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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 卿建業(Jianye Ching) | |
dc.contributor.author | Yuan-Hsun Ho | en |
dc.contributor.author | 何元勛 | zh_TW |
dc.date.accessioned | 2021-05-11T04:59:49Z | - |
dc.date.available | 2020-08-07 | |
dc.date.available | 2021-05-11T04:59:49Z | - |
dc.date.copyright | 2019-08-07 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-05 | |
dc.identifier.citation | emdag, S., Gurocak, Z., & Gokceoglu, C. (2015). A simple regression based approach to estimate deformation modulus of rock masses. Journal of African Earth Sciences, 110, 75-80.
Alemdag, S., Gurocak, Z., Cevik, A., Cabalar, A. F., & Gokceoglu, C. (2016). Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Engineering Geology, 203, 70-82. Alvarez, I., J. Niemi, and M. Simpson. (2014). Bayesian inference for a covariance matrix. In Proc., 26th Annual Conf. on Applied Statistics in Agriculture. Manhattan, KS: Kansas State Univ. Barton, N. (2002). Some new Q-value correlations to assist in site characterisation and tunnel design. International Journal of Rock Mechanics and Mining Sciences, 39(2), 185-216. Barton, N., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics, 6(4), 189-236. Beiki, M., Bashari, A., & Majdi, A. (2010). Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. International Journal of Rock Mechanics and Mining Sciences, 47(7), 1091-1103. Bieniawski, Z.T. (1973). Engineering classification of jointed rock masses. Transactions of the South African Institution of Civil Engineers. 15, 335-344. Bieniawski, Z.T. (1976). Rock mass classification in rock engineering. In: Bieniawski, Z.T. (Ed.), Proc. Symp. on Exploration for Rock Eng. vol. 1. Balkema, Cape Town, pp. 97-106. Bieniawski, Z.T. (1989). Engineering Rock Mass Classifications. John Wiley, Rotterdam. Ching, J., & Phoon, K.-K. (2019). Constructing site-specific multivariateprobability distribution model using Bayesian machine learning. Journal of Engineering Mechanics, 145(1), 04018126. Ching, J., Li, K.-H., Phoon, K.-K., & Weng, M.-C. (2018). Generic transformation models for some intact rock properties. Canadian Geotechnical Journal, 55(12), 1702-1741. Ching, J., Lin, G.H., Chen, J.R., and Phoon, K.K. (2017a). Transformation models for effective friction angle and relative density calibrated based on generic database of coarse-grained soils. Canadian Geotechnical Journal, 54(4), 481-501. Chun, B.-S., Lee, Y.-J., Seo, D.-D., & Lim, B.-S. (2006). Correlation deformation modulus by PMT with RMR and rock mass condition. Tunnelling and Underground Space Technology, 21(3-4), 231-232. Clerici, A. (1993). Indirect determination of the modulus of deformation of rock masses - Case histories. Proc. Conf. Eurock '93, pp. 509-517. Deere, D. U. (1964). Technical description of rock cores for engineering purposes. Rock Mechanics and Rock Engineering Geology, 1, 107-116. Galera J. M., Alvarez Z, Bieniawski Z. T. (2005). Evaluation of the deformation modulus of rock masses: comparison between pressure meter and dilatometer tests with RMR predictions. In: Gambin M, Mestat P, Baguelin F (eds) Proceedings of ISP5-PRESSIO 2005. LCPC publication Paris. Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. (2013). Bayesian data analysis. 3rd ed. Boca Raton, FL: Chapman and Hall/CRC. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1(3), 515-534. Ghamgosar M., Fahimifar A., Rasouli V. (2010). Estimation of rock mass deformation modulus from laboratory experiments in Karun dam. In: Zhao, Laboise, Dudt, Mathier (eds) Proceedings of the international symposium of the international society for rock mechanics. Taylor & Francis Group, pp 805-808. Gokceoglu, C., Sonmez, H., & Kayabasi, A. (2003). Predicting the deformation moduli of rock masses. International Journal of Rock Mechanics and Mining Sciences, 40(5), 701-710. Grimstad E., & Barton N. (1993). Updating the Q-System for NMT. In: Proceedings of the international symposium on sprayed concrete-modern use of wet mix sprayed concrete for underground support, Oslo, Norwegian Concrete Association. Hoek, E., & Diederichs, M. S. (2006). Empirical estimation of rock mass