Developing new machine-learning intelligent models to predict the excavation-tunnel displacements

With the increase of urban development in big cities, the requirement for deep excavations to build the tall building foundations of has increased significantly. Depending on the dimension and location, these excavations can have an important effect on underground tunnels, especially subway tunnels. In order to get a better realization of the behavior of an existing tunnel due to a vicinity deep excavation, this research that consists of three main parts, propose new intelligent models for predicting the excavation-tunnel displacements using machine learning. For the purpose four equations present to predict displacements of the excavation-tunnel complex. In the first step, a three-dimensional (3D) finite-element (FE) model validate against case stories. In the second step, a number of three-hundred and sixty 3D simulations of an existing tunnel located directly beneath an excavation under different parameters such as excavation geometry and tunnel positions beneath the excavation were carried out. Finally in the third part, based on the simulation results two models developed for predict and validate the [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] values. Based on 3D FE results, the displacements mechanisms of the excavation-tunnel complex were presented. It was observed that the [Formula: see text] ratio variation have a more effect on the [Formula: see text] values than [Formula: see text]. Additionally, the [Formula: see text] values occurs approximately in the middle [Formula: see text]. The results demonstrate that when the tunnel located at very close beneath the excavation area, tunnel tends to move vertically towards the excavation area. As [Formula: see text] value increases, the vertical displacement values of the tunnel decrease. The proposed models validated against FE results the results show that the models has an acceptable performance in estimating the of excavation and tunnel displacements.

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Publication Details

Journal
Scientific Reports
Published
2025-08-22
DOI
https://doi.org/10.1038/s41598-025-11477-x
Citations
3
Primary Topic
Geotechnical Engineering and Analysis
Type
article
Field-Weighted Citation Impact
3.91
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article

Developing new machine-learning intelligent models to predict the excavation-tunnel displacements

Amir Hossein Vakili, Ali Mohammed Ali Al-Araji, Abdollah Tabaroei, Muhand Jawad Jasim
3 citations
Scientific Reports
Geotechnical Engineering and Analysis
3.91
article

Developing new machine-learning intelligent models to predict the excavation-tunnel displacements

Amir Hossein Vakili, Ali Mohammed Ali Al-Araji, Abdollah Tabaroei, Muhand Jawad Jasim
article en
3 citations

Abstract

With the increase of urban development in big cities, the requirement for deep excavations to build the tall building foundations of has increased significantly. Depending on the dimension and location, these excavations can have an important effect on underground tunnels, especially subway tunnels. In order to get a better realization of the behavior of an existing tunnel due to a vicinity deep excavation, this research that consists of three main parts, propose new intelligent models for predicting the excavation-tunnel displacements using machine learning. For the purpose four equations present to predict displacements of the excavation-tunnel complex. In the first step, a three-dimensional (3D) finite-element (FE) model validate against case stories. In the second step, a number of three-hundred and sixty 3D simulations of an existing tunnel located directly beneath an excavation under different parameters such as excavation geometry and tunnel positions beneath the excavation were carried out. Finally in the third part, based on the simulation results two models developed for predict and validate the [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] values. Based on 3D FE results, the displacements mechanisms of the excavation-tunnel complex were presented. It was observed that the [Formula: see text] ratio variation have a more effect on the [Formula: see text] values than [Formula: see text]. Additionally, the [Formula: see text] values occurs approximately in the middle [Formula: see text]. The results demonstrate that when the tunnel located at very close beneath the excavation area, tunnel tends to move vertically towards the excavation area. As [Formula: see text] value increases, the vertical displacement values of the tunnel decrease. The proposed models validated against FE results the results show that the models has an acceptable performance in estimating the of excavation and tunnel displacements.

Scientific ReportsVol. 15(1)
Sadra Institute Of Higher Education (IR), Tabaran Institute of Higher Education (IR), Al-Furat Al-Awsat Technical University (IQ)
Sustainable cities and communities
Openalex Percentile: Top 7%
Geotechnical Engineering and Analysis
3.91
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