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Student Number 88624006
Author Chung-Jen Kao(高仲仁)
Author's Email Address No Public.
Statistics This thesis had been viewed 2211 times. Download 1753 times.
Department Graduate Institute of Applied Geology
Year 2000
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Application of Neural Network to Rock Mass Classification
Date of Defense 2001-07-02
Page Count 87
  • Neural network
  • Rock mass classification
  • support types
  • Tunnel
  • Abstract Rock mass classification system is an important criterion for proposing a support type in rock tunneling. Currently rock mass classification systems used in Taiwan are all come from abroad, and it is necessary to modify these methods for complex geologic condition in Taiwan. In this study, we take the advantages of efficiency and quick learning character of a non-linear and supervised classification problem, and uses BPN (Back Propagation Artificial Neural Network) method to perform the rock mass classification. We collected tunnel engineering reports of the Fu-de and the New Guan-yin tunnels as examples for study. We adopted geologic factors from these reports, and normalize the factors as input level. In the output level of BPN model, we designed a fuzzy membership, so as uncertainty could be considered. We adopt some good samples for BPN learning and the parameters judging, and then use the BPN and rest data for testing the performance of the system.
    In the case of Fu-de tunnel, we use 300 good samples for BPN training and learning and get a good BPN model. We test the rest 2019 samples with the BNP model, and result reveals that 74.39% cases output are exact by the same type of support as the target type and 96.19% cases output support type within one neighboring class of the target type. In the case of Guan-yin, we picked up 17 geologic factors from the engineering reports and summarized 2099 samples for learning and testing in BPN model. Result reveals that the accuracy rate is 99.05% with the suggestion is exactly as the target type. After these two case studies, the best BPN models are two hind layers, the neural units of hind layers are above 13, training more than 5000 times, and moment factor and learning rate are almost closed to 0.5.
    Results from the BPN model of the Fu-de and the New Guan-yin tunnels may conclude: (a) If the overburden factor is exclude for the analysis, it is necessary to remove the test data at portal section of the tunnel. (b) The result in BPN training and testing could be better if we consider more factors for analyses. (c) When the quality of data is good enough, we may use as much data as we have to get the best result. Whereas more data produce more chaotic BPN model, when data quality is bad. (d) BPN model trained from the first 1/3 portion of the tunnel is not good enough to predict the support type and geologic condition of the rest of the tunnel. Field investigation and drilling are necessary for determining the supporting type for the rest potion of tunnel. (e) The fuzzy membership function output from the BPN model can help us making decisions. In the future, we may collect a large number of tunnel geologic and construction data, and establish a more general BPN model. With the assistance of factor analysis and cluster analysis, we may construct a more complete and friendly procedure for tunnel rock mass classification and support prediction.
    Table of Content 第一章、 緒論
    1.1 研究動機與目的
    1.2 文獻回顧
    1.3 研究流程
    第二章、 研究方法
    2.1 類神經網路
    2.2 倒傳遞類神經網路
    2.3 倒傳遞類神經網路演算法
    第三章、 資料收集與整理
    3.1 福德隧道
    3.1.1 資料取得
    3.1.2 資料區分與選取
    3.2 新觀音隧道
    3.2.1 資料取得
    3.2.2 資料區分與選取
    第四章、 類神經網路分析
    4.1 類神經網路資料的處理原則
    4.2 福德隧道案例研究
    4.2.1 網路架構設計
    4.2.2 類神經網路訓練
    4.2.3 結果與討論
    4.3 新觀音隧道案例研究
    4.3.1 網路架構設計
    4.3.2 類神經網路訓練
    4.3.3 結果與討論
    第五章、 討論
    5.1 資料收集與處理
    5.2 類神經網路架構設計及訓練
    5.3 類神經網路輸出及應用
    第六章、 結論與建議
    6.1 結論
    6.2 建議
    Reference 中興工程顧問社(1987)台灣北部區域第二高速公路汐止木柵段初步設計報告,第6-1—6-31頁。
    張 泰(1992)以模糊集合處理岩體品質分類。國立中央大學地球物理研究所碩士論文,共58頁。


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  • Chyi-Tyi Lee(李錫堤)
  • Files
  • 88624006.pdf
  • approve immediately
    Date of Submission 2001-07-02

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