4.5 Article

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Journal

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING
Volume 15, Issue 1, Pages 80-98

Publisher

HIGHER EDUCATION PRESS
DOI: 10.1007/s11709-021-0682-3

Keywords

Bayesian belief network; seismically induced soil liquefaction; interpretive structural modeling; lateral displacement

Funding

  1. National Key Research & Development Plan of China [2018YFC 1505300-5.3, 2016YFE0200100]
  2. Key Program of the National Natural Science Foundation of China [51639002]

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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.
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.

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