岩體裂隙程度與力學性質之不確定性

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：94

、訪客IP：3.145.33.244

姓名

許哲睿(CHE JUI HSU) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

岩體裂隙程度與力學性質之不確定性
(The uncertainty of fracture intensity and mechanical properties of rock masses)

相關論文

★ 花蓮溪安山岩含量之悲極效應研究	★ 層狀岩盤之承載力
★ 海岸山脈安山岩之鹼-骨材反應特性及抑制方法	★ 集集大地震罹難者居住建築物特性調查分析
★ 岩石三軸室應變量測改進	★ 傾斜互層地層之承載力分析
★ 花蓮溪安山岩骨材之鹼反應行為及抑制方法	★ 混成岩模型試體製作與體積比量測
★ 台灣骨材鹼反應潛能資料庫建置	★ 平台式掃描器在影像擷取及長度量測之應用
★ 溫度及鹽水濃度對壓實膨潤土回脹性質之影響	★ 鹼骨材反應引致之破裂行為
★ 巨觀等向性混成岩製作表面影像與力學性質	★ 膨潤土與花崗岩碎石混合材料之熱傳導係數
★ 邊坡上基礎承載力之數值分析	★ 鹼-骨材反應引致裂縫之量測與分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本文針對不同裂隙程度之巨觀等向性岩體在不同取樣體積下，三維裂隙程度（P32）與力學性質（單壓強度與楊氏模數）之不確定性進行探討。在裂隙方面，首先於岩體模型內，隨機生成裂隙中心點，再計算每一裂隙中心點進入取樣範圍內之機率，此一機率問題為伯努利試驗，透過二項式定理，可推導得P30不確定性之解析解。透過P30 與P32之關係，轉換為P32不確定性之解析解。利用FracMan生成具有不同裂隙程度之離散裂隙網絡（discrete fracture network, DFN），建構岩體模型，再以不同體積取樣，分別計算量測P32，獲得量測P32不確定性之數值解，並解析解進行比較與驗證，當裂隙直徑與取樣邊長之比例小於0.5時，其兩者結果極為相近。
力學方面本文利用FracMan軟體在岩體模型(rock mass model)內生成不同裂隙程度離散裂隙網絡，並進行不同尺寸之取樣，再將裂隙資料導入顆粒流軟體PFC3D，套用平滑節理模型（smooth joint model, SJM）以生成具有之合成岩體（synthetic rock mass, SRM），以位移控制方式，模擬一系列岩體在單壓試驗下之力學行為，計算在不同條件下，合成岩體之單壓強度與楊氏模數之平均值及變異係數。合成岩體力學性質不確定性受到（1）裂隙排列及（2）裂隙程度之影響。Esmaieli等人(2010)利用現地裂隙岩體之P32產生合成岩體，從中取樣進行單壓之數值試驗，以探討力學性質之不確定性，雖然可獲得整體之不確定性。但因每個試體之P32非固定，故無法分離裂隙排列與裂隙程度對岩體力學性質不確定性之貢獻度。本文提出直接法與取樣微調法兩種方法，使試體之P32得以固定。在固定P32的條件下，建立裂隙排列所引致之不確定性。裂隙程度引致之不確定性則可透過力學性質(UCS及E50)與P32之趨勢線及P32變異係數之解析解以求得。本文研究結果顯示：（1）直接法與取樣微調法所求得裂隙排列引致之不確定性結果相近（2）單壓強度之變異係數裂隙程度引致之不確定性與裂隙排列之影響相近（3）楊氏模數之變異係數裂隙程度引致之不確定性皆大於裂隙排列造成之影響（4）合成岩體力學性質之變係數之平方為裂隙排列引致之變異係數與裂隙程度引致之變異係數之平方和，此一關係可由變異數分析(ANOVA)之理論加以驗證。
綜合本文對岩體裂隙程度及力學性質不確定性之研究，可發現裂隙程度之變異係數隨著P32增加而減少，而單壓強度與楊氏模數之變異係數則隨P32增加而增加，且在本文參數研究範圍內，楊氏模數之變異係數皆小於單壓強度之變異係數，岩體表徵單元體積在P32小於1.9 m-1為裂隙程度之變異係數最大，單壓強度次之，楊氏模數最小，在P32大於1.9 m-1為單壓強度之變異係數最大，裂隙程度次之，楊氏模數最小。

