Prediction of liquefaction of gravelly soils based on a cost-sensitive Bayesian network combined with rough set weighting

Abstract

Existing liquefaction prediction approaches for gravelly soils rely on in situ test data, which neglects the in-suit test cost sensitivity. Furthermore, these approaches do not consider the effect of the weights of the factors on the model performance. Therefore, in this paper, a cost-sensitive Bayesian network with rough set theory (CSW-BN) and model hierarchies and factor weighting are combined to address the two aforementioned challenges in research on gravelly soil liquefaction prediction. A two-layer model is constructed using the proposed CSW-BN approach. The first layer of the model reduces the data costs by utilizing conventional soil parameters, without necessitating specific in situ tests such as shear wave velocity tests. In the second layer of the model, variable weights calculated using rough set theory are incorporated into the parameter learning to enhance the generalization of the model. The results show that the learning and prediction accuracies of the new approach are 0.968 and 0.95, respectively. Compared with existing models, the proposed method not only reduces the number of required in situ tests but also improves the prediction accuracy and interpretability. Furthermore, the effectiveness of the CSW-BN model is evaluated using new cases for the 2008 Wenchuan earthquake, with a prediction accuracy of 0.89. Finally, a software tool is developed using Visual Basic for Applications programming to facilitate its application in engineering practice.