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Developing new machine-learning intelligent models to predict the excavation-tunnel displacements.
Abdollah Tabaroei1, Muhand Jawad Jasim2, Ali Mohammed Al-Araji3
1Department of Civil Engineering, Eshragh Institute of Higher Education, Bojnourd, Iran. a.tabaroei@eshragh.ac.ir.
Scientific Reports
|August 22, 2025
Summary
New machine learning models predict tunnel displacements from deep urban excavations. These models help assess risks to underground infrastructure like subway tunnels during construction.
Area of Science:
- Geotechnical Engineering
- Computational Mechanics
- Machine Learning Applications
Background:
- Urban development necessitates deep excavations for foundations, posing risks to nearby underground structures.
- Subway tunnels are particularly vulnerable to displacements caused by adjacent deep excavations.
Purpose of the Study:
- To develop intelligent models for predicting excavation-induced tunnel displacements.
- To analyze the impact of excavation geometry and tunnel position on tunnel behavior.
Main Methods:
- Validated a three-dimensional (3D) finite-element (FE) model against case studies.
- Conducted 360 3D FE simulations varying excavation and tunnel parameters.
- Developed two machine learning models to predict specific displacement values.
Main Results:
- Displacement mechanisms of the excavation-tunnel complex were identified.
- Excavation geometry significantly influences tunnel displacements.
- Tunnel proximity to excavation impacts vertical displacement, with closer proximity causing downward movement.
Conclusions:
- The developed machine learning models accurately predict excavation and tunnel displacements.
- Findings provide insights for mitigating risks to tunnels from deep excavations.
- Understanding displacement mechanisms is crucial for safe urban construction.
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