福德隧道案例中， RMR的6項因子資料經去除重複性後，共計有300筆樣本，以此訓練得到各處理單元間之權重，將全部2019個樣本類神經網路經分類後得到系統輸出值，將其與目標輸出值(即專家分類的支撐型式)進行對照，結果第一正確率(分類結果與原始類別完全相同)達74.39%，第二正確率(分類結果與原始類別差一級)達96.19%。利用新觀音隧道地質報告表挑選出17項因子，共2099筆資料經類神經網路輸入訓練並測試，其結果第一正確率高達99.05%。研究顯示類神經網路架構以兩個隱藏層、隱藏層內處理單元數13個以上、訓練次數5000次以上、慣性因子與學習速率0.5左右，可以得到最佳岩體分類結果。
經由福德隧道與新觀音隧道資料分析顯示： (1) 在未考慮覆蓋因子的情況下，去除洞口段資料有其必要性。 (2)考慮更多的分類因子，類神經網路系統可以有效學習，做出良好的支撐建議。 (3)只要樣本的品質夠好，使用愈多的訓練樣本可以得到愈好的結果，但若是訓練樣本品質不佳，越多的樣本只會增加訓練學習上的混亂。 (4)僅以隧道開挖的前三分之一的資料進行類神經網路訓練，並無法完全預測隧道剩餘三分之二區段的地質狀況，而給予適當支撐建議。必須有賴野外露頭調查與鑽探…等方式，增加對隧道剩餘區段未知地質況狀的掌控。 (5)本類神經網路系統的輸出值是一個隸屬函數，有利於使用者根據隸屬函數做彈性的決策，作成最後支撐型式的決定。在未來可蒐集更多的隧道地質與工程資料來建立類神經網路模型，並配合群集分類與因子分析等方法，來建立更為完善可行的隧道岩體分類系統。

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.

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.