台灣的山坡地因長期受風化及地震等地質營力作用使得岩體較破碎且降低原本的強度，若遇豪雨就容易發生滑動，因此台灣常見的岩坡崩滑類型為破碎風化岩體之淺層滑動，而用符合岩體材料性質的Hoek-Brown破壞準則進行岩坡安全性分析較為合適，但仍需要更多的案例研究來探討台灣山坡地之淺層岩體參數。本研究的主要成果為：
一、以台灣常見的多節理風化岩石邊坡之破壞為代表性案例，探討以Hoek-Brown破壞準則為分析核心的有限元素極限分析法(FELA)數值模式模擬結果能符合實際破壞情形。進一步探討Hoek-Brown破壞準則參數對岩坡安全係數的影響，可知岩體擾動係數(D)與地質強度指標(GSI)這兩項參數對該破碎風化岩坡的安全性有明顯的影響；並反推此岩坡案例的GSI值為20~30、D值為0.8~0.9。當缺乏現場實測資料時，此參數可作為該地區類似岩坡安全分析之參考。
二、蒐集颱風豪雨引致的淺層滑動山崩目錄，依本研究研擬的參數組合方式及分析程序，運用機器學習領域之極限學習機(ELM)計算模式對每筆山崩目錄反算岩坡之Hoek-Brown破壞準則參數，再歸納成七種岩性淺層岩體參數組合表，可用於破碎風化岩坡淺層滑動之潛勢評估。
三、以不同的山崩目錄實例來驗證七種岩性淺層岩體參數組合表的可信度。以安全係數未達1.1(破壞機率大於55%)作為認定是否與各山崩目錄相符合之基準下，對不同岩性各岩坡計算其安全係數是否與實際山崩相符，其中砂岩類邊坡的符合率為55%~60%；其他岩性如火山角礫岩、安山岩、砂頁岩互層等邊坡的符合率可達80%以上。綜合各類岩性邊坡的總體符合率為84.9%，確認用此參數表對不同岩性的破碎風化岩坡評估其淺層滑動潛勢並計算安全係數是可行的。
四、透過機器學習結合Hoek-Brown破壞準則之計算模式，對8平方公里測試區內各個邊坡單元計算其淺層滑動安全係數及繪製滑動機率圖並探討影響該圖之因素。研究顯示區域性的岩體擾動係數(D)為受地震力影響之參數，在測試區範圍內變動幅度不大，故對該圖結果的影響程度較小。而GSI與岩石單壓強度(σci)值屬於各邊坡單元的局部特性，受風化作用影響大，故這兩項參數對該圖結果有顯著的影響。應用上可以七種岩性之淺層岩體參數組合表為基礎參考資料，再考量擬出圖的比例尺精度，輔以適量的調查獲得具代表性之GSI與σci值，套用於更細緻的邊坡單元，進而提升製圖的可信度。
五、本研究提出之廣域岩坡安全係數圖製作方式僅適用於較陡破碎風化岩坡之淺層滑動潛勢評估，對於坡度較緩(如低於30度)之土壤邊坡則不適用。故對於陡峭岩坡可用符合Hoek-Brown破壞準則之破碎風化岩坡淺層滑動計算模式，對於較緩的土質邊坡則以Mohr-Coulomb破壞準則計算模式，兩者合而為一可較完整地呈現廣域邊坡安全係數圖。工程師可參考此資訊節省逐一建立邊坡穩定分析模型的時間並獲得初步安全係數參考值，再對廣域邊坡篩選出高風險熱區執行進一步調查與分析，如此可更有效率且經濟地評估廣域邊坡的安全性。

Due to long-term geological forces such as weathering and earthquakes, the rock mass in Taiwan's hillsides is relatively fragmented and the original strength is reduced, and rock slopes slide easily in case of heavy rain event. The common landslide type in Taiwan is shallow debris slide that occurs in fragmented and weathered rock mass, and the Hoek-Brown failure criterion is appropriate in line with such kind of rock mass material properties for analyzing the sliding safety of rock slope. However, more case studies are needed to investigate the parameters of shallow rock mass in Taiwanese hillsides. The main results of this study are:
1. Taking the failure of riched joints weathered rock slope common in Taiwan as a representative case, the numerical model based on the finite element limit analysis (FELA) method with the Hoek-Brown failure criterion as the core of the analysis is discussed, and the simulation results can conform to actual failure situation. The influence of Hoek-Brown failure criterion parameters to rock slope safety is discussed, and realized that the rock mass disturbance coefficient (D) and the geological strength index (GSI) have significant impact on overall stability of fragmented and weathered rock slope. The proper D is 0.8~0.9 and GSI is 20~30 by back analyses for this rock slope failure case. These parameters can be a reference for safety analysis of similar rock slopes around this area when in-situ investigating data is lack.