摘要(英)

This paper presents the uncertainties of geometrical and mechanical properties based on macroscopically isotropic synthetic rock mass. In the geometrical section, the analytical approach used the probability of whether each fracture center point would be sampled or not. The coefficient of variance of P30 could then be calculated by binomial theorem. According to the relation between P30 and P32, the coefficient of variance of P32 could also be yielded. To verify the analytical solution, FracMan was used to generate the rock mass model and discrete fracture network (DFN) to simulate the measurements and access the coefficient of variance of P32. The analytical solution and numerical solution were very similar when the ratio of the fracture diameter and the length of the sample volume was smaller than 0.5.
In the mechanical section, this paper adopted the concept of synthetic rock mass (SRM). FracMan was used to generate DFN in rock mass models and to execute the sampling. The SRM was generated by combining the fracture data from FracMan and the bonded particle model in PFC3D. The uniaxial compression test, which uses strain control, was conducted to investigate the mechanical behavior of SRM. The uncertainty in the mechanical behavior of SRM is derived from fracture permutation and sampled fracture intensity. In past studies, the effects of these two factors were studied at the same time, but the precise effect of each factor could not be obtained due to the methodology used in these studies. To quantify the effect of fracture permutation, the P32 of each sample must be the same. The direct-generate method and sample-modify method were therefore adopted to ensure that each sample’s P32 were the same. By using the analytical solution of P32 and the relation between P32 and the mechanical property, the effect of the sampled fracture intensity could be calculated. The results showed that the effect of fracture permutation and fracture intensity were almost the same for uniaxial compression strength, and that the effect of fracture intensity was larger than fracture permutation on Young’s modulus. The variance of the mechanical behavior of SRM was also equal to the combination of the variance affected by fracture permutation and sampled fracture intensity. This relationship could also be proven by the theory of analysis of variance.
According to this paper, as P32 increases, the coefficient of variance of fracture intensity will decrease and the coefficient of variance of both uniaxial compression strength and Young’s modulus will increase. In the range of parameters adopted in this paper, when P32 was smaller than 1.8 m-1, the coefficient of variance of fracture intensity was larger than coefficient of variance of uniaxial compression strength, and Young’s modulus was the smallest among them. When P32 was greater than 1.8 m-1, the coefficient of variance of uniaxial compression strength was larger than the coefficient of variance of fracture intensity, and Young’s modulus was the smallest among them.

關鍵字(中)

★ 裂隙程度
★ PFC
★ 離散裂隙網絡
★ 合成岩體

關鍵字(英)

★ fracture intensity
★ PFC
★ DFN
★ SRM

論文目次

目錄
摘要 I
Abstract III
致謝 VI
目錄 VII
圖目錄 IX
表目錄 XIV
第一章、緒論 1
1.1 研究動機 1
1.2 研究方法與目的 3
1.3 論文架構 4
第二章、文獻回顧 5
2.1 表徵單元體積 5
2.2 裂隙含量量測 5
2.3 離散裂隙網絡 10
2.4 合成岩體 11
2.5 岩體之幾何表徵單元體積 14
2.6 岩體之力學表徵單元體積 20
第三章、研究步驟 22
3.1 研究流程 22
3.2 建模步驟 23
第四章、岩體取樣引致裂隙程度之不確定性 30
4.1 裂隙程度不確定性之解析解 30
4.2 裂隙程度不確定性之數值解 34
4.3 案例研究與驗證 38
第五章、裂隙排列引致力學性質之不確定性 41
5.1 裂隙程度之影響 42
5.2 取樣體積之影響 45
5.3 裂隙排列之影響 47
第六章、岩體裂隙程度及力學不確定性之整合探討 68
6.1 各種性質不確定性間之關係 68
6.2 由裂隙程度之不確定性轉換力學性質之不確定性 73
6.3 裂隙排列及程度引致力學性質之不確定性 77
6.4 數值驗證 81
6.5 岩體裂隙程度與力學之表徵單元體積 82
第七章、結論與建議 84
7.1 結論 84
7.2 建議 86

