Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest
Liquefaction evaluation on the sands induced by earthquake is of significance for engineers in seismic design. In this study, the random forest (RF) method is introduced and adopted to evaluate the seismic liquefaction potential of soils based on the shear wave velocity. The RF model was developed u...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/3230343 |
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| author | Lu Liu Shushan Zhang Xiaofei Yao Hongmei Gao Zhihua Wang Zhifu Shen |
| author_facet | Lu Liu Shushan Zhang Xiaofei Yao Hongmei Gao Zhihua Wang Zhifu Shen |
| author_sort | Lu Liu |
| collection | DOAJ |
| description | Liquefaction evaluation on the sands induced by earthquake is of significance for engineers in seismic design. In this study, the random forest (RF) method is introduced and adopted to evaluate the seismic liquefaction potential of soils based on the shear wave velocity. The RF model was developed using the Andrus database as a training dataset comprising 225 sets of liquefaction performance and shear wave velocity measurements. Five training parameters are selected for RF model including seismic magnitude (Mw), peak horizontal ground surface acceleration (amax), stress-corrected shear wave velocity of soil (Vs1), sandy-layer buried depth (ds), and a new introduced parameter, stress ratio (k). In addition, the optimal hyperparameters for the random forest model are determined based on the minimum error rate for the out-of-bag dataset (ERROOB) such as the number of classification trees, maximum depth of trees, and maximum number of features. The established random forest model was validated using the Kayen database as testing dataset and compared with the Chinese code and the Andrus methods. The results indicated that the random forest method established based on the training dataset was credible. The random forest method gave a success rate for liquefied sites and even a total success rate for all cases higher than 80%, which is completely acceptable. By contrast, the Chinese code method and the Andrus methods gave a high success rate for liquefaction but very low for nonliquefaction which led to the increase of engineering cost. The developed RF model can provide references for engineers to evaluate liquefaction potential. |
| format | Article |
| id | doaj-art-d8f718c25daa49b5b0f68637e453e2b8 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d8f718c25daa49b5b0f68637e453e2b82025-02-03T05:43:35ZengWileyAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/3230343Liquefaction Evaluation Based on Shear Wave Velocity Using Random ForestLu Liu0Shushan Zhang1Xiaofei Yao2Hongmei Gao3Zhihua Wang4Zhifu Shen5Urban Underground Space Research CenterUrban Underground Space Research CenterUrban Underground Space Research CenterUrban Underground Space Research CenterUrban Underground Space Research CenterUrban Underground Space Research CenterLiquefaction evaluation on the sands induced by earthquake is of significance for engineers in seismic design. In this study, the random forest (RF) method is introduced and adopted to evaluate the seismic liquefaction potential of soils based on the shear wave velocity. The RF model was developed using the Andrus database as a training dataset comprising 225 sets of liquefaction performance and shear wave velocity measurements. Five training parameters are selected for RF model including seismic magnitude (Mw), peak horizontal ground surface acceleration (amax), stress-corrected shear wave velocity of soil (Vs1), sandy-layer buried depth (ds), and a new introduced parameter, stress ratio (k). In addition, the optimal hyperparameters for the random forest model are determined based on the minimum error rate for the out-of-bag dataset (ERROOB) such as the number of classification trees, maximum depth of trees, and maximum number of features. The established random forest model was validated using the Kayen database as testing dataset and compared with the Chinese code and the Andrus methods. The results indicated that the random forest method established based on the training dataset was credible. The random forest method gave a success rate for liquefied sites and even a total success rate for all cases higher than 80%, which is completely acceptable. By contrast, the Chinese code method and the Andrus methods gave a high success rate for liquefaction but very low for nonliquefaction which led to the increase of engineering cost. The developed RF model can provide references for engineers to evaluate liquefaction potential.http://dx.doi.org/10.1155/2021/3230343 |
| spellingShingle | Lu Liu Shushan Zhang Xiaofei Yao Hongmei Gao Zhihua Wang Zhifu Shen Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest Advances in Civil Engineering |
| title | Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest |
| title_full | Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest |
| title_fullStr | Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest |
| title_full_unstemmed | Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest |
| title_short | Liquefaction Evaluation Based on Shear Wave Velocity Using Random Forest |
| title_sort | liquefaction evaluation based on shear wave velocity using random forest |
| url | http://dx.doi.org/10.1155/2021/3230343 |
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