AIN SHAMS UNIVERSITY

FACULTY OF ENGINEERING

Vol. 38, No. 3, September 30, 2003

SCIENTIFIC BULLETIN Received on : 8/4/2003 Accepted on : 15/9/2003 PP. : 75-94

PREDICTION OF GROUND SUBSIDENCE ASSOCIATED WITH CONSTRUCTION OF

GREATER CAIRO METRO – LINE 3

A.A. Ahmed1 H.E. Ali2 S.M. El-Sayed2 S.M. Nour El-Din3

ABSTRACT Substantial length of Greater Cairo Metro – Line 3 will be excavated through the Nile alluviums under a high groundwater head. The planned route passes in close proximity to many structurally sensitive buildings especially in the downtown area. Controlling ground subsidence associated with tunneling in these problematic conditions, is especially consequential when inquiring the success of the new line. This paper predicts the settlement trough associated with tunneling between Attaba Station and Bab El-Sharia Station. The analysis is based on two different analytical approaches. The first approach is a nonlinear three-dimensional finite element analysis based on the Gap Parameter method. The second approach is a novel method based on the Artificial Neural Networks (ANN) technology. The ANN was trained and verified using the observed settlements for many tunnels in Egypt and abroad. The results show that these approaches compatibly predict the settlement associated with tunneling although they are based on different modeling techniques. KEYWORDS: Soft Ground Tunneling, Hydroshield, Three-Dimensional Finite Elements Analysis, Gap Parameter, Artificial Neural Network (ANN), Genetic Algorithms (GAs), Monitoring Programs, Settlement Trough, Trough Width Parameter, Standard Penetration Test (SPT).

ملخصالترسیبات النهریة جزء كبیر من الخط الثالث لمترو أنفاق القاهرة الكبرى فى حفروف یتم س

بالقرب الخطالهذزمع المسار المیمرو. ضاغط كبیر من المیاه االرضیةتأثیر لوادى النیل تحت .المدینةوسط وخصوصا فى منطقة الحساسةمن العدید من المبانى ذات الطبیعة االنشائیة

هم یعتبرأالحرجة هذه الظروف تحت النفق التحكم فى الهبوط المصاحب لتنفیذن إف بالتالىوثالث بین محطتى الهبوط المصاحب لتنفیذ الخط الین یفى هذا البحث تم تع. هذا الخطأنشاء نواحى

على طریقة العناصرالمحددة یةهما مبنالعتبة وباب الشعریة باستخدام طریقتین مختلفتین اوالمبنیة على الخالیا العصبیة االصطناعیة فلحساب الهبوط الطریقة االخرى أماالفراغیةالالخطیة

عدید من االنفاق فى الهبوط المصاحب للقیاساتباستخدام والتحقق من نتائجها التى تم تدریبها ختالف طریقة التحلیل فى إالهبوط على الرغم من نتائجالبحث تقارب وقد بین . مصر والخارج

.الطریقتین 1 Professor of Geotechnical Engineering, Ain Shams University, Cairo Egypt 2 Assistant Professor of Geotechnical Engineering, Ain Shams University, Cairo Egypt 3 Graduate Student, Ain Shams University, Cairo Egypt

