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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 葛宇甯(Louis Ge) | |
| dc.contributor.author | Ying Zheng | en |
| dc.contributor.author | 鄭瀅 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:53:36Z | - |
| dc.date.copyright | 2022-08-10 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-07-29 | |
| dc.identifier.citation | 參考文獻 [1]Abrmowitz, M. and Stegun, I.A. (1970) Handbook of Mathematical Functions: With Formulas, Graphs and Mathematical Tables. National Bureau of Standards, Washington DC. [2]Ahmed A, Soubra A H. Probabilistic analysis at the serviceability limit state of two neighboring strip footings resting on a spatially random soil[J]. Structural Safety, 2014, 49: 2-9. [3]Al-Naqshabandy MS, Bergman N , Larsson S .Strength variability in lime-cement columns based on cone penetration test data[J]. Proceedings of the Institution of Civil Engineers Ground Improvement, 2012, 165(GI1):15-30. [4]Beck J L, Au S K. Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation[J]. Journal of engineering mechanics, 2002, 128(4): 380-391. [5]Bennett R J. Spatial time series: Analysis-forecasting-control[M]. Pion, 1979. [6]Betz W, Papaioannou I, Straub D. Transitional Markov chain Monte Carlo: observations and improvements[J]. Journal of Engineering Mechanics, 2016, 142(5): 04016016. [7]Bong T, Stuedlein A W. Effect of cone penetration conditioning on random field model parameters and impact of spatial variability on liquefaction-induced differential settlements[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2018, 144(5): 04018018. [8]Box, G. and Jenkins, G. (1970) Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco. [9]Breysse D, Niandou H, Elachachi S, Houy L. A generic approach to soil-structure interaction considering the effects of soil heterogeneity. Geotechnique 2005;55(2):143–50. [10]Brockwell, P.J. and Davis, R.A. (1987) Time Series: Theory and Methods. Springer-Verlag, New York. [11]Cami B, Javankhoshdel S, Phoon K K, et al. Scale of fluctuation for spatially varying soils: estimation methods and values[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2020, 6(4): 03120002. [12]Chang Y C, Ching J, Phoon K K, et al. On the Hole Effect in Soil Spatial Variability[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2021, 7(4): 04021039. [13]Ching J, Chen Y C. Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging[J]. Journal of engineering mechanics, 2007, 133(7): 816-832. [14]Ching J, Phoon KK. Impact of auto-correlation function model on the probability of failure. ASCE J Eng Mech 2018;145(1):04018123. [15]Ching J, Phoon KK, Beck JL, Huang Y. On the identification of geotechnical sitespecific trend function. ASCE-ASME J Risk Uncertainty Eng Syst Part A Civ Eng 2017; 3(4):04017021. [16]Ching J, Schweckendiek T. State-of-the-art review of inherent variability and uncertainty in geotechnical properties and models[J]. ISSMGE Technical Committee, 2021, 304. [17]Ching J, Wu TJ, Stuedlein AW, Bong T. Estimating horizontal scale of fluctuation with limited CPT soundings. Geosci Front 2018;9(6):1597–608. [18]Ching J, Phoon K K. Constructing site-specific multivariate probability distribution model using Bayesian machine learning[J]. Journal of Engineering Mechanics, 2019, 145(1): 04018126. [19]Cressie N. The origins of kriging[J]. Mathematical geology, 1990, 22(3): 239-252. [20]Dasaka S M, Zhang L M. Spatial variability of in situ weathered soil[J]. Géotechnique, 2012, 62(5): 375-384. [21]DeGroot D J, Baecher G B. Estimating autocovariance