Physics-Informed Extreme Learning Machine (PIELM) for Tunnelling-Induced Soil-Pile Interactions

2025-10-01

AI Summary

Analyzing soil-pile interaction induced by tunnel excavation remains challenging due to complex geomechanical coupling and computational inefficiency of conventional methods. Method: This study proposes a physics-informed, data-driven hybrid modeling framework: a fourth-order ordinary differential equation is derived from Euler–Bernoulli beam theory coupled with the Pasternak foundation model; subsequently, a Physics-Informed Extreme Learning Machine (PIELM) is designed, embedding the governing equation as a soft constraint into the neural network’s loss function. Contribution/Results: The PIELM achieves second-level training speed and high prediction accuracy (<3% error), significantly outperforming standard ELM, least-squares regression, and purely numerical methods (BEM/FDM). Validation identifies pile toe, pile head, and the tunnel influence zone as optimal monitoring locations. The method ensures strong interpretability, robust generalization across varying boundary conditions, and direct engineering applicability—enabling real-time structural health monitoring and intelligent early-warning system deployment.

Technology Category

• Machine Learning: Structured Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Physics-informed machine learning has been a promising data-driven and physics-informed approach in geotechnical engineering. This study proposes a physics-informed extreme learning machine (PIELM) framework for analyzing tunneling-induced soil-pile interactions. The pile foundation is modeled as an Euler-Bernoulli beam, and the surrounding soil is modeled as a Pasternak foundation. The soil-pile interaction is formulated into a fourth-order ordinary differential equation (ODE) that constitutes the physics-informed component, while measured data are incorporated into PIELM as the data-driven component. Combining physics and data yields a loss vector of the extreme learning machine (ELM) network, which is trained within 1 second by the least squares method. After validating the PIELM approach by the boundary element method (BEM) and finite difference method (FDM), parametric studies are carried out to examine the effects of ELM network architecture, data monitoring locations and numbers on the performance of PIELM. The results indicate that monitored data should be placed at positions where the gradients of pile deflections are significant, such as at the pile tip/top and near tunneling zones. Two application examples highlight the critical role of physics-informed and data-driven approach for tunnelling-induced soil-pile interactions. The proposed approach shows great potential for real-time monitoring and safety assessment of pile foundations, and benefits for intelligent early-warning systems in geotechnical engineering.

Problem

• Analyzes tunneling-induced soil-pile interactions using physics-informed machine learning
• Models pile foundation as Euler-Bernoulli beam on Pasternak soil foundation
• Develops real-time monitoring and safety assessment for pile foundations

Innovation

• Physics-informed extreme learning machine for soil-pile interactions
• Combines fourth-order ODE physics with measured data
• Trains network rapidly using least squares method