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1. INTRODUCTION As Cairo population has soared in the recent decades from 3.5 millions in 1960 to about 20 millions today, the need for a new mass transportation system was inevitable. Studies carried out between 1970 and 1974 suggested to construct the Greater Cairo Metro to be the spinal mass transportation system (Richards et al., 1997). Madkour et al. (1999) estimated that about 530 million passengers used the Greater Cairo Metro in the year 2000. Metro network, Fig (1), comprises a regional line and two urban lines. The first line of the Greater Cairo Metro or the regional line was completed in 1989 and was the first subway metro line in Africa and the Middle East. It is 42.5 km long from El-Marg at the North of Cairo to Helwan at the South with about 4.5 km underground part through downtown area using cut-and-cover tunnels (Madkour et al., 1999). Line 2 also lies along the city’s north/south corridor extending from Shubra El-Kheima to Giza suburban areas. The line was constructed and put into operation in several phases. Phase 1A, from Shubra El-Kheima to its intersection with Line 1 at Mubarak Station. Phase 1B starts from Mubarak Station and terminates at Sadat Station which forms another interchange with the older first line. Line 2 included the first-ever tunnel crossing under the Nile River (Ahmed, 2001). Two identical Herrenknecht Bentonite Slurry TBMs (Hydroshields) of 9.45m diameter were selected to drive the tunnel in the second line. The details of the employed TBM are shown in Fig (2). Line 3 is situated in the urban densely-populated areas connecting Imbaba to the downtown of Cairo at Attaba and Heliopolis district. The first phase of this line is expected to be from Attaba Station to Abbasia Station with a 4.292 km double deck circular bored tunnel. This phase includes four stations namely, Attaba, Bab El-Shariaa, El Guesh, Abdo Basha and Abbassia Stations. It is anticipated that same tunneling technique of Line 2 will be used in driving the tunnel during the construction of this phase. The geological conditions prevailing in both Line 2 and the first phase of Line 3 lie totally within a Pleistocene deposit known as the young alluvial plain that represents the majority of the lowland portion of the Nile Valley in the Cairo area. The Nile River deposits governed the subsurface and groundwater conditions in considerable area of the project. The sediments in the alluvial Nile plain are generally fairly consistent with depth, but vary somewhat laterally as a result of the long history of river meanders, and alternate cycles of sedimentation and erosion before the construction of Aswan High Dam in Upper Egypt in the 1960's. These sediments are over 60-90 meters thick in the Cairo area underlain by tertiary sedimentary rocks and older basement rocks (Shata, 1988). Such geological formations limit the tunnel construction methods to pressurized full face tunneling machines. Tunneling through Nile alluviums is commonly associated with unfavorable surface and subsurface ground subsidence that could affect adversely the feeble buildings in Cairo. The difficulties of tunneling through these deposits are due to the relatively low strength, high deformability of soils and the shallow groundwater table. The control of ground subsidence is considered as one of the main tasks of the tunnel design that dictate the selection of an appropriate tunneling technology.

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Fig(1): Greater Cairo Metro Network (after Madkour et al., 1999)

Fig (2): Hydroshield used in Line 2 (after El-Nahhas, 1999)

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2. SUBSIDENCES DUE TO SOFT GROUND TUNNELING Ground subsidence associated with tunneling activities in soft grounds results from the axial ground loss in front of the tunnel, the radial ground loss at the peripherals of the tunnel and changes of the radial and tangential stresses around the tunnel. Ground losses describe the difference between the actual and theoretical volumes of the tunnel. Settlements induced by tunneling are influenced by the affinity of the soil to dilate or densify when sheared. Dilation results in a small influenced zone localized in the area above the tunnel, whereas densification is usually coupled with movements expanding towards the surface of the soil. In undrained cohesive soils, constant volume shearing is anticipated. Soil displacements associated with tunneling in most tunnel projects are collected, documented, interpreted and utilized as an input data in the prediction of future projects under similar conditions. Gained experiences are documented and statistically studied; consequently, many empirical relationships and recommendations have been developed. For estimation of settlement distribution above a single shielded-driven tunnel, the shape of the settlement trough at the ground surface resembles the shape of error or normal probability curve. Peck (1969) used the properties of an error function or the normal probability curve to represent the distribution of the surface ground settlement. Fig (3) illustrates the observed distribution of the ground settlement curve. According to Peck (1969), the Settlement value (S) at any point is given by:

−⋅= 2

2

max i2xexpS)x(S (1)

where (Smax) is the maximum settlement at the centre of the trough and (i) is the trough width parameter which represents the offset of the point of inflection of the normal probability curve. Many researchers suggested relations between the width parameter, the maximum settlement and the soil/tunnel characteristics (Peck, 1969; Attwell et al., 1986; Fujita, 1989, Mair & Taylor, 1997). Modeling via finite elements is considered the most powerful means of the analysis of tunneling-induced settlement as it allows the main parameters involved in the tunneling process to be accurately accounted for. Finite element can incorporate the actual geometry of the tunnel, the ground constitutive behavior, seepage towards and away from the tunnel, and the different construction phases associated with each excavation technique. Yet, complications in developing finite models covering the contemporary tunneling operations and intricate model parameters encourage the use novel models such as artificial neural networks (Nour El-Din, 2003). The current research makes use of the successful application of both nonlinear finite elements and neuronet models to predict the surficial settlement trough associated with the construction of the proposed Line 3 of the Cairo Metro between Attaba Station and Bab El-Sharia Station.

