Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/34963
標題: 分離元素模型於地質材料及邊坡之應用
Discrete Element Modelling of Geomaterials and Slopes
作者: 林明慧
Lin, Ming-Hui
關鍵字: Discrete Element Method
分離元素法
PFC2D
Plackett-Burman design
Central Composite Design
PFC2D
Plackett-Burman design
Central Composite Design
出版社: 水土保持學系所
引用: [1]彭健豪(2008),「九份二山地滑區滑動歷程與堆積行為之研究」,碩士論文,國立台灣大學土木工程研究所。 [2]李宏輝(2008),「砂岩力學行為之微觀參數-以個別元素法探討」,博士論文,國立台灣大學土木工程研究所。 [3]賴丞昶(2009),「地質材料參數最佳化及其於地滑行為分析之應用」,碩士論文,國立中興大學水土保持學系。 [4]蔣志宏(2007),「分離元素法應用於土石流行為之研究」,碩士論文,朝陽科技大學營建工程系。 [5]經濟部中央地質調查所(2006),「山崩調查與危險評估-紅菜坪地滑地監測系統建立與變形機制研究」,經濟部中央地質調查所第95-19號報告。 [6]張家偉(2007),「應用分離元素探討紅菜坪地區地滑演化」,碩士論文,國立台灣大學土木工程研究所。 [7]張家銓(2007),「分離元素法於擬脆性岩材微觀破裂機制之初探」,碩士論文,土木與防災研究所。 [8]Box, G. E. P., and Hunter (1957), “Mutifactor experimental designs for exploring response surface”, Annals of Mathematical Statistics, Vol. 28, pp.195-242. [9]Bardet, J. P. (1997), “Experimental soil mechanics”, Prentice Hall. [10]Cundall, P.A., and Strack, O.D.L. (1979), “A discrete numercal model for granular assemblies”, Geotechnique 1979:29(1):47-65. [11]Fakhimi, A., Carvalho, F., Ishida, T., and Labuz, J.F. (2002), “Simulation of failure around a circular opening in rock”, International Journal of Rock Mechanics & Mining Sciences 39, 507-515. [12]Giani, G. P.(1992), “Rock slop stability analysis”, Balkema, Rotterdam, pp. 207. [13]Hadjigeorgiou, J., Esmaieli, K., and Grenon, M. (2009), “Stability analysis of vertical excavations in hard rock by integrating a fracture system into a PFC model”, Tunnelling and Underground Space Technology 24, 296-308. [14]Itasca Consulting Group Inc.(2004), “Particle Flow Code in 2 Dimensions”, Version 3.1, Minneapolis, MN: ICG. [15]Potyondy, D., and Cundall, P. (2004), “A bonded-particle model for rock”, International Journal of Rock Mechanics & Mining Sciences, Volume41, pp. 1329-1364. [16]Tang, C. L., Hu, J. C., Lin, M. L., Angelier, J., Lu, C. Y., Chan, Y. C., and Chu, H. T. (2009), “ The Tsaoling landslide triggered by the Chi-Chi earthquake, Taiwan: Insights from a discrete element simulation ”, Elsevier B.V Engineering Geology 106, pp. 1-19. [17]Tan, Y., Yang, D., and Sheng, Y. (2009), “Discrete element method (DEM) modeling of fracture and damage in the machining process of polycrystalline SiC”, Journal of the European Ceramic Society 29, pp. 1029-1037. [18]Wang, C., Tannant, D.D., and Lilly, P.A. (2003), “Numerical analysis of the stability of heavily jointed rock slopes using PFC2D”, International Journal of Rock Mechanics & Mining Sciences 40, pp. 415-424. [19]Wang, Y. H., and Leung, S. C. (2008), “A particulate-scale investigation of cemented sand behavior”, Can. Geotech. J. 45: 29-44. [20]Yoon, J. (2007), “Application of experimental design and optimization to PFC model calibration in uniaxial compression simulation”, International Journal of Rock Mechanics & Mining Sciences 44, 871-889.
摘要: 分離元素法(Discrete Element Method, DEM)為近年來新興之數值分析方法,其模擬之材料主要由諸多顆粒組成,可模擬顆粒間相互碰撞堆積等力學行為。本研究針對分離元素法軟體PFC2D之輸入參數(微觀參數),使用實驗設計方法、MATLAB最佳化工具箱以及UCODE分別對砂、岩及岩體進行一系列之模擬與試驗,並由實驗室提供之巨觀參數(E、ν、qu、φ)校正PFC2D之微觀參數。 砂之顆粒與顆粒間僅存在摩擦力,須要決定的微觀參數較少,因此先使用人工調整模型使其巨觀行為逼近實驗值,再將此組參數透過UCODE進行最佳化;結果顯示,UCODE在參數較少時,最佳化的微觀參數表現之巨觀行為與實驗值相當接近。使用parallel bond連接顆粒與顆粒模擬岩石,並藉著PB設計與CCD設計迴歸出巨觀參數與微觀參數之關係式,再透過MATLAB最佳化工具箱找出實驗值所對應之微觀參數;將最佳化之參數輸入PFC2D之單壓與雙軸模型,發現模型計算出之巨觀參數與實驗值很接近。以岩石之校正方法應用於岩體,其實驗值採用Hoek-Brown推估之巨觀參數;結果顯示使用迴歸設計與MATLAB最佳化有十分良好的結果,且UCODE在調整參數較少的情況下,可將因迴歸外插而偏離的參數調整至符合實驗室之值。
Discrete Element Method (DEM) is a numerical method that has been becoming popular in recent years for landslide analysis. It simulates mechanical behavior between particles with micro-parameters. This study calibrates the parameters(micro-parameters) of the DEM software PFC2D by macro-parameters such as Young's modulus (E), Poission ratio (ν), uniaxial compressive strength (qu), and friction angle (φ), using the design of experiments, MATLAB and UCODE for sand, rock, and slope modeling. After the adjustment of the micro-parameters artificially, we use UCODE to make macro-behavior match experimental observation by UCODE. The optimization is quite effective when fewer parameters are involved. The rock model is created by using parallel bonds to connect particles. The relationships of macro and micro-parameters are established by regression equations through PB design and CC design. Then, We use the optimization toolbox of MATLAB to determine the micro-parameters, which can yield macro-behavior as close as possible to laboratory test results. The same methods as for the rock model is applied to a slope model, with the macro-parameters estimated from the Hoek-Brown failure criterion. Overall the experimental design and optimization methods are effective. The optimization process can satisfactorily calibrate the micro-parameters.
URI: http://hdl.handle.net/11455/34963
其他識別: U0005-2607201019371600
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