2. Collecting the massive inventories of shallow debris landslides caused by typhoon and heavy rain, based on the parameters combination procedures established in this study, a machine learning model-extreme learning machine (ELM) is leading-in to back calculate the Hoek-Brown failure criterion parameters for each landslide inventory, then to summarize the shallow rock mass parameters combination table in terms of seven typical lithologies for northern Taiwan hillsides. This table can be applied to assess shallow debris sliding potential for fragmented weathered rock slope.
3. The reliability of the shallow rock mass parameters combination table for seven lithologies is verified by different landslide inventories. Taking factor of safety (FS) less than 1.1 (i.e. failure probability is greater than 55%) as a threshold for determining whether the assessing FS for different lithology rock slope is in line with the landslide inventories. The coincidence rate in sandstone slopes is 55%-60%, in other lithology such as volcanic breccia, andesite, sandstone and shale interbedded slopes can reach more than 80%. The overall coincidence rate among various lithological rock slopes is 84.9%, which confirms that it is feasible to apply this parameter table to assess shallow landslide potential and calculate the FS for fragmented weathered rock slopes with different lithologies.
4. Using the ELM model combined with Hoek-Brown failure criterion, FS of shallow sliding is calculated for each slope unit in a test area about 8 square kilometers, and explores factors that affect the results of the sliding FS probability map. The research shows that the regional D is a parameter affected by seismic force, and the fluctuation range is not large in test area, which has little influence on this map. However, GSI and σci belong to the characteristic parameters of each slope unit which are greatly affected by weathering, so these two parameters have a significant impact on this map. The representative GSI and σci obtained by field investigations in line with appropriate scale precision which increases reliability of this map.
5. The FS and probability map for wide-area rock slope proposed in this study is only applicable to shallow sliding potential of steeper fragmented weathered rock slopes, but not applicable to soil slope with a gentle slope (e.g., less than 30 degrees). Therefore, for steep rock slopes, the shallow sliding assessing ELM model for fragmented weathered rock slopes that conforms to the Hoek-Brown failure criterion should be used. For gentle soil slopes, the Mohr-Coulomb failure criterion model should be used. Geotechnical engineers can refer this information to save time of building slope stability analysis models one by one to obtain the preliminary FS, and also efficiently perform further investigation and analysis on wide-area slopes to screen out high-risk hotspot slopes.

[1]Abdellah, W., Mitri, H.S., Thibodeau, D., & Moreau-Verlaan, L. (2014). Stability of mine development intersections - a probabilistic analysis approach. Canadian Geotechnical Journal, 51(2), 184–195.
[2]Arora, M. K., Das Gupta, A. S., & Gupta, R. P. (2004). An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. International Journal of Remote Sensing, 25(3), 559-572.
[3]Baum, R. L., Savage, W. Z., & Godt, J. W. (2002). TRIGRS -A Fortran Program for Transient Rainfall Infiltration and Grid-based Regional Slope-stability Analysis. U.S. Geological Survey Open-File Report 02-0424.
[4]Barton, N., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock mechanics, 6, 189-236.
[5]Barton, N., & Bar, N. (2015). Introducing the Q-slope method and its intended use within civil and mining engineering projects. In: Schubert W, Kluckner A (eds) Future development of rock mechanics. Proceedings of the ISRM regional symposium, Eurock 2015 and 64th geomechanics colloquium, Salzburg, 7–10, 157–162.