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Fig (3): Surface settlement profiles (after Peck, 1969)

3. THREE-DIMENSIONAL FINITE ELEMENT ANALYSIS OF TUNNEL Realistic analysis of soil-tunneling interaction requires an understanding of details of the tunneling technology to simulate their effects in the finite element model. The comprehensive features in tunneling simulation that should be considered in the FE modeling are illustrated in Fig (4) and summarized as follows:

• The nonlinear soil constitutive behavior, which depends mainly on the stress path, confining pressure and rate of loading,

• The unloading forces developed during ground excavation and the potential seepage towards or away from the tunnel,

• The radial and axial ground loss and the overcutting gap, • The pressurized excavation boundaries and ground support measures, • The effect of the potential marginal yield zones around the tunnel, • The tail-skin grouting and the hardening of grouting material with time, • TBM advancement and lining installation, • The mutual interaction between the excavated tunnel and the existing

underground pipelines and tunnels. Three dimensional analyses seem to be the most suitable numerical framework to tackle this kind of problems, since the state of stress and strain near the working face of the tunnel is fully three-dimensional in nature. The following sections describe the details of the finite element approach. The employed analysis is based on a rigorous three-dimensional code that was used in the evaluation of performance of the German Hydroshield in Greater Cairo geological formations (El-Sayed, 2001). In this algorithm, the soil, shield and liner are modeled using three-dimensional hexahedral elements.

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Fig

(4):

Mod

elin

g fe

atur

es o

f sof

t gro

und

shie

lded

tunn

elin

g (a

fter E

l-Say

ed, 2

001)

Ove

rcut

Segm

enta

l lin

ing

Shie

ld

Pressurized face

Inst

alla

tion

Shaf

t

Prob

able

def

orm

atio

n fie

ld

Soft

pres

suriz

ed

grou

t H

ard

grou

t

Exca

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kin

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ting

and

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& T

BM

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ting

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ound

St

ruct

ures

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able

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3.1. Constitutive Modeling The soil nonlinear behavior is identified by variable modulii dependent on the confining pressure and the stress level. The soil constitutive relationship is also expressed in an incremental form to account for the path-dependency. Using the incremental form of the constitutive matrix [Det], the tangential element stiffness matrix [Ket] can be written as:

[ ] [ ] [ ][ ]∫=element

eett

eet )Volume(dBDBK (2)

where the [Be] is the element strain-nodal displacement matrix. The loading modulus (Et) and the unloading-reloading modulus (Eur) are presented by the following exponential forms (Duncan and Chang, 1970):

( )( ) n

a

3

2

3

31fat psin2cosC2

sin1R1KpE

σ

φσ+φφ−σ−σ

−= (3)

n

a

3aurur p

pKE

σ= (4)

where Rf is the ratio between the ultimate and the failure deviator stresses, pa is the atmospheric pressure, n is the stiffness exponent, K is the loading stiffness coefficient, Kur is unloading-reloading stiffness coefficient, σ1 & σ3 are the major and the minor principal stresses and C&φ are the soil shear parameters. 3.2. Effect of Stress Path During the finite element analysis, it is not possible to pre-determine the regions subjected to loading or unloading in order to conclude whether the loading modulus or unloading modulus should be used. Since the loading modulus is lower than the unloading modulus as shown in Fig. (5), the use of the loading modulus can lead to numerical divergence when unloading occurs. Using the unloading-reloading modulus during the first iteration of every loading step will underestimate the displacement in the first iteration if loading occurs but the correct modulus will be used in subsequent iterations according to a parameter called the stress level (Duncan et al., 1984) depending on deviator stress, the shear parameters and the confining pressure as follows:

4

a

3

3

31

psincosC)(SL σ

⋅φσ+φ

σ−σ= (5)

The stress level (SL) is calculated for each gauss point and compared to the maximum value reached during the loading history at the same gauss point (SLmax). The modulus (E) depends on the parameter SL as following:

• If ( maxSLSL ≥ ), loading is taking place and the used modulus E=Et • If ( maxSL75.0SL ≤ ), unloading is taking place and used modulus E=Eur • If ( maxmax SL75.0SLSL >> ), neutral loading is taking place and the modulus

E is calculated by interpolation between Et and Eur as shown in Fig (5).

