Modeling the Impact of Soil Liquefaction on Structural Stability Using an Artificial Neural Network Optimized by NSGA-III

Soil liquefaction is one of the most critical geotechnical phenomena that can severely compromise the stability and performance of engineering structures during seismic events. Accurate prediction of liquefaction potential and its subsequent effects on structural stability remains a complex and nonlinear problem influenced by multiple interdependent soil and seismic parameters. In this study, an Artificial Neural Network (ANN) model is developed to model and predict the impact of soil liquefaction on structural stability using a comprehensive set of geotechnical and seismic input features, including groundwater depth, shear wave velocity (Vs30), standard penetration test (SPT) results, and peak ground acceleration (PGA). To enhance the predictive performance and generalization capability of the ANN, its hyperparameters and network architecture are optimized through the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which allows for simultaneous optimization of multiple conflicting objectives such as prediction accuracy and model complexity. The optimized ANN demonstrates superior performance in classifying liquefaction and non-liquefaction cases, achieving high accuracy and robustness across validation datasets. Moreover, the proposed hybrid NSGA-III–ANN framework provides a reliable and efficient computational approach for evaluating the influence of liquefaction on structural stability, offering valuable insights for seismic design, risk assessment, and mitigation strategies in geotechnical engineering.