of in-situ soil properties[J]. Journal of Geotechnical Engineering, 1993, 119(1): 147-166. [22]DeGroot, M.H. (1970) Optimal Statistical Decisions. McGraw-Hill, Inc., New York. [23]Fenton G A. Random field modeling of CPT data[J]. Journal of geotechnical and geoenvironmental engineering, 1999, 125(6): 486-498. [24]Firouzianbandpey S, Griffiths D V, Ibsen L B, et al. Spatial correlation length of normalized cone data in sand: case study in the north of Denmark[J]. Canadian Geotechnical Journal, 2014, 51(8): 844-857. [25]Gneiting T, Genton M G, Guttorp P. Geostatistical space-time models, stationarity, separability, and full symmetry[J]. Monographs On Statistics and Applied Probability, 2006, 107: 151. [26]Hastings W K. Monte Carlo sampling methods using Markov chains and their applications[J]. 1970. [27]Hristopulos D T, Žukovič M. Relationships between correlation lengths and integral scales for covariance models with more than two parameters[J]. Stochastic Environmental Research and Risk Assessment, 2011, 25(1): 11-19. [28]J. A. Nelder, R. Mead, A Simplex Method for Function Minimization, The Computer Journal, Volume 7, Issue 4, January 1965, Pages 308–313. [29]Jaksa, M., 1995. The Influence of Spatial Variability on the Geotechnical Design Properties of a Stiff, Overconsolidated Clay. Ph.D. Dissertation, University of Adelaide, Australia. [30]Journel, A.G. and Huijbregts, C.J. (1978) Mining Geostatistics. Academic Press, London, 600 p. [31]Krige D G. A statistical approach to some basic mine valuation problems on the Witwatersrand[J]. Journal of the Southern African Institute of Mining and Metallurgy, 1951, 52(6): 119-139. [32]Liu W F, Leung Y F, Lo M K. Integrated framework for characterization of spatial variability of geological profiles[J]. Canadian Geotechnical Journal, 2017, 54(1): 47-58. [33]Lloret-Cabot M, Fenton G A, Hicks M A. On the estimation of scale of fluctuation in geostatistics[J]. Georisk: Assessment and management of risk for engineered systems and geohazards, 2014, 8(2): 129-140. [34]Lukiantchuki J A, Bernardes G P, Esquivel E R. Energy ratio (ER) for the standard penetration test based on measured field tests[J]. Soils and Rocks, 2017, 40(2): 77-91. [35]Marchant B P, Lark R M. The Matérn variogram model: Implications for uncertainty propagation and sampling in geostatistical surveys[J]. Geoderma, 2007, 140(4): 337-345. [36]Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equation of state calculations by fast computing machines[J]. The journal of chemical physics, 1953, 21(6): 1087-1092. [37]Minasny B, McBratney A B. The Matérn function as a general model for soil variograms[J]. Geoderma, 2005, 128(3-4): 192-207. [38]Myung I J. Tutorial on maximum likelihood estimation[J]. Journal of mathematical Psychology, 2003, 47(1): 90-100. [39]Onyejekwe, Site, Xin Kang, and Louis Ge. 'Assessment of empirical equations for the compression index of fine-grained soils in Missouri.' Bulletin of Engineering Geology and the Environment 74.3 (2015): 705-716. [40]Sawada, S. (2004). Estimation of liquefaction potential using dynamic penetration with pore pressure transducer. International Conference on Cyclic Behavior of Soils and Liquefaction Phenomena, Bochum, Germany, 305-312. [41]Sawada, S., Yoshizawa, D., Hiruma, N., Nagase, M., Sugano, T., & Nakazawa, H. (2009). Evaluation of differential settlement following liquefaction using Piezo Drive Cone. 17th International Conference on Soil Mechanics and Geotechnical Engineering, Alexandria, Egypt, 1064-1067. [42]Sawada, S., & Towhata, I. (2011). Use fo Piezo