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Fig (5): Effect of stress path on soil stiffness (after Duncan et al., 1984)

3.3. Interface Modeling The shield-soil interface is modeled using a hyperbolic gap element. The liner-grout-soil interface is modeled by introducing grout elements with incremental hardening strength parameters and initial hydrostatic pressure equal to the grouting pressure. Details of the tail-grout characteristics and modeling are elaborated by El-Sayed (2001) as shown in Figs (6) and (7); the normal stiffness is considered a constant of very high value for the contact and of trivial value for the open gap. The tangential shear stiffness (kt) of the gap is defined by:

ns

a

n

n

sfiwtit p

RAkk

−=

σδσ

τγ

2

tan1 (6)

where kti is the stiffness coefficient, γw is the water unit weight, σn is the stress normal to the interface surface, Ai is the contact area and Rsf & ns are defined similarly to the soil hyperbolic model parameters.

Fig (6): Interface element

SLmax 0.75 SLmax

Et

Eur

SL

E

ε1

σ 1−

σ 3

Et

Eur

Local axes for convergent gap

Normal stiffness

Tangential stiffness

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Fig (7): Interface modeling for shielded tunneling (after El-Sayed, 2001)

Overcut

Shield elements

Gap elements

Soil elements

(a) Shield/soil interface modeling

Liner elements

Soil elements

(b) Liner/grouting/soil interface modeling

Tail gap

Shield/Liner

Enfolding ground

Initially prestressed grout elements

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3.4. Modeling of Staged Construction Tunnel excavation is modeled by removing a cluster of ground elements from the finite element meshing; conversely, lining elements are new elements that are added to the mesh; this algorithm is equivalent of the Stress Reversal Approach (Ahmed, 1991). The required changes in the mesh are applied to reconstruct the residual vector {R} resulting from the difference between the applied force and the straining forces and the tangential stiffness matrix [Kt]. The residual vector and the stiffness matrix are calculated at the beginning of each iteration (Newton-Raphson), i.e. the (i+1)th iteration is described by the following equation:

[ ] { } { }RUK tt1i

tt1it

tti

∆++

∆++

∆+ =& { } [ ] { }∑ ∫=

∆+∆+ σ−=elements of No.

1e elemente

tti

Te

tt )Volume(dBF (7)

where {F} is the nodal force vector. The left subscript denotes the iteration process and the left superscript donates a sequential time index. If the iteration superscript is zero, the matrix or vector is calculated at the end of the previous time step. The stress increment can be calculated from the strain {ε} using the following integration:

{ } { } { } { } [ ] { }{ }

{ }

∫ε

ε

∆+∆++

∆+∆++

∆++

∆+

ε+σ=σ+σ=σtt1i

tti

dDettti

tt1i

tti

tt1i & (8)

Numerical integration is used to evaluate the integral in the stress calculations. Employing a predictor-corrector method (Modified Euler scheme) as follows:

{ } { } [ ] { } { } [ ] [ ] { }UBD21D

21 tt

1ieettti

tti

tt1iet

tti

tti

tt2/1i

&&∆+

+∆+∆+∆+

+∆+∆+∆+

+ +σ=ε+σ≅σ (9)

then is calculated using the predicated stress . The stress is updated as follows:

{ } { } [ ] [ ] { }UBD tt1ieet

tt2/1i

tti

tt1i

&∆++

∆++

∆+∆++ +σ≅σ (10)

3.5. Geotechnical Data Tunnel depth at the considered section is 20.5m as shown in Fig(8). According to Hamza Associates (2002), the soil in the region of the bored tunnel between Attaba Station and Bab El Shariaa has the following distinctive layers:

1. Man-made ground: This layer is encountered at the ground surface and extends down to a depth from the ground surface of 5.50 m. This layer constitutes mainly of sand, stone pieces, red bricks, asphalt pieces, silt and clay.

