Accurate and Generalizable Soil Liquefaction Prediction Model Based on the CatBoost Algorithm

Abstract

Accurate prediction of soil liquefaction is important for preventing geological disasters. Soil liquefaction prediction models based on machine learning algorithms are efficient and accurate; however, the generalizability of some models is weak and they fail to achieve highly precise soil liquefaction predictions in certain areas, which limits the applicability of these models. Thus, a soil liquefaction prediction model was constructed using the CatBoost (CB) algorithm to support categorical features. The model was trained using standard liquefaction datasets from domestic and foreign sources and was optimized with Optuna hyperparameters. Additionally, the model was evaluated using five evaluation metrics and its performance was compared to that of other models that use multi-layer perceptron and support vector machine algorithms. Finally, the prediction capability of the model was verified by a case study. The experimental results demonstrated that the CB-based model generated more accurate soil liquefaction predictions than other comparison models and maintained their performance. Hence, the proposed model accurately predicts soil liquefaction and offers strong generalizability, demonstrating potential to contribute toward the prevention and control of soil liquefaction in engineering projects, and toward ensuring the safety and stability of structures built on or near liquefiable soils.