期刊
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
卷 23, 期 6, 页码 711-717出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tust.2008.01.001
关键词
tunnels; seismic prediction; neural networks; geotechnical explorations
This research aims at improving the methods of prediction of hazardous geotechnical structures in the front of a tunnel face. We propose and showcase our methodology using a case study on a water supply system in Cheshmeh Roozieh, Iran. Geotechnical investigations had previously reported three measurements of the newly established method of TSP-203 (Tunnel Seismic Prediction) along 684 in of the 3200 in long tunnel Lip to a depth of 600 m. We use the results of TSP-203 in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between TSP-203 results and those obtained by the methods of Rock Mass Rating classification (RMR - treated here as real values). Our results show that all appropriately trained neural network call reliably predict the weak geological zones in front of a tunnel face accurately. (C) 2008 Elsevier Ltd. All rights reserved.
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