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