2. Clay: Firm to stiff brown micaceous calcareous CLAY with little calcareous pebbles. This layer is encountered below the first layer and extends down to a depth from the ground surface of 10.45 m. This layer is interbedded at a depth of 9.0 by a 0.45 m thick layer of firm Silt.

3. Sand: Dense to very dense yellowish brown micaceous calcareous slightly silty to silty poorly graded SAND with different percentage of gravel at some depths varying from slightly gravelly to gravelly. This layer is encountered below the second layer and extends down to the end of the boring.

The groundwater was encountered at a depth of 1.1m. The geotechnical properties of the encountered soil strata are listed in Table (1).

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Fig (8): Tunnel configuration and soil stratification

Table (1): The geotechnical parameters

Stratum SPT

(blows)

C

(kPa) φo K Ku Rf n Rsf ns

γ

(kN/m3)

FILL 7 0 25 50 150 0.8 0.6 0.8 0.6 17

CLAY 15 50 0 100 300 0.8 0.5 0.8 0.5 18.5

SAND 50 0 37 600 1800 0.8 0.5 0.8 0.5 19

3.5. Ground Loss The face loss is estimated to be 0.09%, while the loss along perimeter of the TBM (which is mainly due to the overcutting) is estimated to be 0.14%. These data are based on the results of the monitoring programs performed on two test sections that were heavily instrumented during the construction Line 2 - Phase 1A (Hamza Associates, 1995). Campenon Bernard-SGE (1999) reported comparable values of ground losses during the driving of Al-Azhar Twin Roadway Tunnels. 3.6. Results of Finite Element Simulation The data input required several hours to be completed and verified during all stages of tunnel construction. The mesh of the finite element comprises 3015 element and 3528 joint. Twenty two steps were employed in the analysis. Each step is divided into 10 substeps. Newton-Raphson iterative procedure was used in the analysis. About 4 hours were required to complete the analysis using Pentium IV-1.7 GHz computer. The predicted settlement from the finite element analysis is shown in Fig (9). The maximum settlement is 13.8 mm and the trough extends to about 35m.

86

Fig (9): The spatial distribution of settlement using finite element method

4. THE NEURONET MODEL Artificial Neural Network (ANN) is a simulation of human-like response within the computer hardware and specialized software using multiple layers of interconnected processing elements called neurons. ANN systems are not programmed, they just learn by examples. The learning process of the neural networks is accomplished by adjusting the strengths of the binds between the neurons to cause the overall network to output proper results. The learning algorithm is based upon the backpropagation algorithm (Werbos, 1974; Rumelhart et al., 1986) by which the weight of the connections between the neurons are adjusted to reduce the error between the desired output and the actual output. Artificial neural networks are formed by clustering of the primitive artificial neurons into layers, which are then connected to one another. Some of the neurons interface with the external environment to receive the inputs and other neurons provide the network’s outputs through the input and the output layer. All the rest of the neurons are included in a number of hidden layers between the input and the output layers (Tsoukalas and Uhrig, 1997). 4.1. Network Topology The model comprises two-staged ANN model similar to procedures of hand calculations of tunnel-induced settlements. The configuration of the neural network is shown in Fig (10). The first stage (A), indicated by light solid arrows, is used to estimate the maximum settlement using the following input parameters: the diameter of tunnel, the depth of the tunnel, depth of the groundwater table, thickness of different soil layers, and the average values of the standard penetration test (SPT) counted in each layers. The second stage (B), indicated by bold dashed arrows, is used to estimate the trough width from the following input variables: the tunnel depth, the excavated diameter of tunnel and, the maximum surface settlement obtained from the first stage.

