Development of load-temporal model to predict the further mechanical behaviors of tunnel structure under various boundary conditions

Abstract

Prediction of the further mechanical behaviors is vitally important for tunnel engineering to prevent disasters and maintain stability. It is a challenge for most existing researches to couple multiple influence factors. This study aims to develop a novel Load-Temporal (LT) model to predict the further mechanical behaviors of structure using machine learning method, which considers the effect of both historical performance and external loads. As a case study, the developed model is employed in an underwater shield tunnel, in which a Structural Health Monitoring System (SHMS) is installed. Based on the monitoring data obtained from SHMS, plenty of data experiments are conducted to develop model and determine the optimal parameters. Also, the comparison analysis is adopted to indicate the prediction accuracy of proposed model is higher than that of the classical models. The predicted ability of LT model is discussed via experiments of different time scale in further. As promising applications, LT model is used to predict the mechanical behaviors under various boundary conditions, based on which to determine the dangerous states and the structural performance under these conditions.