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Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

Frontiers of Structural and Civil Engineering 2021年第2期15卷 490-505页

作者： Mahmood ahmaD Xiao-Wei TANG Jiang-Nan QIU feezan ahma Wen-Jing GU State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian 116024China Department of Civil Engineering University of Engineering and Technology Peshawar(Bannu Campus)Bannu 28100Pakistan School of Economics&Management Dalian University of TechnologyDalian 116024China Department of Civil Engineering Abasyn UniversityPeshawar 25000Pakistan

This study investigates the performance of four machine learning(ML)algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network(BBN)learning software *** BBN structures that were developed by ML algorithms-K2,hill climbing(HC),tree augmented naive(TAN)Bayes,and Tabu search were adopted to perform parameter learning in Netica,thereby fixing the BBN *** performance measure indexes,namely,overall accuracy(OA),precision,recall,F-measure,and area under the receiver operating characteristic curve,were used to evaluate the training and testing BBN models’performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC *** sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors,whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction *** results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

关键词： seismic soil liquefaction Bayesian belief network cone penetration test parameter learning structural learning

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