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Volume 18, issue 5 | Copyright
Nat. Hazards Earth Syst. Sci., 18, 1451-1468, 2018
https://doi.org/10.5194/nhess-18-1451-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 May 2018

Research article | 29 May 2018

Assessment of liquefaction-induced hazards using Bayesian networks based on standard penetration test data

Xiao-Wei Tang1,2, Xu Bai1,2, Ji-Lei Hu3, and Jiang-Nan Qiu4 Xiao-Wei Tang et al.
  • 1State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
  • 2Institute of Geotechnical Engineering, Dalian University of Technology, Dalian 116024, China
  • 3School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China
  • 4Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China

Abstract. Liquefaction-induced hazards such as sand boils, ground cracks, settlement, and lateral spreading are responsible for considerable damage to engineering structures during major earthquakes. Presently, there is no effective empirical approach that can assess different liquefaction-induced hazards in one model. This is because of the uncertainties and complexity of the factors related to seismic liquefaction and liquefaction-induced hazards. In this study, Bayesian networks (BNs) are used to integrate multiple factors related to seismic liquefaction, sand boils, ground cracks, settlement, and lateral spreading into a model based on standard penetration test data. The constructed BN model can assess four different liquefaction-induced hazards together. In a case study, the BN method outperforms an artificial neural network and Ishihara and Yoshimine's simplified method in terms of accuracy, Brier score, recall, precision, and area under the curve (AUC) of the receiver operating characteristic (ROC). This demonstrates that the BN method is a good alternative tool for the risk assessment of liquefaction-induced hazards. Furthermore, the performance of the BN model in estimating liquefaction-induced hazards in Japan's 2011 Tōhoku earthquake confirms its correctness and reliability compared with the liquefaction potential index approach. The proposed BN model can also predict whether the soil becomes liquefied after an earthquake and can deduce the chain reaction process of liquefaction-induced hazards and perform backward reasoning. The assessment results from the proposed model provide informative guidelines for decision-makers to detect the damage state of a field following liquefaction.

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