Data-driven probabilistic seismic demand prediction and sustainability optimization of stone columns for liquefaction mitigation in regional mildly sloping ground

Abstract

With the growing need for efficient mitigation strategies in liquefaction-prone regions, ensuring both seismic resilience and sustainability of infrastructure has become increasingly significant. This paper presents a data-driven probabilistic seismic demand model (PSDM) prediction and sustainability optimization framework to mitigate liquefaction-induced lateral deformation in regional mildly sloping ground improved with stone columns. The framework integrates finite element (FE) simulations with machine learning (ML) models, generating 1,200 ground FE models based on the key site attributes, such as ground inclination, soil properties, and stone column configurations. The performance of the selected ML models is evaluated through hyperparameter tuning by k-fold cross-validation, with the artificial neural network (ANN) outperforming other models in accurately predicting the PSDM. Subsequently, this framework is applied to a set of representative mildly sloping ground sites, enabling rapid PSDM prediction for each site with varying site attributes. Moreover, by incorporating cost and sustainability metrics, multi-objective optimization is performed using the developed ANN predictive model to maximize seismic performance while minimizing total carbon emissions and costs associated with ground improvement. Overall, the framework allows for rapid and accurate PSDM prediction and regional optimization, facilitating the identification of the optimal stone column configurations for efficient and sustainable liquefaction mitigation.