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Updated Support Vector Machine for Seismic Liquefaction Evaluation Based on the Penetration Tests
Simplified techniques based on in situ testing methods are commonly used to predict liquefaction potential. Many of these simplified methods are based on finding the liquefaction boundary separating two categories (the occurrence or non-occurrence of liquefaction) through the analysis of liquefaction case histories. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all the independent variables, such as the seismic and soil properties, using conventional modeling techniques. Hence, in many of the conventional methods that have been proposed, simplified assumptions have been made. In this study, an updated support vector machine (SVM) based on particle swarm optimization (PSO) is used to evaluate liquefaction potential in two separate case studies. One case is based on standard penetration test (SPT) data and the other is based on cone penetration test (CPT) data. The SVM model effectively explores the relationship between the independent and dependent variables without any assumptions about the relationship between the various variables. This study serves to demonstrate that the SVM can “discover” the intrinsic relationship between the seismic and soil parameters and the liquefaction potential. Comparisons indicate that the SVM models perform far better than the conventional methods in predicting the occurrence or non-occurrence of liquefaction.
Updated Support Vector Machine for Seismic Liquefaction Evaluation Based on the Penetration Tests
Simplified techniques based on in situ testing methods are commonly used to predict liquefaction potential. Many of these simplified methods are based on finding the liquefaction boundary separating two categories (the occurrence or non-occurrence of liquefaction) through the analysis of liquefaction case histories. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all the independent variables, such as the seismic and soil properties, using conventional modeling techniques. Hence, in many of the conventional methods that have been proposed, simplified assumptions have been made. In this study, an updated support vector machine (SVM) based on particle swarm optimization (PSO) is used to evaluate liquefaction potential in two separate case studies. One case is based on standard penetration test (SPT) data and the other is based on cone penetration test (CPT) data. The SVM model effectively explores the relationship between the independent and dependent variables without any assumptions about the relationship between the various variables. This study serves to demonstrate that the SVM can “discover” the intrinsic relationship between the seismic and soil parameters and the liquefaction potential. Comparisons indicate that the SVM models perform far better than the conventional methods in predicting the occurrence or non-occurrence of liquefaction.
Updated Support Vector Machine for Seismic Liquefaction Evaluation Based on the Penetration Tests
Zhao, Hong-Bo (author) / Ru, Zhong-Liang (author) / Yin, Shunde (author)
Marine Georesources & Geotechnology ; 25 ; 209-220
2007-12-05
12 pages
Article (Journal)
Electronic Resource
Unknown
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