Developing new machine-learning intelligent models to predict the excavation-tunnel displacements
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11477-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849226367950913536 |
|---|---|
| author | Abdollah Tabaroei Muhand Jawad Jasim Ali Mohammed Al-Araji Amir Hossein Vakili |
| author_facet | Abdollah Tabaroei Muhand Jawad Jasim Ali Mohammed Al-Araji Amir Hossein Vakili |
| author_sort | Abdollah Tabaroei |
| collection | DOAJ |
| description | 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 $${\delta }_{hrm}$$ , $${\delta }_{vm}$$ , $${\delta }_{htm}$$ and $${\delta }_{vtm}$$ values. Based on 3D FE results, the displacements mechanisms of the excavation-tunnel complex were presented. It was observed that the $$L/B$$ ratio variation have a more effect on the $${\delta }_{hrm}$$ values than $${\delta }_{vm}$$ . Additionally, the $${\delta }_{hrm}$$ values occurs approximately in the middle $$1/3{H}_{w}$$ . 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 $${D}_{h}$$ 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. |
| format | Article |
| id | doaj-art-9aa6907af6cd47cfb22c368940104c5e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9aa6907af6cd47cfb22c368940104c5e2025-08-24T11:25:39ZengNature PortfolioScientific Reports2045-23222025-08-0115112110.1038/s41598-025-11477-xDeveloping new machine-learning intelligent models to predict the excavation-tunnel displacementsAbdollah Tabaroei0Muhand Jawad Jasim1Ali Mohammed Al-Araji2Amir Hossein Vakili3Department of Civil Engineering, Eshragh Institute of Higher EducationTechnical Institute of Babylon, Al-Furat Al-Awsat Technical University (ATU)Department of Civil Techniques, Technical Institute of Babylon, Al-Furat Al-Awsat Technical UniversityDepartment of Civil Environmental Engineering, Faculty of Engineering, Zand Institute of Higher EducationAbstract 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 $${\delta }_{hrm}$$ , $${\delta }_{vm}$$ , $${\delta }_{htm}$$ and $${\delta }_{vtm}$$ values. Based on 3D FE results, the displacements mechanisms of the excavation-tunnel complex were presented. It was observed that the $$L/B$$ ratio variation have a more effect on the $${\delta }_{hrm}$$ values than $${\delta }_{vm}$$ . Additionally, the $${\delta }_{hrm}$$ values occurs approximately in the middle $$1/3{H}_{w}$$ . 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 $${D}_{h}$$ 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.https://doi.org/10.1038/s41598-025-11477-xExcavation-tunnel responseNumerical simulationsTunnel displacementsNew intelligent modelsMachine learning |
| spellingShingle | Abdollah Tabaroei Muhand Jawad Jasim Ali Mohammed Al-Araji Amir Hossein Vakili Developing new machine-learning intelligent models to predict the excavation-tunnel displacements Scientific Reports Excavation-tunnel response Numerical simulations Tunnel displacements New intelligent models Machine learning |
| title | Developing new machine-learning intelligent models to predict the excavation-tunnel displacements |
| title_full | Developing new machine-learning intelligent models to predict the excavation-tunnel displacements |
| title_fullStr | Developing new machine-learning intelligent models to predict the excavation-tunnel displacements |
| title_full_unstemmed | Developing new machine-learning intelligent models to predict the excavation-tunnel displacements |
| title_short | Developing new machine-learning intelligent models to predict the excavation-tunnel displacements |
| title_sort | developing new machine learning intelligent models to predict the excavation tunnel displacements |
| topic | Excavation-tunnel response Numerical simulations Tunnel displacements New intelligent models Machine learning |
| url | https://doi.org/10.1038/s41598-025-11477-x |
| work_keys_str_mv | AT abdollahtabaroei developingnewmachinelearningintelligentmodelstopredicttheexcavationtunneldisplacements AT muhandjawadjasim developingnewmachinelearningintelligentmodelstopredicttheexcavationtunneldisplacements AT alimohammedalaraji developingnewmachinelearningintelligentmodelstopredicttheexcavationtunneldisplacements AT amirhosseinvakili developingnewmachinelearningintelligentmodelstopredicttheexcavationtunneldisplacements |