistic elastic-plastic problem and supply probability distributions of stress and stiffness
to finite element computations.
Both SFEM and SEPFEM provide very accurate results in terms of full probability density functions
(PDFs) of main unknowns (Degrees of Freedom, DoFs) and stress (forces). Of particular importance is the
very accurate calculation of full PDF, which supplies accurate tails of PDF, so that Cumulative Distribution
Functions (CDFs, or fragilities) can be accurately obtained. However, while SFEM and SEPFEM are (can be)
extremely powerful, and can provide very useful, full probabilistic results (generalized displacements,
stress/forces), it requires significant expertise form the analyst. In addition, significant site
characterization data is needed in order for uncertain (stochastic) characterization of material properties.
If such data is not available, one can of source resort to (non-site specific) data available in literature
(Baecher and Christian, 2003; Phoon and Kulhawy, 1999a,b). However, use of non-site specific data
significantly increases uncertainties (tails of material properties distributions become very ”thick”) as
data is now obtained from a number of different, non-local sites, and is averaged, a process which usually
increases variability.
50
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