modulus. International Journal of Rock Mechanics and Mining Sciences, 43(2), 203-215. Hoek, E., Brown, E.T., (1997). Practical estimates of rock mass strength. International Journal of Rock Mechanics and Mining Science, 34, 1165-1186. Huang, A., & Wand, M. P. (2013). Simple marginally noninformative prior distributions for covariance matrices. Bayesian. Anal. 8(2), 439-452. Isık, N. S., Ulusay, R., & Doyuran, V. (2008). Deformation modulus of heavily jointed–sheared and blocky greywackes by pressuremeter tests: Numerical, experimental and empirical assessments. Engineering Geology, 101(3-4), 269-282. ISRM (1975): Commission on terminology, symbols and graphic representation, International Society for Rock Mechanics (ISRM). James, A. T. (1964). Distributions of matrix variates and latent roots derived from normal samples. The Annals of Mathematical Statistics, 35(2), 475-501. Johnson, N. L. (1949). Systems of frequency curves generated by methods of translation. Biometrika 36(1/2), 149-176. Kayabasi, A., & Gokceoglu, C. (2018). Deformation modulus of rock masses: An assessment of the existing empirical equations. Geotechnical and Geological Engineering, 36(4), 2683-2699. Kayabasi, A., Gokceoglu, C., & Ercanoglu, M. (2003). Estimating the deformation modulus of rock masses: a comparative study. International Journal of Rock Mechanics and Mining Sciences, 40(1), 55-63. Kıncal, C., & Koca, M. Y. (2019). Correlations of in situ modulus of deformation with elastic modulus of intact core specimens and RMR values of andesitic rocks: a case study of the İzmir subway line. Bulletin of Engineering Geology and the Environment. Mardia, K. V., Kent, J. T., & Bibby, J. M. 1979. Multivariate analysis. London: Academic Press. Marinos, P., & Hoek, E., (2000). Estimating the mechanical properties of heterogeneous rock masses such as flysh. (submitted for publication). Mitri HS, Edrissi R, Henning J (1994) Finite element modeling of cable bolted stopes in hard rock ground mines. Presented at the SME annual meeting, New Mexico, Albuquerque pp 94-116. Mohammadi H, Rahmannejad R (2010) The estimation of deformation modulus using regression and artificial neural network analyis. Arabian Journal for Science Engineering, 35, 205-217. Nicholson, G. A., & Bieniawski, Z. T. (1990). A nonlinear deformation modulus based on rock mass classification. International Journal of Mining and Geological Engineering, 8(3), 181-202. Palmström, A., & Singh, R. (2001). The deformation modulus of rock masses — comparisons between in situ tests and indirect estimates. Tunnelling and Underground Space Technology, 16(2), 115-131. Phoon, K.K. (2006). Modeling and simulation of stochastic data. Presented at the Geo Congress 2006, ASCE, Reston, VA. Ramamurthy, T. (2004). A geo-engineering classification for rocks and rock masses. International Journal of Rock Mechanics and Mining Sciences, 41(1), 89-101. Read SAL, Richards LR, Perrin ND (1999) Applicability of the Hoek–Brown failure criterion to New Zealand greywacke rocks. In: Vouille G, Berest P (eds) Proceedings of the nineth international congress on rock mechanics, Paris, August 2, pp 655-660. Serafim, J. L., Pereira, J. P. (1983). Considerations on the geomechanical classification of Bieniawski. In: Proceedings of the symposium on engineering geology and underground openings, Lisboa, Portugal, pp 1133-1144. Sharma, V. M., & Singh, R. B. (1989). Deformability of rock mass. Proc. Conf. Application of rock mechanics in river valley projects, Roorkee, pp. II-7 - II-12. Shen, J., Karakus, M., & Xu, C. (2012). A comparative study for empirical equations in estimating deformation modulus of rock masses. Tunnelling and Underground Space Technology, 32, 245-250. Slifker, J. F., & Shapiro, S. S. (1980). The Johnson system: selection and parameter estimation. Technometrics, 22(2), 239-246. Sonmez, H., Gokceoglu, C., Nefeslioglu, H. A., & Kayabasi, A. (2006). Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences, 43(2), 224-235. Tokuda, T., Goodrich, B., Mechelen, I. V., & Gelman, A. (2011). Visualizing distributions of covariance matrices. Accessed August 3, 2017. Xuecheng, D. (1987). Deformability study of Gezhouba dam foundation rocks. Rock Mechanics and Rock Engineering, 20(2), 95-109. Zhang, L. (2017). Engineering Properties of Rocks. United Kingdom: Butterworth-Heinemann. Zhang, L. (2017). Evaluation of rock mass deformability using empirical methods – A review. Underground Space, 2(1), 1-15. Zhang, L., & Einstein, H. H. (2004). Using RQD to estimate the deformation modulus of rock masses. International Journal of Rock Mechanics and Mining Sciences, 41(2), 337-341. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/702 | - |
