Minimum principle and related numerical scheme for simulating initial flow and subsequent propagation of liquefied ground

Montassar, Sami; Buhan, Patrick de

doi:10.1002/nag.443

Cited by 10 publications

(1 citation statement)

References 29 publications

(32 reference statements)

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“…Seismic liquefaction of saturated soils, which occurs as a result of earthquakes, is one of geotechnical engineers' most pressing problems. This is because the lateral expansion of soil mass might represent a significant hazard to civil engineering works in the area if it occurs [1][2][3]. As an example, after the Wenchuan earthquake of M 8.0 which struck China in 2008, both surface buildings and subsurface utilities were damaged by liquefaction [1,2,4].…”

Section: Introductionmentioning

confidence: 99%

Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines

Sabri

Al-Bahrani

et al. 2022

Sustainability

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Liquefaction has been responsible for several earthquake-related hazards in the past. An earthquake may cause liquefaction in saturated granular soils, which might lead to massive consequences. The ability to accurately anticipate soil liquefaction potential is thus critical, particularly in the context of civil engineering project planning. Support vector machines (SVMs) and Bayesian optimization (BO), a well-known optimization method, were used in this work to accurately forecast soil liquefaction potential. Before the development of the BOSVM model, an evolutionary random forest (ERF) model was used for input selection. From among the nine candidate inputs, the ERF selected six, including water table, effective vertical stress, peak acceleration at the ground surface, measured CPT tip resistance, cyclic stress ratio (CSR), and mean grain size, as the most important ones to predict the soil liquefaction. After the BOSVM model was developed using the six selected inputs, the performance of this model was evaluated using renowned performance criteria, including accuracy (%), receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). In addition, the performance of this model was compared with a standard SVM model and other machine learning models. The results of the BOSVM model showed that this model outperformed other models. The BOSVM model achieved an accuracy of 96.4% and 95.8% and an AUC of 0.93 and 0.98 for the training and testing phases, respectively. Our research suggests that BOSVM is a viable alternative to conventional soil liquefaction prediction methods. In addition, the findings of this research show that the BO method is successful in training the SVM model.

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