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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

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Abstract

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.

摘要

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。

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Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Ahmad Mahmood & Xiao-wei Tang  (唐小微)

  2. Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu, 28100, Pakistan

    Ahmad Mahmood

  3. Faculty of Management and Economics, Dalian University of Technology, Dalian, 116024, China

    Jiang-nan Qiu  (裘江南) & Wen-jing Gu  (谷文静)

  4. Department of Civil Engineering, Abasyn University, Peshawar, 25000, Pakistan

    Ahmad Feezan

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  1. Ahmad Mahmood
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  2. Xiao-wei Tang  (唐小微)
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  3. Jiang-nan Qiu  (裘江南)
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Corresponding author

Correspondence to Jiang-nan Qiu  (裘江南).

Additional information

Foundation item: 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

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Mahmood, A., Tang, Xw., Qiu, Jn. et al. A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks. J. Cent. South Univ. 27, 500–516 (2020). https://doi.org/10.1007/s11771-020-4312-3

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