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

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....

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Bibliographic Details
Published in:Applied sciences 2023-05, Vol.13 (10), p.6074
Main Authors: Oh, Dong-Wook, Kong, Suk-Min, Kim, Su-Bin, Lee, Yong-Joo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Axial stress
Disclaimers
Excavation
Investigations
Learning algorithms
Machine learning
Masonry
Numerical analysis
Performance evaluation
Pile foundations
pile-tunnel interaction
piled raft
Prediction models
Regression analysis
Soil structure
Structural stability
Tunneling
Tunnels
Urban areas
Variables
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13106074