參考文獻

1. 田永銘、盧育辰、許哲睿，「不連續異質性岩體力學性質與幾何特性之變異性(II)」，行政院國家科學委員會專題研究計畫期末報告， MOST103-2221-E-008-057-（2015）。
2. 田永銘、盧育辰、許哲睿，「岩體裂隙成度量測之不確定性」，中華民國力學學會第四十屆全國力學會議，國立交通大學，新竹市 (2016)。
3. 吳宛庭，「三維裂隙網路升尺度方法推估等效參數之差異評估」，國立中央大學，碩士論文 (2016)。
4. 劉明坤，「離散裂隙網路數值模擬：以花蓮溪畔坑道花崗片麻岩體為例」，國立中正大學，碩士論文 (2014)。
5. Anderson, T. W., and Darling, D. A., “Asymptotic theory of certain, “goodness-of-fit" criteria based on stochastic processes". Annals of Mathematical Statistics, Vol. 23, pp. 193–212 (1952).
6. Anderson, T.W., and Darling, D.A., “A Test of Goodness-of-Fit,” Journal of the American Statistical Association. Vol. 49, pp. 765–769 (1954).
7. Cundall, P.A., Pierce, M., and Mas Ivars, D., “Quantifying the size effect of rock mass strength,” Paper presented at the 1st South Hemisphere International Rock Mechanics Symposium, Australia (2008).
8. Cunha, A.P., “Scale effects in rock engineering – An overview of the Loen Workshop and other recent papers concerning scale effects,” Proceedings of the second international workshop on scale effects in rock masses, Lisbon, Portugal, pp. 3-14 (1993).
9. Dershowitz, W.S. “Rock joint system” Ph.D. Dissertation, MIT, Cambridge, Mass (1985).
10. Dershowitz, W.S., “A probabilistic model for the deformability of jointed rock masses,” M.S. Dissertation, MIT, Cambridge, Mass (1979).
11. Dershowitz, W.S., and Einstein, H.H., “Characterizing rock joint geometry with joint system models,” Journal of Rock Mechanics and Rock Engineering, Vol. 21, pp. 21–51 (1988).
12. Dershowitz, W.S., and Herda, H.H., “Interpretation of Fracture Spacing and Intensity,” The 33th U.S. Symposium on Rock Mechanics, Santa Fe, New Mexico (1992).
13. Dershowitz, W.S., Personal communication (2007).
14. Duan, K., and Kwok, C.Y., “Discrete element modeling of anisotropic rock under Brazilian test conditions,” International Journal of Rock Mechanics and Mining Sciences, Vol. 78, pp. 46-56 (2015).
15. Esmaieli, K., Hadjigeorgiou, J., and Grenon, M., “Estimating geometrical and mechanical REV based on synthetic rock mass models at Brunswick mine,” International Journal of Rock Mechanics and Mining Sciences, Vol. 47, pp. 915-926 (2010).
16. Esmaieli, K., Hadjigeorgiou, J., and Grenon, M., “Estimation of synthetic rock mass strength accounting for sample size,” The International Conference on Rock Joints and Jointed Rock Masses, Tucson, AZ (2009).
17. Farichah, Himatul, Hsu, C.J., and Tien, Y.M., “A novel equation to determine geometrical representative elementary volume of fractured rock mass,” 51st US rock mechanics symposium, San Francisco, USA (2017).
18. Farichah, Himatul, Hsu, C.J., and Tien, Y.M., “An equation for determining geometrical representative elementary volume of fractured rock mass,” Taiwan Rock Engineering Symposium, Kaohsiung, Taiwan (2016).
19. Farichah, Himatul, “Representative elementary volume of P32 and hydraulic conductivity of fractured rock mases” National Central University, Master thesis, Taoyuan, Taiwan (2017).
20. Fisher, R. A., “Dispersion on a sphere,” Proc. Roy. Soc. London, Ser. A, 217, pp. 295-305 (1953).
21. Grenon, M., and Hadjigeorgiou, J., “A design methodology for rock slopes susceptible to wedge failure using fracture system modeling,” Engineering Geology, Vol. 96, pp. 78–93 (2008).
