Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines
نویسندگان
چکیده
Liquefaction has been responsible for several earthquake-related hazards in the past. An earthquake may cause liquefaction saturated granular soils, which might lead to massive consequences. The ability accurately anticipate soil potential is thus critical, particularly context of civil engineering project planning. Support vector machines (SVMs) and Bayesian optimization (BO), a well-known method, were used this work forecast potential. Before development BOSVM model, an evolutionary random forest (ERF) model was input selection. From among nine candidate inputs, ERF selected six, including water table, effective vertical stress, peak acceleration at ground surface, measured CPT tip resistance, cyclic stress ratio (CSR), mean grain size, as most important ones predict liquefaction. After developed using six performance evaluated renowned criteria, accuracy (%), receiver operating characteristic (ROC) curve, area under ROC curve (AUC). In addition, compared with standard SVM other machine learning models. results showed that outperformed achieved 96.4% 95.8% AUC 0.93 0.98 training testing phases, respectively. Our research suggests viable alternative conventional prediction methods. findings show BO method successful model.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2022
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su141911944