ppt on evaluation of liquefaction potential in modern methodology

 Evaluation of Liquefaction

potential in Modern Methodology

BY

SUVADEEP DALAL

DEPARTMENT OF CIVIL ENGINEERING

IIT KHARAGPUR, KHARAGPUR

APRIL 2012

Fig.1: Schematic of the saturation mound

Liquefaction Potential

1. Thickness of the liquefied layer;2. Proximity of the liquefied layer to the surface; and3. Amount by which the factor safety (FS) is less than 1.0.

In whichF = 1-FS for FS<=1, and F = 0 for FS>1, andDepth weighting factor, w(z) = 10-0.5z

Methods for evaluation of Liquefaction potential

Methods based on computation of the cyclic stress ratio.

Methods based on SPT measurement.Methods based on CPT measurement.Methods based on Vs measurement.Probability-based methods.Empirical methods.

Assessment of Liquefaction Potential Using Neural Networks

Neural Network(NN) mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons. They are usually used to model complex relationships between inputs and outputs or to find patterns in data.

Assessment of Liquefaction Potential Using Neural Networks

Empirical approach.Based on neural networks technology.Maps the basic inputs directly to the output.

Neural Networks Implementation ofPattern Recognition

Pattern recognition problem.Description of the objects in a representation space, the

specification of an interpretation space, and the mapping from representation space to interpretation space.

Important attributes need to be determined (e.g. relative density, ground shaking intensity) and mapped into the liquefaction susceptibility space.

The neural network structure

Fig.3: Schematic diagram of a simulated neuron

The neural network structure

Fig. 4. Schematic diagram of a neural net: connected

processing elements arranged in layers.

There are two layers serving as buffers. An input buffer (layer) where data are presented to the network and an output buffer (layer) which holds the response of the network to a given set of input. Layers distinct from the input and output layers are called the internal or hidden layers.

Cyclic stress ratio and liquefaction -- US practice

NN = CN * N

Empirical equation -- Chinese approach

Ncrit = N0(0.9+0.1(ds-dw))√(3/Pc)

Fig.5: Liquefaction occurrences as a function of stress ratio and penetration resistance, data from 1976 Tangshan Earthquake

Liquefaction Data Base

(i) Tangshan dataName of the site Tangshan

Data collected by Institute of Earthquake Engineering, China Academy of Building Research, Beijing, China.

(ii) Xing Xiang dataName of the site Xing Xiang

Table .1: Reference value of the standard penetration strike numbers

Input attributes

1. Ground shaking intensity (MMI).2. Ground water level (m).3. Depth of liquefiable soil deposit (m).4. Soil penetration resistance (blow count/ft).

Correction of Data

N =Cc * Nc

Cc = 1 to 0.83

Nn = Cn * Cc * Nc

Recall Accuracy--Tangshan Data

Table .2: Training data and recall test results (1976 Tangshan Earthquake)

Recall Accuracy--Tangshan Data

Fig. 6. Histogram of recall error, recall test based on the Tangshan data.

Prediction Test - Tangshan

Table .3: Prediction test data and results (1976 Tangshan Earthquake)

Prediction Test - Tangshan

Fig. 7. Histogram of 'prediction' error, prediction for theTangshan data.

Prediction Test – Xing Xiang

Table .4: Prediction test data and results for the city of Xing Xiang

Prediction Test – Xing Xiang

Table 4. Prediction test data and results for the city of Xing Xiang(contd.)

Conclusions

1. Maps the basic inputs directly to the output without the need for intermediary steps.

2. Direct and hence simple.

3. Incremental learning and adaptive capabilities.

4. Autonomous and automatic synthesis of the underlying data structure.

5. The procedure is not completely automatic. The burden lies in data screening and selection, as is also true for other empirical methods.

References(i) Tung.A.T.Y.; Wang.Y.Y; Wong.F.S. Assessment of

liquefaction potential using neural networks. Soil Dynamics and Earthquake Engineering 12 (1993) 325-335.

(ii) Toprak.S,A. M.ASCE; Holzer.T.L. Liquefaction Potential Index: Field Assessment. J. Geotech. and Envir. Engrg. (ASCE) 2003; 129:4(315).

(iii) Moss.R.E.S., M.ASCE; Seed.R. B., M.ASCE; Kayen. R. E., M.ASCE; Stewart. J. P, M.ASCE; Kiureghian.A.D., M.ASCE; and Cetin.K.O., M.ASCE. CPT-Based Probabilistic and Deterministic Assessment of In Situ Seismic Soil Liquefaction Potential, J. Geotech. and Envir. Engrg. (ASCE) 2006; 132:8(1032).