Abstract

The residual shear strength of liquefied soil is critical to estimating the displacement of lateral spreading. In the paper, an Artificial Neural Network model was trained to predict the residual shear strength ratio based on the case histories of lateral spreading. High‐quality case histories were analyzed with Newmark sliding block method. The Artificial Neural Network model was used to predict the residual shear strength of liquefied soil, and the post‐liquefaction yield acceleration corresponding with the residual shear strength was obtained by conducting limit equilibrium analysis. Comparing the predicted residual shear strength ratios to the recorded values for different case histories, the correlation coefficient, R, was 0.92 and the mean squared error (MSE) was 0.001 for the predictions by the Artificial Neural Network model. Comparison between the predicted and reported lateral spreading for each high‐quality case history was made. The results showed that the probability of the lateral spreading calculated with the Newmark sliding block method using the residual shear strength was 98% if a lateral spreading ratio of 2.0 was expected and a truncated distribution was used. An exponential relationship was proposed to correlate the residual shear strength ratio to the equivalent clean sand corrected SPT blow count of the liquefied soil.

Highlights

  • Liquefaction is the phenomenon whereby saturated sandy soil behaves like a liquid during the shaking by earthquakes

  • The residual shear strength ratio was successfully predicted by the Artificial Neural Network model, with a good correlation coefficient for the predicted values, there are still uncertainties that are limiting the further application of Artificial Neural Network. e number of case histories used in the analysis is limited. ere are 43 case histories in total in the development of Artificial Neural Network model, and the median values for the residual shear strength ratio and the equivalent clean sand corrected SPT blow count cannot represent the variance of the soil parameters and may induce errors when training Artificial Neural Network model

  • Regarding the conventional Newmark sliding block method used in this paper, the intact soil above the liquefied soil is assumed to be a perfectly rigid block, the sliding surface used in the limit equilibrium analysis may not be consistent with the location where the liquefaction occurred, and the deformation of the sliding mass is omitted, so the inaccuracy of the predicted lateral spreading is too attributed to the fact that the dynamic response of the sliding mass is not considered

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Summary

Introduction

Liquefaction is the phenomenon whereby saturated sandy soil behaves like a liquid during the shaking by earthquakes. Based on the residual shear strength values of the high-quality case histories, an exponential equation was proposed to represent the relationship between the residual shear strength ratio and the equivalent clean sand corrected SPT blow count of the liquefied soil. By analyzing the reported SPT blow counts and the residual shear strength ratios for the liquefied soil from the database of lateral spreading, it provides an opportunity to investigate the residual shear strength of the liquefied soil using Artificial Neural Network model. It has to be noticed that, except for the residual shear strength relationship proposed by Olson and Johnson [2] and Ozener [19], the other relationships or empirical models used to calculate the residual shear strength of liquefied soil are developed based on the case histories of flow failures. E fines content correction equation of liquefied sand by Seed [1] is used to consider the influence of fines content on the SPT blow count and the residual shear strength. e equivalent clean sand corrected SPT blow count can be obtained in equation (1), where (N1)60-cs is the equivalent clean sand SPT blow count and Ncr is the fines content correction for the SPT blow count recommended by Seed [1], as shown in Table 1: N1􏼁60−cs N1􏼁60 + Ncr

Introduction to Artificial Neural Network
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