Title page for 88624006


[Back to Results | New Search]

Student Number 88624006
Author Chung-Jen Kao(高仲仁)
Author's Email Address No Public.
Statistics This thesis had been viewed 2265 times. Download 1801 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
Keyword
  • 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。
    王錦洋(1998)長大鐵路隧道工程-新觀音隧道之施工。工程,第71卷,第12期,第14—27頁。
    余旗文、陳錦清(2000)倒傳遞類神經網路於隧道支撐設計之應用。岩盤工程研討會論文集,第223—232頁。
    李榮松,莊文任(1997)岩體分類隧道支撐設計法之分析與探討。財團法人中興工程顧問社專案研究報告,共125頁。
    洪如江(1993)初等工程地質學大綱。財團法人地工技術研究發展基金會,共258頁。
    高仲仁、鄭錦桐、李錫堤(1999)類神經網路在岩體分類上的應用。第八屆台灣地球物理研討會暨八十九年度中國地球物理學會年會論文集,第654—658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。
    張吉佐、李民政(1997)北二高隧道設計與施工。工程,第70卷,第4期,第8—21頁。
    張淑玲(1998)應用類神經網路建立隧道安全預警之經驗評估模式(以三義隧道南口工作面為例)。台灣科技大學碩士論文,共112頁。

    曾于修(1994)群集分析法在岩體分類上之應用。國立中央大學應用地質研究所碩士論文,共102頁。

    葉怡成(1993)類神經網路模式與運作。儒林圖書股份有限公司,第69—110頁。
    詹君治、冀樹勇、陳錦清(2000)類神經網路於深開挖壁體變形之預測。中興工程,第六十九期,第21—38頁。
    蔡紹陽、許健宏(1997)北迴線鐵路新觀音隧道選線與規劃過程。工程,第70卷,第7期,第12—25頁。
    鄭錦桐、李錫堤(1996)運用類神經網路做岩體分類。中國地質學會八十五年年會大會手冊及論文摘要,第258—262頁。
    Adeli, H., and Yeh, C. (1989) Perceptron Learing in Engineering Design: Microcomputers in Civil Engineering, Vol. 4, p.247-256.
    Barton, N. (1987) Rock Mass Classification and Tunnel Reinforcement Selection using the Q-system: ASTM Special Technical Publication = American Society for Testing and Materials Special Technical Publication, Vol. 984. p. 59-84.
    Barton, N., Lien R., and Lunde. J. (1974) Engineering Classification of Rock Masses for the Design of Tunnel Support: Rock Mechanics, Vol. 6, p. 183-236.
    Bieniawski, Z. T. (1974) Geomechanics Classification of Rock Masses and its Application in Tunneling: Proc. 3rd Cong. Intl. Soc. Rock Mech., Denver, Vol. 2A, p.27-32.
    Bieniawski, Z. T. (1979) The Geomechaics Classification in rock Engineering Application: Proc. Intl. Cong. Rock Mech. Montreux 2, p.40-48.
    Bieniawski, Z. T. (1984) Rock Mechanics Design in Mining and Tunneling: Balkema, Rotterdam, 209p.
    Bieniawski, Z. T. (1989) Engineering Rock Mass Classification: JOHN Wiely & ons, New York, 251p.
    Bieniawski, Z. T. (1993) Classification of Rock Masses for Engineering: The RMR System and Future Trends: In Comprehensive Rock Engineering (edited by Hudson J. A.), Vol.3, p.553-573.
    Carranza-Torres C., and Fairhurst C. (2000) Application of Convergence-Confinement Method of Tunnel Design to Rock Masses That Satisfy the Hoek-Brown Failure Criterion: Tunneling and Underground Space Technology, Vol. 15, No. 2, p. 187-213.
    Lee C. A., and Sterling R. L. (1992) Identifying probable failure modes for underground openings using a neural network: International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, Vol. 29, No. 1, p. 49-67.
    Deere, D. U. and Deere, D. W. (1987) The Rock Quality Designation (RQD) index in practice: ASTM Special Technical Publication = American Society for Testing and Materials Special Technical Publication, Vol. 984. p. 91-101.
    Deere, D. U., Hendron, A. J., Patton, F. D., and Cording, E. J. (1967) Design of Surface and Near Surface Construction in Rock: Proceedings 8th U.S. Symposium Rock Mechanics, American Institute of Mining, Metallurgical and Petroleum Engineers, New York, p.237-302.
    Huang, Y., and Waenstedt, S. (1998) The introduction of neural network system and its application in rock engineering: Engineering Geology, Vol. 49, No. 3-4, p. 253-260.
    I.S.R.M. (1981) Suggested Methods for the Quantitative Descriptions in Rock Masses, Lisbon.
    Rabcewicz, L. (1964) The New Austrian Tunnelling Method: Water Power, Nov. 1964, p. 453-457.
    Sterling R. L., and Lee C. A. (1992) A neural network — Expert system hybrid approach for tunnel design: Proceedings-Symposium on Rock Mechanics, Vol.33, p.501-510.
    Tapia, M. A., Valverde, M. A., Amadei, B., and Madrigal, H. (1998) The REX Expert System: A New Alternative for Rock Excavation Design” International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, Vol. 35, No. 4-5, Paper No. 23.
    Advisor
  • Chyi-Tyi Lee(李錫堤)
  • Files
  • 88624006.pdf
  • approve immediately
    Date of Submission 2001-07-02

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have dissertation-related questions, please contact with the NCU library extension service section.
    Our service phone is (03)422-7151 Ext. 57407,E-mail is also welcomed.