Prediction of Radial Displacement of MRT Shieid Tunnels by Adjacent Construction Using NNLSTM

• Main Authors: Shih-Yuan Liao, 廖仕元
• Other Authors: Min-Yuan Cheng
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/wsy63x

碩士 === 國立臺灣科技大學 === 營建工程系 === 106 === Due to the rapid economic development and dense population in the metropolitan area, the new mass transit system has become a necessary trend, and the rapid development of the MRT and the promotion of local prosperity have led to a large increase in new cases along the MRT; However, the Songshan Formation in the Taipei Basin is composed of soft mud-sand interbeds. The congenital geological environment conditions are not good. The risk of damage to the shield tunnels is relatively high in the adjacent construction. Recently, the radial displacement of the shield tunnels caused by the construction of the Taipei Big Giant near the Bannan Line has exceeded the action. The value and the inclination of part of the orbital surface have caused the disturbance between the Taipei City MRT and the construction company to become the focus of the news and cause social unrest. Therefore, the influence of the adjacent construction on the deformation of the shield tunnel can be an in-depth discussion. At present, the engineering community often uses the numerical analysis software such as FLAC and PLAXIS to analyze the deformation of the shield tunnel affected by adjacent construction. In order to simulate the real engineering situation and simulate the interaction of different interfaces, it is necessary for experienced full-time personnel to join the actual case. Feedback data can ensure the rationality of the analysis results, so it is usually necessary for professionals to operate the requirements, modeling complexity and difficulty. This study attempts to establish a set of prediction modes for the radial displacement of the MRT submarine tunnel caused by adjacent construction by artificial intelligence inference model. Through the literature and SPSS correlation verification, confirm the radial displacement influence factor of the shield tunnel and establish case data. In this study, after using the "NNLSTM" case training and testing, the results show that the error measure of "NNLSTM" is superior to other inference models and has the best predictive ability. The follow-up will use "NNLSTM" as the MRT tunnel path. Inferion mode to displacement. In this study, we use "NNLSTM" to predict the maximum displacement of the maximum shield tunnel from the six observation points and compare the actual values, and use MAPE as the error measure. The research results show that CP4, CP6, CP7, CP9, CP11, etc. 5 The prediction error rate is within 10%, which is accurate prediction. The prediction error rate of CP8 observation point is 14.01%, which is a good prediction. The reason is that NNLSTM can calculate the non-sequential factors and time series factors separately, and the two modes. The output results are integrated, and the final predicted value is combined with the automatic adjustment weight function to improve the actual prediction accuracy. Therefore, "NNLSTM" is applied to the prediction of radial displacement of the shield tunnel, which has the advantages of ease of use, high precision and high reliability. The inference model of this study can be used to predict the radial displacement of the shield tunnel at each stage, and assist the construction management personnel to detect the abnormal situation and early warning reference early, so as to take appropriate treatment measures and effectively control the risk.