22. Grenon, M., and Hadjigeorgiou, J., “Fracture-SG, A fracture system generator software package,” Version 2.17 (2008).
23. Hadjigeorgiou, J., Lessard, J.F., and Flament, F., “Characterizing in-situ block size distribution using a stereological model,” Tunneling Association of Canada Annual Publication (1995).
24. Jaeger, J.C., “Shear fracture of anisotropic rocks,” Geol. Mag., p.p. 65-72 (1960).
25. Kolmogorov A., “Sulla determinazione empirica di una legge di distribuzione,” Sulla determinazione empirica di una legge di distribuzione, pp. 83–91 (1933).
26. Kulatilake, P. H. S. W., Liang, J., and Gao, H., “Experimental and numerical simulations of jointed rock block strength under uniaxial loading,” Journal of Engineering Mechanics-Asce, Vol. 127, pp. 1240-1247 (2001).
27. Lu, Y.C., Tien, Y.M. and Juang, C.H., “Uncertainty of 1D fracture intensity measurements,” Journal of Geophysical Research – Solid Earth (2017, under review).
28. Mas Ivars, D. M., Pierce, M., Darcel, C., Reyes-Montes, J., Potyondy, D. O., Young, R. P., and Cundall, P. A., “The synthetic rock mass approach for jointed rock mass modelling,” International Journal of Rock Mechanics and Mining Sciences, Vol. 48, pp. 219-244 (2011).
29. Mas Ivars, D., Pierce, M., De Gagne, D., and Darcel, C., “Anisotropy and scale dependency in jointed rock mass strength a synthetic rock mass study,” In Proceedings of the First International FLAC/DEM Symposium. Minneapolis, MN (2008).
30. Neuman, S. P. “Stochastic continuum representation of fractured rock permeability as an alternative to the REV and fracture network concepts,” Groundwater Flow and Quality Modelling, Springer, Netherlands, pp. 331-362 (1988).
31. Pan, H.,Wan, J., and Liang, X. “A method for determining representative elementary volume of rock masses,” Proceedings of the 12th International Symposium on Water-Rock Interaction, Kunming, China (2007).
32. Park E.S., Martin C.D., and Christiansson R., “Simulation of the mechanical behavior of discontinuous rock masses using a bonded-particle model,” The 6th North American rock mechanics symposium, Houston, (2004).
33. Pierce, M., Mas Ivars, D., Cundall, P.A. and Potyondy, D.O., “A Synthetic Rock Mass Model for Jointed Rock,” Rock Mechanics: Meeting Society′s Challenges and Demands (1st Canada-U.S. Rock Mechanics Symposium, Vancouver, Canada, pp. 341-349 (2007).
34. Pinto da Cunhafed, A., “Scale Effects in Rock Masses,” National Laboratory for Civil Engineering (LNEC), Lisbon, Portug (1993).
35. Potyondy D.O., and Cundall P.A., “A bonded-particle model for rock,” International Journal of Rock Mechanics and Mining Sciences, Vol. 41, pp. 1329–1364 (2004).
36. Wang, X., “Stereological interpretation of rock fracture traces on borehole walls and other cylindrical surfaces” The Virginia Polytechnic Institute and State University, PhD thesis, Blacksburg, Virginia (2005).
37. Zhang, Q., Zhu, H. H., Zhang, L. Y., and Ding, X. B., “Study of scale effect on intact rock strength using particle flow modeling,” International Journal of Rock Mechanics and Mining Sciences, Vol. 48, pp. 1320-1328 (2011).
38. Zhang, W., Chen, J., Chen, H., Xu, D., and Li, Y., “Determination of RVE with consideration of the spatial effect,” International Journal of Rock Mechanics and Mining Sciences, Vol. 61, pp. 154-160 (2013).

指導教授

田永銘(Yong Ming Tien)

審核日期

2017-10-31

推文