87

Fig (10): The ANN topology

(* indicates multiple nodes)

*

*

88

A key motive to divide the ANN model into two stages is the difference in size of data of the maximum settlements and trough widths. Most instrumentation programs exercise frequenter measurements of the longitudinal maximum settlement than acquiring the transverse troughs (Murray, 1990). As the complex interaction among the network architectural units makes model optimization a difficult task, the network configuration has been determined using the evolution principle of Genetic Algorithms (GAs). Genetic Algorithms combine selection, crossover, and mutation operators with the goal of finding the best solution to the problem. The introduction of GAs in ANNs is implemented by introducing both the structure and parameters of the neural network as a fixed-length string and a population of such string is evolved to minimize the error between the predicted outputs and the training set. There are four stages in the genetic search process: initialization, evaluation, selection, crossover and mutation. In the initialization stage, a population of genetic structures which are randomly distributed in the solution space is selected as the starting point of the search. In the second stage, each structure is evaluated using a fitness function. On the basis of their relative fitness values, structures in the current population are selected for reproduction.. The selected structures are recombined using crossover. A mutation operator, which arbitrarily alters one or more components of a selected structure, provides the means for introducing new information into the population (Nour El-Din, 2003). 4.2. Generalization Technique One of the primary goals in training neural networks is to ensure that the network performs well on data that is has not been trained on; this principal is called “generalization”. The standard method of ensuring good generalization is to divide the training data into multiple data sets. The most common data sets are the training, testing, and validation data sets. Testing (cross-validation) data set is used by the network periodically during training as a test for performance. During testing, the weights are not trained, but the performance of the network on the cross-validation set is saved and compared to past values. If the network is starting to over-train on the training data, the cross-validation performance will begin to degrade. Thus, the cross validation data set is used to determine when the network has been trained as well as possible without over-training. About 50% of the data in the input and desired files will be used for training, 25% are used for cross validation (testing) and 25% for validation of the model. The network is “optimal” when the error in the cross validation set is at its minimum position. 4.3. Training and Testing Databases The training database was collected from field measurements for surface settlement associated with soft ground tunneling and the corresponding geotechnical information. The database covers a wide range of variation in geotechnical data and the tunnel geometrical data (depth and diameter). The database used in developing the stages (A) and (B) are detailed in Table (2). The considered cases comprise the following tunneling techniques: Hydroshield Tunneling (Greater Cairo Metro, Al-Azhar Roadway Tunnels, El-Salam Syphon), Earth Pressure Balance TBM (Alexandria Wastewater Tunnel), Compressed Air TBM (Cairo Wastewater Tunnel) and Open Face TBM (Alberta Experimental Tunnels). Also, the data bases include tunnel diameters ranging between 2.00 and 9.45m with

89

depth ranging between 5 to 25m. The geotechnical conditions range between soft alluvial deposits with shallow groundwater depths for Cairo projects, marine geological deposits of El-Salam Syphon, fractured limestone for Alexandria waste water projects, and glacial clay till for Alberta tunnels. Table (2): Database of the tunnels used to develop the ANN

Project and Location Depth of

springline (m)

Tunnel

Diameter

(m)

No. of

Cases (A*)/(B**)

Greater Cairo Metro – Egypt (after Hamza Associates, 1995 & 1997) 13.4-25 9.45 17/2

North Tunnel – Al-Azhar Roadway Tunnels – Egypt

(after Campenon Bernard-SGE, 1999) 19.5-23 9.45 4/1

Alberta Experimental Tunnels – Canada (alter El-Nahhas, 1980) 27& 11.7 2.56 & 6.2 2/9

Alexandria Wastewater Tunnel – Egypt (after Kotait, 2001) 14 2.81 1/1

Spinal Tunnel – Cairo Wastewater Tunnel – Egypt

(after El-Nahhas et al., 1990) 15.6 5.15 1/1

First Tunnel – Al-Salam Syphon – Egypt

(after Esmail, 1997) 24 6.6 1/1

Soft Soil Tunnels (after Lee et al., 1999) 5 2.5 0/7

* Used in ANN for stage (A) ** Used in ANN for stage stage (B) 4.4. Validation of the Proposed ANN Model The performance of the model has to be checked for an independent validation data set that was previously unseen by the model. The coefficient of correlation (R2), as defined by Chapra and Canale (1990), is the key criteria to evaluate the performance of analytical models. The value of R2 generally range between zero and one. The model behavior can be categorized according to the value of (R2) as shown in Table (3). Tables (4) and (5) show that the value of the coefficient of correlation between the validation (production) data set and the ANN results are generally less the correlation with training or cross-validation sets. However, strong correlation is observed between all data sets and ANN results. The plots of the measured and predicted settlements for training, cross validation and production are shown in Figs (11) and (12). The results indicate that the model performs well in obtaining the Gaussian settlement distribution characteristics