dc.description.abstract | 不確定性(uncertainty)普遍存在於大地工程中,在可靠度設計中是一個重要的指標。目前業界使用的安全係數設計法雖然具有方便且迅速的優點,但無法準確量化不確定性因子,可能導致過於保守的設計。因此本研究的目的即是有效利用現場或室內試驗的資訊來預測岩體之變形模數的機率分佈情形,並結合不僅僅單一參數的資訊來降低其不確定性。首先,藉由文獻回顧蒐集前人對岩石經由現地或室內試驗所得之岩石參數資料,建立一龐大的資料庫,篩選出我們認為與岩石變形性有密切關聯的參數,包含:(1)RQD;(2)RMR;(3)Q-System;(4)GSI;(5)完整岩石之楊氏模數(Young's modulus of intact rock, Er);(6)完整岩石之單軸抗壓強度(uniaxial compressive strength of intact rock, σc),並利用這六種參數來預測岩體的變形性參數,包含:(1)岩體之變形模數(deformation modulus of rock mass, Em);(2)岩體之彈性模數(elasticity modulus of rock mass, Ee);(3)岩體之動態模數(dynamic modulus of rock mass, Edyn)。接著,應用貝氏機器學習(Bayesian machine learning)於此資料庫,建立通用的多變數機率分佈模型,並且量化資料庫中的空洞所造成的統計不確定性。根據此多變數機率分佈模型,可以得出參數間的相關性,並且能在不同參數組合的條件下,對岩體變形模數進行預測。當輸入的已知資訊越多,預測參數的不確定性也會隨之縮小,於可靠度設計的架構下,能更準確地設計結構物,適度地節省工程材料成本。 | zh_TW |
dc.description.abstract | Comparing with safety factor method, reliability-based design method can quantify the uncertainty to design geotechnical structure in a more systematical and economical design. In this study, a multivariate probability distribution model for nine parameters of rock is constructed based on the RM/9/5890 database by a Bayesian machine learning method. These nine parameters are: (1) RQD; (2) RMR; (3) Q-System; (4) GSI; (5) deformation modulus of rock mass (Em); (6) elasticity modulus of rock mass (Ee); (7) dynamic modulus of rock mass (Edyn); (8) Young's modulus of intact rock (Er); (9) uniaxial compressive strength of intact rock (σc). This method admits incomplete multivariate data, so it can handle missing data in the database. It can rigorously quantify transformation and statistical uncertainties. From the results, the transformation uncertainty can be effectively reduced as the multivariate site-specific information increases. With smaller uncertainty, reliability-based design can be more economical. | en |
dc.description.provenance | Made available in DSpace on 2021-05-11T04:59:49Z (GMT). No. of bitstreams: 1 ntu-108-R06521120-1.pdf: 8492768 bytes, checksum: fdc4ad7826a6ac9e87462dbcecda1f23 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii ABSTRACT iv 目錄 v 圖目錄 vii 表目錄 ix 第一章 前言 1 1.1 研究背景與動機 1 1.2 研究方法 2 1.3 本文內容 3 第二章 文獻回顧 4 2.1 Classification of Rock Mass 4 2.1.1 Rock Quality Designation (RQD) 4 2.1.2 Rock Mass Rating (RMR) 5 2.1.3 Q-System 7 2.1.4 Geological Strength Index (GSI) 9 2.2 Deformability of Rock Mass 11 2.2.1 Definition 11 2.2.2 In situ tests 12 2.3 Transformation model 15 2.3.1 RQD-Em 15 2.3.2 RMR-Em 16 2.3.3 Q-Em 18 2.3.4 GSI-Em 19 第三章 資料庫 20 3.1 資料庫RM/9/5890 20 3.2 參數間之分佈與相關性 23 3.3 與前人轉換模型之比較 26 3.3.1 RQD-Em 26 3.3.2 RMR-Em 27 3.3.3 Q-Em 28 3.3.4 GSI-Em 29 第四章 多變數機率分佈模型之建置 30 4.1 多變數常態分佈 30 4.2 Johnson分佈系統 32 4.2.1 Johnson分佈系統參數之估計 33 4.3 貝氏分析與吉普斯取樣法 39 4.3.1 貝氏分析 39 4.3.2 吉普斯取樣法 42 4.4 資料模擬 46 4.4.1 模擬結果 46 第五章 參數預測與案例驗證 57 5.1 參數預測 57 5.2 案例驗證 58 5.2.1 案例一 59 5.2.2 案例二 66 第六章 結論與未來建議 73 6.1 結論 73 6.2 未來建議 74 REFERENCE 75 附錄A 資料庫RM/9/5890之基本資訊 80 附錄B 論文口試—問題與答覆 136 | |
dc.language.iso | zh-TW | |
dc.title | 岩體參數間之轉換與相關性探討 | zh_TW |
dc.title | Transformations and correlations among some rock mass parameters | en |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 翁孟嘉(Meng-Chia Weng) | |
dc.contributor.oralexamcommittee | 王瑞斌 | |
dc.subject.keyword | 岩體,變形性,岩體分類法,相關性,轉換模型,多變數機率分佈模型,貝氏機器學習, | zh_TW |
dc.subject.keyword | Rock mass,Deformability,Rock mass classification,Correlation,Transformation model,Multivariate probability distribution model,Bayesian machine learning, | en |
dc.relation.page | 137 | |
dc.identifier.doi | 10.6342/NTU201902455 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2019-08-05 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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