[6]Bar, N., & Barton, N. (2016). Empirical slope design for hard and soft rocks using Q-slope. In: Proceedings of the 50th US rock mechanics/ geomechanics symposium, ARMA 2016, Houston, 26–29 June 2016, ARMA16-384.
[7]Bar, N., & Barton, N. (2017). The Q-Slope Method for Rock Slope Engineering. Rock Mechanics and Rock Engineering, 50, 3307–3322.
[8]Brenning, A. (2005). Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6), 853-862.
[9]Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427-445.
[10]Chu, H. T., Lee, J. C., Bergerat, F., Hu, J. C., Liang, S. H., Lu, C. Y., & Lee, T. Q. (2013). Fracture patterns and their relations to mountain building in a fold-thrust belt: A case study in NW Taiwan. Bulletin de la Société Géologique de France, 184, 485–500.
[11]Ercanoglu, M., & Gokceoglu, C. (2002). Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology, 41, 720-730.
[12]Ercanoglu, M., & Gokceoglu, C. (2004). Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75, 229-250.
[13]Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66, 327-343.
[14]Franklin, J. A. (1975). Size-Strength System for Rock Characterization. Geotechnical Ltd. Department of Earth Sciences, University of Waterloo, Canada.
[15]Gómez, H., & Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78, 11-27.
[16]Gravanis, E., Pantelidis, L., & Griffiths, D.V. (2014). An analytical solution in probabilistic rock slope stability assessment based on random field. International Journal of Rock Mechanics & Mining Sciences, 71, 19–24.
[17]Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1-4), 181-216.
[18]Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. (2006). Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Sciences, 6, 115-131.
[19]Hoek, E., & Brown, E. T. (1980). Empirical Strength Criterion for Rock Masses. Journal of the Geotechnical Engineering Division, GT9, 1013-1035.
[20]Hoek, E., & Bray, J. W. (1981). Rock Slope Engineering. London, England: Institution of Mining and Metallurgy by E&FN SPON Press.
[21]Hoek, E. (1994). Strength of rock and rock masses. ISRM News Journal, 2(2), 4-16.
[22]Hoek, E., Kaiser, P. K., & Bawden, W. F. (1995). Support of underground excavations in hard rock. Rotterdam, Netherlands: A.A. Balkema.
[23]Hoek, E., & Brown, E. T. (1997). Practical Estimates of Rock Mass Strength. International Journal of Rock Mechanics & Mining Sciences, 34(8), 1165-1186.
[24]Hoek, E. (1998). Reliability of Hoek-Brown estimates of rock mass properties and their impact on design. International Journal of Rock Mechanics & Mining Sciences, 35(1), 63–68.
[25]Hoek, E., Read, J., Karzulovic, A., & Chen, Z. Y. (2000). Rock slopes in civil and mining engineering. Proceedings of the International Conference on Geotechnical and Geological Engineering, GeoEng2000, 19-24 November, Melbourne.
[26]Hoek, E., & Marinos, P. G. (2000). Predicting tunnel squeezing problems in weak heterogeneous rock masses. Tunnels and Tunnelling International, 132(11), 45-51.
[27]Hoek, E., Carranza-Torres, C., & Corkum, B. (2002). Hoek-Brown failure criterion-2002 edition. Proc. NARMS-TAC Conference, Toronto, Canada, 1, 267-273.
[28]Hoek, E., & Brown, E. T. (2019). The Hoek-Brown failure criterion and GSI–2018 edition. Journal of Rock Mechanics and Geotechnical Engineering, 11, 445-463.
[29]Huang, C. S., & Liu, H. C. (1988). Explanatory Text of the Geologic Map of Taiwan, Scale 1:50000, Sheet 5, ShuangHsi. Published by Central Geological Survey, Taiwan, July.
[30]Huang, G. B., Chen, Y. Q., & Babri, H. A. (2000). Classification ability of single hidden layer feedforward neural networks. IEEE transactions on neural networks, 11(3), 799-801.