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Table (2): ANN performance versus (R2) values (after Chapra and Canale, 1990) Category Value of R2

Strong correlation exists between output of the ANN and actual data More than 0.8

Medium correlation exists between output of the ANN and actual data Between 0.2 and 0.8

Weak correlation exists between output of the ANN and actual set Less than 0.2

Table (3): Correlation Coefficient for stage (A)

Data set R2

All data 0.92

Training data 0.95

Cross-validation data 0.98

Production data 0.80

Table (4): Correlation Coefficient for stage (B)

Data set R2

All data 0.97

Training data 0.99

Cross validation data 0.99

Production data 0.84

0

3

6

9

12

15

18

21

24

27

30

0 3 6 9 12 15 18 21 24 27 30

Actual maximum settlement (mm)

Pred

icte

d m

axim

um s

ettle

men

t (m

m)

Greater Cairo Metro

North Tunnel – Al-AzharRoad Tunnels

Alberta ExperimentalTunnels

Alexandria Waste WaterProject

Spinal Tunnel – CairoWastewater

First Tunnel – Al-SalamSyphon

Predicted = Measured

Fig (11): Stage (A) performance

Roadway Tunnels

91

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20

Actual trough width parameter (m)

Pred

icte

d tr

ough

wid

th p

aram

eter

(m)

Greater Cairo Metro

North Tunnel – Al-AzharRoad Tunnels

Alberta ExperimentalTunnels

Alexandria Waste WaterProject

Spinal Tunnel – CairoWastewater

First Tunnel – Al-SalamSyphon

Soft Soil Tunnels

Predicted = Measured

Fig (12): Stage (B) performance

4.4. ANN Prediction of Line 3 Settlement The ANN model was feed the tunnel configuration and SPT data to analyze the tunneling status between Attaba and Bab El-Sharia Stations. The time consumed in feeding the input and production of the output was only a few minutes. The model gives a maximum settlement of 13.8mm and a trough width parameter of about 8.5m. A comparison of results of the two models is shown in Fig (13) which implies that almost typical results can be obtained using both approaches. The maximum difference between the two models is about 1mm near the end of the trough. Variation in the parameters of the numerical model or geotechnical data may give differences larger than predicted discrepancies in the analyses.

0

2

4

6

8

10

12

14

16

-50 -40 -30 -20 -10 0 10 20 30 40 50

Distance from the tunnel CL (m)

Settl

emen

t (m

m) ANN model

FE model

Fig (13): The results of the finite element model and the neuronet model

Roadway Tunnels

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5. SUMMAY AND CONCLUSIONS In the present study, two dissimilar analytical approaches are utilized to predict the settlement trough associated with construction of Line 3 bored tunnel between Attaba Station and Bab El-Sharia Station. The first approach is based on nonlinear three-dimensional finite element simulation of the Hydroshield tunneling technology. The model adapts the main factors affecting the pressurized bentonite slurry tunneling such as unloading forces due to excavation, ground nonlinearly, interface conditions, engineering properties of shield, rate of advance, machine overcutting, face pressure, yielding zones and the tail pressurized grouting process. The second approach is based on the back-propagated supervised ANN enhanced by the evolution capabilities of GAs. Many projects in Egypt and abroad were utilized to train, test and validate the proposed model. The database used in model training covers a wide spectrum of geological conditions, configurations and construction techniques. The two approaches give compatible results concerning the maximum settlement and the trough width. The ANN model proved to be reliable and robust in determining the settlement associated with tunneling despite the small effort and time to develop the model. Results of mentoring programs of the future tunneling projects cab be employed in developing more yielding ANN models to compete with the current practice of complicated finite element methods. 6. ACKNOWLEDGEMENT The authors are deeply grateful to Dr. A. Abu-Krisha of the National Authority of Tunnels (NAT) for providing invaluable monitoring and geotechnical information for the Greater Cairo Metro Project. 7. REFERENCES

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