A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from
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RESEARCH ARTICLE
A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data Mahmood AHMADa,b, Xiao-Wei TANGa, Jiang-Nan QIUc* , Feezan AHMADd, Wen-Jing GUc a
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 c School of Economics & Management, Dalian University of Technology, Dalian 116024, China d Department of Civil Engineering, Abasyn University, Peshawar 25000, Pakistan
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Corresponding author. E-mail: [email protected]
© Higher Education Press 2020
ABSTRACT The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability (LLDV) when determining whether liquefaction is likely to cause damage at the ground’s surface. This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network (BBN) methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model. The BBNbased LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learning (ML) algorithm-K2 and domain knowledge (DK) data fusion methodology. Compared with the C4.5 decision tree-J48 model, naive Bayesian (NB) classifier, and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen’s kappa coefficient, the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage. The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations, and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development. The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling. This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefied sites based on an engineering point of view. KEYWORDS Bayesian belief network, liquefaction-induced damage potential, cone penetration test, soil liquefaction, structural learning and domain knowledge
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Introduction
Liquefaction potential and liquefaction-induced ground failures are two integral and correlated issues in the assessment of liquefaction hazard risks caused by earthquakes. The former aims to evaluate whether or not the soil liquefied after an earthquake event, while the latter not only aims to predict soil liquefaction-induced hazards occurring after soil liquefaction but also to assess the Article h
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