Developing new machine-learning intelligent models to predict the excavation-tunnel displacements - Abdollah Tabaroei; Muhand Jawad Jasim; Ali Mohammed Al-Araji; Amir Hossein Vakili (Scientific Reports)
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 $${δ }_{hrm}$$ , $${δ }_{vm}$$ , $${δ }_{htm}$$ and $${δ }_{vtm}$$ 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 $${δ }_{hrm}$$ values than $${δ }_{vm}$$ . Additionally, the $${δ }_{hrm}$$ values occurs approximately in the middle $$1/3H_{w}$$ . 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.