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標題: 地質材料參數最佳化及其於地滑行為分析之應用
The Optimization of Geomaterial Parameters and Its Application in the Analysis of Landslide Behavior
作者: 賴丞昶
Lai, Cheng-Chang
關鍵字: parameter optimization;參數最佳化;Lushan;artificial neural networks;UCODE;廬山;類神經網路;UCODE
出版社: 水土保持學系所
引用: [01] 「地質敏感區災害潛勢評估與監測-重大山崩災害潛勢地區災害模擬與監測(1/4)成果報告書」,經濟部中央地質調查所,中華民國九十七年六月。 [02] 「地質敏感區災害潛勢評估與監測-重大山崩災害潛勢地區災害模擬與監測(2/4)成果報告」,經濟部中央地質調查所,中華民國九十七年十二月。 [03] 「台14線88K至91K 地滑地治理調查規劃工程-成果報告書」,行政院農業委員會水土保持局第三工程所,中華民國九十五年十月。 [04] 「廬山地滑監測及後續治理規劃-期末報告書」,行政院農業委員會水土保持局第三工程所,中華民國九十七年一月。 [05] 葉怡成,「應用類神經網路」,儒林圖書,台北,(2002)。 [06] 張志增、高永濤、張曉平,「邊坡岩體力學參數反分析方法」,北京科技大學學報,第28卷,第12期,Dec.2006。 [07] 汪能君、梧松,「單樁靜載試驗的位移反分析研究」,重慶建築大學學報,第29卷,第2期,Apr.2007。 [08] 李端有、甘孝清,「滑坡體力學參數反分析研究」,長江科學院院報,第22卷,第6期,Dec.2005。 [09] 李端有、李迪、馬山水,「三峽永久船閘開挖邊坡岩體力學參數反分析」,長江科學院院報,1998(2),第10-13頁。 [10] Calvello M, Leonardo Cascini and Giuseppe Sorbino, “A numerical procedure for predicting rainfall-induced movements of active landslides along pre-existing slip surfaces, ” International Journal for Numerical and Analytical Methods in Geomechanics (in press). [11] Calvello M, Finno RJ. “Selecting parameters to optimize in model calibration by inverse analysis, ” Computers and Geotechnics 2004; 31(5):411–425. [12] Finno RJ, Calvello M., “Supported excavations: the observational method and inverse modeling, ” ASCE Journal of Geotechnical and Environmental Engineering 2005;131(7):826–836. [13] Karanagh K, Clough R W., “Finite Element Application in the Characterization of Elastic Solids DJ, ” Solids Structures, 1971 (7): 11-13. [14] Keidser A, Rosjberg D. “A comparison of four inverse approaches to groundwater flow and transport parameter identification, “ Water Resources Research 1991;27(9):2219–2232. [15] K.M. Neaupane, S.H. Achet, “Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya, ” Engineering Geology, 74 (2004) 213–226. [16] Ou CY, Tang YG. “Soil parameter determination for deep excavation analysis by optimization, ” Journal of the Chinese Institute of Engineers 1994; 17(5):671–688. [17] Poeter EP, Hill MC. “Documentation of UCODE, a computer code for universal inverse modeling, “ U.S. Geological Survey Water-Resources Investigations Report 98-4080, 1998. [18] Poeter EP, Hill MC. “Inverse methods: a necessary next step in groundwater modeling, ” Ground Water 1997; 35(2):250–260. [19] Rosenblatt, F., “The Perceptron : A Probabilistic Model for Information Storage and Organization in the Brain, ” Psychological Review, Vol. 65, 1958, pp.386-408. [20] Rumelhart, D. E., Hinton, G. E., and Willams, R. J., “Learning Internal Representations by Error Propagation, ” Parallel Distributed Processing, Chapter 8, MIT Press,1986. [21] SangGi Hwang, Ivy F.Guevarra, ByongOk Yu, “Slope failure prediction using a decision tree: A case of engineered slopes in South Korea ” Engineering Geology, 104 (2009) 126–134. [22] Th.W.J. van Asch, L.P.H. Van Beek, T.A. Bogaard, “Problems in predicting the mobility of slow-moving landslides ” Engineering Geology, 91 (2007) 46–55.

Numerical analysis takes advantage of the fast calculation and convenience of computers in solving complicated engineering problems. It is down with the setup of a geological model, boundary conditions, and material parameters, numerical results closely depend on the material parameters, which are not easy to determine, however. While they are often subjectively decided by experience or from literature, objective methods should be used to find representative parameters to render more accurate numerical results. This study uses observation data, the program UCODE, and artificial neural networks (ANN) to obtain optimum parameters. The UCODE is based on statistics, and the artificial neural networks are capable of prediction, classification and optimization. In particular, the back-propagation network is suitable for the problems of optimization. In addition to the UCODE and ANN, parameters are adjusted artificially for comparison. In the analysis of the triaxial compression test, the value of the parameter E50ref is raised from the initial one after the calibration by artificial method and the UCODE, while it is dropped by the ANN method. The parameter m varies around 0.9. The parameter Φ is all raised from initial values after the calibration of the three methods, and it has the greatest influence on the analysis. In the analysis of the Lushan landslide, the values of the parameters C and Φ are all raised from initial values by the three methods. Similarly, the parameter Φ has the greatest influence on the numerical results. The UCODE and ANN methods show their ability to optimize the parameters and improve the numerical results in two cases of the triaxial compression test and the Lushan landslide. In the Lushan case, further improvement of the numerical results may require better geological and constitutive models.
其他識別: U0005-1708200917581600
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