[31]Iverson, R. M. (2000). Landslide Triggering by Rain Infiltration. Water Resource Research, 36(7), 1897-1910.
[32]Koukis, G., & Ziourkas, C. (1991). Slope instability phenomena in Greece: A statistical analysis. Bulletin of the International Association of Engineering Geology, 3, 47-60.
[33]Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85, 347-366.
[34]Keefer, D. K., & Larsen, M. C. (2007). Assessing landslide hazards. Sciences, 316, 1136-1137.
[35]Krabbenhoft, K., Lyamin, A. V., Hjiaj, M., & Sloan, S. W. (2005). A new discontinuous upper bound limit analysis formulation. International Journal for Numerical Methods in Engineering, 63(7), 1069-1088.
[36]Krabbenhoft, K., Lyamin, A. V., & Krabbenhoft, J. (2015). Optum computational engineering (OptumG2). Computer software, Retrieved from https://www.optumce.com
[37]Lacombe, O. (2001). Paleostress magnitudes associated with development of mountain belts: Insights from tectonic analyses of calcite twins in the Taiwan Foothills. Tectonics, 20, 834–849.
[38]Lee, C. T., & Huang, C. M. (2007). Neuro-fuzzy-based landslide susceptibility analysis - an example from Central Western Taiwan. Geophysical Research Abstracts, 9, 06849.
[39]Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental geology, 40(9), 1095-1113.
[40]Lee, S., Ryu, J. H., Min, K., & Won, J. S. (2003a). Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms, 28, 1361-1376.
[41]Lee, S., Ryu, J. H., Lee, M. J., & Won, J. S. (2003b). Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environmental Geology, 4, 820-833.
[42]Lee, S., Ryu, J. H., Won, J. S., & Park, H. J. (2004). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71, 289-302.
[43]Lee, C.T., Huang, C. C., Lee, J. F., Pan, K. L., Lin, M. L., & Dong, J. J. (2008). Statistical approach to storm event-induced landslides susceptibility. Natural Hazards and Earth System Sciences, 8, 941-960.
[44]Li, A. J., Merifield, R. S., & Lyamin, A. V. (2008). Stability charts for rock slopes based on the Hoek-Brown failure criterion. International Journal of Rock Mechanics & Mining Sciences, 45(5), 689-700.
[45]Li, A. J., Merifield, R. S., & Lyamin, A. V. (2011). Effect of rock mass disturbance on the stability of rock slopes using the Hoek-Brown failure criterion. Computers and Geotechnics, 38(4), 546-558.
[46]Li, A. J., Cassidy, M. J., Wang, Y., Merifield, R. S., & Lyamin, A. V. (2012). Parametric Monte Carlo studies of rock slopes based on the Hoek-Brown failure criterion. Computers and Geotechnics, 45, 11-18.
[47]Li, A. J., Khoo, S., Lyamin, A. V., & Wang, Y. (2016). Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Automation in Construction, 65, 42–50.
[48]Li, A. J., Qian, Z., Jiang, J. C., & Lyamin, A. (2019). Seismic slope stability evaluation considering rock mass disturbance varying in the slope. KSCE Journal of Civil Engineering, 23(3), 1043-1054.
[49]Liu, Z. B., Shao, J. F., Xu, W. Y., Chen, H. J., & Zhang, Y. (2014). An extreme learning machine approach for slope stability evaluation and prediction. Natural Hazards, 73, 787–804.
[50]Lim, K., Li, A. J., Schmid, A., & Lyamin, A. (2017). Slope-stability assessments using finite-element limit-analysis methods. International Journal of Geomechanics, 17(2), 06016017.
[51]Lyamin, A. V., & Sloan, S. W. (2002). Upper bound limit analysis using linear finite elements and non‐linear programming. International Journal for Numerical and Analytical Methods in Geomechanics, 26, 181-216.
[52]Lyamin, A. V., & Sloan, S. W. (2002). Lower bound limit analysis using non‐linear programming. International Journal for Numerical Methods in Engineering, 55, 573-611.