Drive Cone for evaluation of subsoil settlement induced by seismic liquefaction. ISSMGE Bulletin, 5(1), 15-25. [43]Sawada, S., Chao-Wen, W., & Hiruma, N. (2019). A few example of liquefaction assessment of ground during earthquake using Piezo Drive Cone in Taiwan. International Conference in Commemoration of 20th Anniversary of the 1999 Chi-Chi Earthquake, Taipei, Taiwan. [44]Stein R S. The role of stress transfer in earthquake occurrence[J]. Nature, 1999, 402(6762): 605-609. [45]Stuedlein AW, Kramer SL, Arduino P, Holtz RD. Geotechnical characterization and random field modeling of desiccated clay. ASCE J Geotechnical Geoenviron Eng 2012;138(11):1301–13. [46]Tipping M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of machine learning research, 2001, 1(Jun): 211-244. [47]TOOMANIAN N, JALALIAN A, KHADEMI H, et al. Subsurface Clay Prediction in an Arid Area[J]. 2010. [48]Vanmarcke EH. Probabilistic modeling of soil profiles. ASCE J Geotechnical Eng 1977;GT11:1227–46. [49]Vanmarcke EH. Random Fields: Analysis and Synthesis. Cambridge, Massachusetts: The MIT Press; 1983. [50]Wang Y, Cao Z. Expanded reliability-based design of piles in spatially variable soil using efficient Monte Carlo simulations[J]. Soils and Foundations, 2013, 53(6): 820-834. [51]Wang T, Zhou G, Jiang X, et al. Assessment for the spatial variation characteristics of uncertain thermal parameters for warm frozen soil[J]. Applied Thermal Engineering, 2018, 134: 484-489. [52]Yan S, Guo L. Calculation of scale of fluctuation and variance reduction function[J]. Transactions of Tianjin University, 2015, 21(1): 41-49. [53]Youd, T. L., Bartholomew, H. W., & Steidl, J. H. (2008). SPT Hammer Energy Ratio versus Drop Height. Journal of Geotechnical and Geoenvironmental Engineering, 134(3), 397-400. [54]Yang, W. [楊文衛]. (2006). Development and application of automatic monitoring system for standard penetration test in site investigation. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b3681191 [55]陳冠宇. PDC 鑽探設備於台灣地區現地調查之適用性[J]. 2021. [56]林姿町. 圓錐貫入試驗空間相關性之研究[J]. 2018. [57]黃玟翰. 以貝氏分析估計三度空間中的趨勢函數[J]. 2019. [58]陳靖霖、邱俊翔（2020年09月）。側潰地盤中樁在未液化土層之側向流動力。第18屆大地工程學術研究討論會，屏東,台灣。科技部：108-2628-E-002-004-MY3。 [59]蔡錦松 (2001)，「落錘型式對標準貫入試驗打擊能量影響」，國立成功大學土木工程研究所。 [60]郭林坪, 楊愛武, 閆澍旺, 等. 天津港地區土層剖面隨機場特徵參數的估計[J]. 工程地質學報, 2016, 24(1): 130-135. [61]黃富國 (2008)，「SPT液化機率及損害評估模式之建立與應用」，中國土木水利工程學刊，20(2)，155-174。 [62]劉樂平, 彭萍, 艾濤. 諾貝爾經濟學獎, 計量經濟學與現代貝葉斯方法[J]. 東華理工學院學報: 社會科學版, 2004, 23(1): 1-6. [63]林鵬,馬文,石恒,王朝陽,潘東東,許振浩,王欣桐.基於多維岩石圖像深度學習的岩性識別方法及系統[P]. 中國專利:CN112132200A. [64]李小勇, 謝康和. 土性參數相關距離的計算研究和統計分析[J]. 岩土力學, 2000, 21(4):4. [65]王俊翔. 根據圓錐貫入試驗資料判識土壤層面與分析工址的機率特性[J]. 2016. [66]章溢, 龔海林. 偏度係數的近似線性貝葉斯估計[J]. 統計與決策, 2017 (10): 78-81. [67]吉澤大造, 植村一瑛, 藤井紀之, 信本実. 荷重計測を伴うピエゾドライブコーンの開発. 応用地質技術年報 No.35, 2016, 75-82. [68]藤井紀之, 東畑郁生, 規矩大義, 澤田俊一, 吉澤大造, 信本実, 植村一瑛. 間隙水圧測定を伴う動的貫入試験法－その16 過剰間隙水圧に着目したFc の推定－. 第48回地盤工学研究発表会（富山）, 2013, 355-356. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85260 | - |
| dc.description.abstract | 迄今爲止諸多關於土壤液化潛勢的研究中，都離不開對土壤強度參數的獲取途徑和對參數料可靠性的評估。工址調查常以標準貫入試驗(SPT)和現場劈管取樣之物性試驗獲得其相關參數，本文旨在利用PDC設備所得的現地資料進行一維空間變異性之分析，其中包括對模擬模型適用性、模擬方法適用性，以及對辨識趨勢函數的可行性與方法的探討。 透過比對PDC資料和既有SPT資料中的土壤分層情況進行模擬N值與深度之波動尺度，進行多種趨勢函數判別法、模擬法、不同的自相關模型之間擬合成果的比較和探討。最終選出貝葉斯分析方法和Whittle-Matérn模型探討波動尺度，在不取樣的情況下對不同土層的波動尺度估算並分類，為PDC鑽探結果之使用提供一種新的加值應用。經研究發現，PDC所得N值與深度的波動尺度大小可分辨出粘土和砂土。 | zh_TW |
