A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks  

基于贝叶斯置信网络的CPT地震液化势混合评估方法（英文）

作　　者：MAHMOOD Ahmad TANG Xiao-wei QIU Jiang-nan GU Wen-jing FEEZAN Ahmad MAHMOOD Ahmad;唐小微;裘江南;谷文静;FEEZAN Ahmad(State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian 116024,China;Department of Civil Engineering,University of Engineering and Technology Peshawar(Bannu Campus),Bannu 28100,Pakistan;Faculty of Management and Economics,Dalian University of Technology,Dalian 116024,China;Department of Civil Engineering,Abasyn University,Peshawar 25000,Pakistan)

机构地区：[1]State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian 116024,China [2]Department of Civil Engineering,University of Engineering and Technology Peshawar(Bannu Campus),Bannu 28100,Pakistan [3]Faculty of Management and Economics,Dalian University of Technology,Dalian 116024,China [4]Department of Civil Engineering,Abasyn University,Peshawar 25000,Pakistan

出　　处：《Journal of Central South University》2020年第2期500-516,共17页中南大学学报（英文版）

基　　金：Projects(2016YFE0200100,2018YFC1505300-5.3)supported by the National Key Research&Development Plan of China;Project(51639002)supported by the Key Program of National Natural Science Foundation of China

摘　　要：Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.地震液化评估是一个复杂的非线性过程,受多种因素的不确定性和复杂性的影响。贝叶斯置信网络(BBN)是一个可靠有效的工具,可以提供一个合适的框架来处理这些不确定性和因果关系。本研究采用一种混合方法来建立基于静力触探试验(CPT)案例记录数据的贝叶斯置信网络(BBN)模型,以评估土壤的地震液化势。在这种混合方法中,先通过结合领域知识(DK)的解释结构建模(ISM)技术建立朴素模型,再在K2算法中嵌入朴素模型的相关信息建立BBN-K2和DK模型。将BBN模型的结果与现有的人工神经网络(ANN)和C4.5决策树(DT)模型进行了比较和验证,发现用混合方法建立的BBN模型在液化势评估中具有良好的适应性和应用前景。用混合方法建立的BBN模型为岩土工程师评估易受地震液化影响的场地环境提供了可行的工具。最后对基于混合方法的BBN模型进行了灵敏度分析,并对液化场地进行了最可能的解释,以了解液化现象的最可能情况。

关 键 词：Bayesian belief network cone penetration test seismic soil liquefaction interpretive structural modeling structural learning 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O212[自动化与计算机技术—控制科学与工程]