[53]Marinos, P., & Hoek, E. (2000). GSI: A geologically friendly tool for rock-mass strength estimation. Proceedings of the International Conference on Geotechnical and Geological Engineering, International Society for Rock Mechanics, Melbourne, Australia, 1422-1446.
[54]Marinos, P., & Hoek, E. (2001). Estimating the geotechnical properties of heterogeneous rock masses such as flysch. Bulletin of Engineering Geology and the Environment, 60, 82–92.
[55]Michalowski Radoslaw, L. (2010). Limit analysis and stability charts for 3D slope failures. Journal of Geotechnical and Geoenvironmental Engineering, 136(4), 583-593.
[56]Marinos, V. (2017). A revised geotechnical classification GSI system for tectonically disturbed rock masses, such as flysch. Bulletin of Engineering Geology and the Environment, 19, 1-14.
[57]Marinos, V., & Carter, T. G. (2018). Maintaining geological reality in application of GSI for design of engineering structures in rock. Journal of Engineering Geology, 239, 282-297.
[58]Pareschi, M. T., Santacroce, R., Sulpizio, R., & Zanchetta, G. (2002). Volcaniclastic debris flows in the Clanio Valley (Campania, Italy): insights for the assessment of hazard potential. Geomorphology, 43(3-4), 219-231.
[59]Qian, Z. G., Li, A. J., Chen, W. C., Lyamin, A. V., & Jiang, J. C. (2019). An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods. Soils and foundations, 59(2), 556-569.
[60]Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A.C., & Peruccacci, S. (2010). Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology, 114, 129-142.
[61]Sari, M. (2009). The stochastic assessment of strength and deformability characteristics for a pyroclastic rock mass. International Journal of Rock Mechanics & Mining Sciences, 46(3), 613–626.
[62]Sidle, R. C., Pearce, A. J., & O'Loughlin, C. L. (1985). Hillslope stability and land use. Water Resources Monograph, 11, 140.
[63]Sloan, S. W. (2013). Geotechnical stability analysis. Géotechnique, 63(7), 531-571.
[64]Sonmez, H., Ulusay, R., & Gokceoglu, C. (1998). A practical procedure for the back analysis of slope failures in closely jointed rock masses. International Journal of Rock Mechanics & Mining Sciences, 35(2), 219-233.
[65]Stevenson, P. C. (1977). An empirical method for the evaluation of relative landslip risk. Bulletin of the International Association of Engineering Geology, 16, 69-72.
[66]Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77-89.
[67]Sun, Z. B., & Qin, C. B. (2014). Stability analysis for natural slope by kinematical approach. Journal of Central South University, 21, 1546-1553.
[68]Taylor, D. W. (1937). Stability of earth slopes. J. Boston Society of Civil Engineers, 24(3), 197-247.
[69]Varnes, D. J. (1978). Slope Movement Types and Processes. In: Schuster, R.L. and Krizek, R.J., Eds., Landslide Analysis and Control, Special Report 176, Transportation Research Board, National Academy of Sciences, Washington DC, 12-33.
[70]Varnes, D. J. (1984). Landslide hazard zonation - a review of principles and practice. IAEG Commission on Landslides, Paris, 63.
[71]Van Westen, C. J., Van Asch, T. W. J., & Soeters, R. (2006). Landslide hazard and risk zonation-why is it still so difficult. Bulletin of Engineering geology and Environment, 65, 167-184.
[72]Wang, H. B., & Sassa, K. (2006). Rainfall-induced landslide hazard assessment using artificial neural networks. Earth Surface Processes and Landforms, 31(2), 235-247.
[73]Xie, M., Esaki, T., & Zhou, G. (2004). GIS-Based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model. Natural Hazards, 33, 265-282.
[74]Yang, X. L., Li, L., & Yin, J. H. (2004). Stability analysis of rock slopes with a modified Hoek-Brown failure criterion. International Journal for Numerical and Analytical Methods in Geomechanics, 28(2), 181–190.