| dc.description.abstract | So far,Many studies on soil liquefaction potential are inseparable from acquiring soil strength parameters and evaluating the reliability of parameter materials. The relevant parameters obtain by standard penetration test (SPT) and physical property test of on-site split pipe sampling in the site survey. The applicability of the simulation method and the discussion on the feasibility and method of identifying the trend function. In order to simulate the scale of fluctuation (SOF) in N value and depth, this paper compares the soil stratification in the Piezo Drive Cone (PDC) data and the existing SPT data. Discuss various trend function discrimination methods, simulation methods, and relevant results between different autocorrelation models. Finally, the Bayesian analysis method and Whittle-Matérn model are selected to explore the SOF, estimate and classify the SOF of different soil layers without sampling, and provide a new value-added application for PDC. The research found that the SOF of N value and depth obtained by PDC can distinguish clay and sand. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:53:36Z (GMT). No. of bitstreams: 1 U0001-2807202216415100.pdf: 35319193 bytes, checksum: 4f40c247ae1419a6207419bee7e45757 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 目錄 口試委員會審定書 I 致謝 II 摘要 III Abstract IV 目錄 V 圖目錄 IX 表目錄 XIII 第一章 緒論 1 1.1 研究動機與目的 1 1.2 研究內容 2 1.3 論文架構 5 第二章 文獻回顧 6 2.1 經典統計 6 2.1.1 最小二乘法 (Ordinary Least Squares, OLS) 6 2.1.2 最大似然估計法 (Maximum likelihood method, MLE) 8 2.1.3 矩估計法 (Method of Moments, MM) 10 2.2 參數估計方法和原理 11 2.2.1 貝葉斯統計和經典統計的區別 11 2.2.2 貝葉斯估計法 (Bayesian Estimation Method, BEM) 14 2.3 空間變異性 16 2.3.1 隨機場理論 (Random Feild Theory) 16 2.3.2 波動尺度 17 2.3.3 自相關距離 21 2.4 自相關模型之選擇 22 2.4.1 自相關模型 22 2.4.2 經典自相關模型 22 2.4.3 新式自相關模型 23 2.5 演算方法 25 2.5.1 馬可夫链蒙地卡羅法 25 2.5.2 漸進式馬可夫鏈蒙地卡羅法 (T-MCMC method) 28 2.5.3 改良漸進式馬科夫鏈蒙地卡羅法 (Improved T-MCMC method,iMCMC) 30 2.5.4 稀疏貝葉斯學習 (Sparse Bayesian Learning, SBL) 31 2.6 現地實驗 34 2.6.1 標準貫入試驗 (Standard Penetration Test, SPT) 34 2.6.2 新式動態貫入設備 (Piezo Drive Cone) 35 2.6.3 PDC操作流程 40 2.6.4 PDC資料之N值獲取 45 第三章 研究方法 47 3.1 資料分析 49 3.1.1 資料介紹 49 3.1.2 資料區別與選用 51 3.2 隨機場參數模擬之影響 56 2.1 影響因子一：波動尺度 56 3.2.2 影響因子二：平滑度參數 57 3.2.3 影響因子三：資料趨勢 58 3.3 建立模擬隨機場 61 3.4 經典分析 62 3.4.1 矩估計法 62 3.4.2 最大似然法 68 3.4.3 模型適用性綜合討論 72 3.4.4 方法適用性綜合討論 72 3.4.5 小結 75 3.5 貝葉斯分析 75 3.5.1 貝葉斯分析法 76 3.5.2 討論 82 第四章 現地案例討論 86 4.1 案例場址資訊 86 4.2 經典分析結果 87 4.2.1 矩估計法結果 87 4.2.2 最大似然法結果 108 4.2.3 結果綜合比較 115 4.3 貝葉斯分析結果 120 4.4 結果統整 123 4.5 土壤分層結果 124 4.5.1 比對標準 124 4.5.2 波動尺度之分層計算結果 125 4.5.3 各土層模擬結果統整 135 第五章 結論與建議 138 5.1 結論 138 5.2 建議 138 參考文獻 139 附錄A 147 附錄B 149 附錄C 158 附錄D 167 附錄E 186 附錄F 253 | |
| dc.language.iso | zh-TW | |
| dc.subject | 波動尺度 | zh_TW |
| dc.subject | Piezo Drive Cone(PDC) | zh_TW |
| dc.subject | Whittle-Matérn模型 | zh_TW |
| dc.subject | 貝葉斯分析 | zh_TW |
| dc.subject | N值 | zh_TW |
| dc.subject | Whittle-Matérn model | en |
| dc.subject | Piezo Drive Cone | en |
| dc.subject | N value | en |
| dc.subject | scale of fluctuation (SOF) | en |
| dc.subject | Bayesian analysis | en |
| dc.title | 以PDC資料分析土壤空間變異性 | zh_TW |
| dc.title | Analysis of Soil Spatial Variability Using Piezo Drive Cone Data | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭世豪(Shih-Hao Cheng),卓雨璇(Yu-Syuan Jhuo) | |
| dc.subject.keyword | Piezo Drive Cone(PDC),N值,波動尺度,貝葉斯分析,Whittle-Matérn模型, | zh_TW |
| dc.subject.keyword | Piezo Drive Cone,N value,scale of fluctuation (SOF),Bayesian analysis,Whittle-Matérn model, | en |
| dc.relation.page | 254 | |
| dc.identifier.doi | 10.6342/NTU202201841 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-08-01 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-07-29 | - |
| 顯示於系所單位： | 土木工程學系 | |
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