Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks
Autor: | Xiaowei Tang, Jiangnan Qiu, Mahmood Ahmad, Feezan Ahmad |
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Rok vydání: | 2021 |
Předmět: |
Computer science
Bayesian network Liquefaction Genetic programming 02 engineering and technology computer.software_genre 01 natural sciences Performance results Lateral displacement 010101 applied mathematics 020303 mechanical engineering & transports 0203 mechanical engineering Architecture Linear regression Sensitivity (control systems) Data mining 0101 mathematics Probabilistic framework computer Civil and Structural Engineering |
Zdroj: | Frontiers of Structural and Civil Engineering. 15:80-98 |
ISSN: | 2095-2449 2095-2430 |
Popis: | 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. |
Databáze: | OpenAIRE |
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