Control of Ground Settlements Caused by EPBS Tunneling Using an Intelligent Predictive Model

Abstract

This paper deals with the control of ground surface settlement due to excavation of shallow tunnels. In order to control the settlement, one should be able to predict it, based on the prediction one may consider required preventions and protections. Prediction of surface settlement depends on several parameters and each parameter has an effect on the other. Application of the traditional methods could become impractical as the proposed equations might have low accuracy. To overcome these limitations, intelligent methods could be implemented. The present study aims to develop an intelligent model for prediction of the surface settlement in Shanghai subway line 2 project using adaptive neuro-fuzzy inference system (ANFIS). The results indicated that the proposed model had an appropriate performance. In order to perform sensitivity analysis of the ANFIS model, cosine amplitude method (CAM) was used and according to the results it was found that the operational, geometric and strength parameters had the highest impacts, respectively. Furthermore, amongst the input parameters, the two parameters of grout filling percentage (n) and grouting pressure (P) were identified as the most effective ones. The values of critical settlement were determined based on Rankin’s criteria of damage risk assessment to control the ground settlement. Then, the corresponding surface settlement was minimized by changing values of the input parameters. According to the results, control of machine operational factors particularly the n and P parameters had a crucial role in reducing surface settlement and preventing pertinent damages.