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A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data

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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 BBN-based 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.

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Acknowledgements

The research presented in this paper was part of the research sponsored by the National Key Research & Development Plan of China (Nos. 2018YFC1505305 and 2016YFE0200100) and Key Program of the National Natural Science Foundation of China (Grant No. 51639002). Much gratitude is extended to the experts for their opinions on the BBN model building.

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Authors and Affiliations

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

    Mahmood Ahmad & Xiao-Wei Tang

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

    Mahmood Ahmad

  3. School of Economics & Management, Dalian University of Technology, Dalian, 116024, China

    Jiang-Nan Qiu & Wen-Jing Gu

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

    Feezan Ahmad

Authors
  1. Mahmood Ahmad
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  2. Xiao-Wei Tang
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  3. Jiang-Nan Qiu
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  4. Feezan Ahmad
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  5. Wen-Jing Gu
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Correspondence to Jiang-Nan Qiu.

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Ahmad, M., Tang, XW., Qiu, JN. et al. A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability: Exploration from historical data. Front. Struct. Civ. Eng. 14, 1476–1491 (2020). https://doi.org/10.1007/s11709-020-0670-z

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