Prediction and Analysis of Axial Stress of Piles for Piled Raft Due to Adjacent Tunneling Using Explainable AI

Abstract

Tunneling, especially in urban areas, affects many structures on the ground, which directly influences the usability and stability of the structures. The settlement of and axial stress on the pile foundation are important factors that determine the behavioral characteristics of the pile foundation. Therefore, this study uses numerical analysis and machine learning to derive a prediction model of pile axial stress due to tunnel excavation adjacent to the piled raft. Numerical analysis data were utilized for machine learning purposes, and the effects of the input data on the prediction model were scrutinized. The numerical analysis revealed that the change in the pile axial stress resulting from tunnel excavation differed depending on the pile’s location, with the greatest axial stress reduction occurring in the center of the piled raft. Furthermore, the rate of reduction was higher in soils with lower relative densities. Several algorithms were employed to derive the prediction model, with tree-based algorithms displaying notable performance in predicting pile axial stress. Additionally, preprocessing the data with appropriate feature engineering techniques exhibited superior predictive power, and incorporating settlement data aided in enhancing the prediction model’s performance.