Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes. Therefore, an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development. This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network (BBN) approach based on an interpretive structural modeling technique. The BBN models are trained and tested using a wide-range case-history records database. The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions. The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models. The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships, with reasonable precision. This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.

Automated summary

A novel probabilistic framework based on Bayesian belief network was proposed for evaluating liquefaction-induced lateral displacement, with two models predicting lateral displacements for different ground conditions and compared with multiple linear regression and genetic programming models. The results showed that the proposed models have reasonable precision in learning complex relationships between lateral displacement and its influencing factors.