Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8886781 |
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| author | Yanxin Yang Bai Yang Chunhui Su Jianlin Ma |
| author_facet | Yanxin Yang Bai Yang Chunhui Su Jianlin Ma |
| author_sort | Yanxin Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2828f5db74e44b7a9773f3322a7a7e4d |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-2828f5db74e44b7a9773f3322a7a7e4d2025-08-20T03:54:38ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88867818886781Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral SpreadingYanxin Yang0Bai Yang1Chunhui Su2Jianlin Ma3School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin, Guangxi 541004, ChinaSchool of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin, Guangxi 541004, ChinaSchool of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin, Guangxi 541004, ChinaSchool of Civil Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaThe 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.http://dx.doi.org/10.1155/2020/8886781 |
| spellingShingle | Yanxin Yang Bai Yang Chunhui Su Jianlin Ma Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading Advances in Civil Engineering |
| title | Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading |
| title_full | Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading |
| title_fullStr | Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading |
| title_full_unstemmed | Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading |
| title_short | Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading |
| title_sort | application of residual shear strength predicted by artificial neural network model for evaluating liquefaction induced lateral spreading |
| url | http://dx.doi.org/10.1155/2020/8886781 |
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