[75]中興工程顧問股份有限公司 (2003)。都會區及周緣坡地整合性環境地質資料庫建置-坡地岩體工程特性調查研究(2/5)。經濟部中央地質調查所報告，第92-16號。
[76]李錫堤、潘國樑、林銘郎 (2003)。山崩調查與危險度評估-山崩潛感分析之研究(1/3)。經濟部中央地質調查所報告，第92-11號。
[77]李錫堤、潘國樑、林銘郎 (2004)。山崩調查與危險度評估-山崩潛感分析之研究(2/3)。經濟部中央地質調查所報告，第93-17號。
[78]李錫堤、潘國樑、林銘郎 (2005)。山崩調查與危險度評估-山崩潛感分析之研究(3/3)。經濟部中央地質調查所報告，第94-18號。
[79]李錫堤 (2009)。山崩及土石流災害分析的方法學回顧與展望。台灣公共工程學刊，5(1)，1-29。
[80]邵國士 (1997)。坪林隧道開挖階段監測與變形行為之研究(碩士論文)。國立中央大學應用地質研究所，桃園縣。
[81]莊緯璉 (2005)。運用判別分析進行山崩潛感分析之研究-以臺灣中部國姓地區為例(碩士論文)。國立中央大學應用地質研究所，桃園縣。
[82]胡逸舟 (2011)。降雨引致山區道路邊坡崩塌潛勢之研究-以阿里山公路為例(博士論文)。國立台灣科技大學營建工程研究所，台北市。
[83]陳嬑璇、譚志豪、陳勉銘、蘇泰維 (2011)。集水區山崩臨界雨量研析。2011中華水土保持學會年會及學術研討會論文集。
[84]陳嬑璇、譚志豪、陳勉銘、蘇泰維 (2013)。降雨誘發山區淺層滑坡之臨界雨量研析。中華水土保持學報，44(1)，87-96。
[85]陳樹群、吳俊毅、謝政道 (2012)。崩塌危害分析模型之建立-以臺北水源特定區為例。中華水土保持學報，43(4)，332-345。
[86]陳建新、陳嬑璇、譚志豪、冀樹勇、蘇泰維 (2013)。台灣西部地區降雨促崩潛勢特性分析。中華水土保持學報，44(2)，179-189。
[87]陳威翰 (2019)。運用人工智慧評估受地震影響之岩石邊坡穩定性及可靠度(碩士論文)。國立台灣科技大學營建工程研究所，台北市。
[88]亞新工程顧問股份有限公司 (2003)。都會區及周緣坡地整合性環境地質資料庫建置計畫-坡地環境地質災害調查研究(2/5)中部地區。經濟部中央地質調查所報告，第92-17 號。
[89]財團法人中興工程顧問社 (2008~2009)。易淹水地區上游集水區地質調查及資料庫建置-集水區水文地質對坡地穩定性影響之調查評估計畫。濟部中央地質調查所研究報告。
[90]張弼超 (2005)。運用羅吉斯迴歸法進行山崩潛感分析-以臺灣中部國姓地區為例(碩士論文)。國立中央大學應用地質研究所，桃園縣。
[91]經濟部中央地質調查所 (2019)。降雨誘發山崩動態警戒模式與調查技術研發應用(1/4)成果報告。
[92]經濟部中央地質調查所 (2020)。降雨誘發山崩動態警戒模式與調查技術研發應用(2/4)成果報告。
[93]廖逸山 (2019)。以現地取樣試體試驗結果探討材料變異性(碩士論文)。國立台灣科技大學營建工程研究所，台北市。
[94]賴忠其 (2020)。運用人工智慧評估受地下水位影響之岩石邊坡穩定性及可靠度(碩士論文)。國立台灣科技大學營建工程研究所，台北市。
[95]鍾明劍、譚志豪、冀樹勇 (2011)。不同尺度分析模式於崩塌潛勢評估之整合應用-以莫拉克颱風事件為例。中興工程季刊，111，47-59。
[96]魏倫瑋、黃韋凱、黃春銘、李璟芳、林聖琪、紀宗吉 (2015)。蘇迪勒颱風於臺灣北部之山崩致災機制初探。中華水土保持學報，46(4)，223-232。