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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 卿建業 | zh_TW |
| dc.contributor.advisor | Jianye Ching | en |
| dc.contributor.author | 陳泳鏗 | zh_TW |
| dc.contributor.author | Tan Yong Keng | en |
| dc.date.accessioned | 2023-09-22T17:21:39Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
| dc.identifier.citation | [1] A. Akkaya and E. Vanmarcke. Estimation of Spatial Correlation of Soil Parameters Based on Data from the Texas AM University NGES, pages 29–40. 06 2003.
[2] E. Bombasaro and T. Kasper. Evaluation of spatial soil variability in the pearl river estuary using cptu data. Soils and Foundations, 56(3):496–505, 2016. [3] T. Bong and A. W. Stuedlein. Effect of cone penetration conditioning on random field model parameters and impact of spatial variability on liquefaction-induced differential settlements. Journal of Geotechnical and Geoenvironmental Engineering,144(5):04018018, 2018. [4] D. Breysse, H. Niandou, S. Elachachi, and L. Houy. A generic approach to soil–structure interaction considering the effects of soil heterogeneity. Géotechnique, 55(2):143–150, 2005. [5] Y. C. Chang, J. Ching, K. K. Phoon, and Q. Yue. On the hole effect in soil spatial variability. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7:04021039, 12 2021. [6] J. Ching and Y. Chen. Transitional markov chain monte carlo method for bayesian model updating, model class selection, and model averaging. Journal of Engineering Mechanics, 133(7):816–832, 2007. [7] J. Ching, W. Huang, and K. K. Phoon. 3d probabilistic site characterization by sparse bayesian learning. Journal of Engineering Mechanics, 146(12), 12 2020. [8] J. Ching, K. K. Phoon, A. W. Stuedlein, and M. Jaksa. Identification of sample path smoothness in soil spatial variability. Structural Safety, 81:101870, 2019. [9] J. Ching, K. K. Phoon, Z. Yang, and A. W. Stuedlein. Quasi-site-specific multivariate probability distribution model for sparse, incomplete, and three-dimensional spatially varying soil data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(1):53–76, 2022. [10] J. Ching and J. S. Wang. Application of the transitional markov chain monte carlo algorithm to probabilistic site characterization. Engineering Geology, 203:151–167, 2016. Special Issue on Probabilistic and Soft Computing Methods for Engineering Geology. [11] J. Ching, T. J. Wu, A. W. Stuedlein, and T. Bong. Estimating horizontal scale of fluctuation with limited cpt soundings. Geoscience Frontiers, 9(6):1597–1608, 2018. Reliability Analysis of Geotechnical Infrastructures. [12] J. Ching, I. Yoshida, and K. K. Phoon. Comparison of trend models for geotechnical spatial variability: Sparse bayesian learning vs. gaussian process regression. Gondwana Research, 08 2022. [13] J. Ching, Y. Zhiyong, and K. K. Phoon. Dealing with nonlattice data in three-dimensional probabilistic site characterization. Journal of Engineering Mechanics, 147:06021003, 05 2021. [14] T. de Gast, P. J. Vardon, and M. A. Hicks. Assessment of soil spatial variability for linear infrastructure using cone penetration tests. Géotechnique, 71(11):999–1013, 2021. [15] D. J. DeGroot and G. B. Baecher. Estimating autocovariance of in-situ soil properties. Journal of Geotechnical Engineering, 119(1):147–166, 1993. [16] S. Firouzianbandpey, D. Griffiths, L. Ibsen, and L. Andersen. Spatial correlation length of normalized cone data in sand: case study in the north of denmark. Canadian Geotechnical Journal, 51(8):844–857, 2014. [17] M. Hicks and W. Spencer. Influence of heterogeneity on the reliability and failure of a long 3d slope. Computers and Geotechnics, 37(7):948–955, 2010. [18] M. A. Hicks and Y. Li. Influence of length effect on embankment slope reliability in 3d. International Journal for Numerical and Analytical Methods in Geomechanics, 42(7):891–915, 2018. [19] M. B. Jaksa, W. S. Kaggwa, and P. I. Brooker. Experimental evaluation of the scale of fluctuation of a stiff clay. In Proceedings of the 8th International Conference on the Application of Statistics and Probability, volume 1, pages 415–422, Sydney, Australia, December 12-15 1999. [20] J. Ji and C. L. Chan. Long embankment failure accounting for longitudinal spatial variation–a probabilistic study. Computers and Geotechnics, 61:50–56, 09 2014. [21] Lankelma. Uk 21 inclined cpt rams. https://www.lankelma.com/wp-content/uploads/2019/02/Inclined-Rig-UK21.pdf, 2019. [22] Y. Li, M. Hicks, and J. Nuttall. Comparative analyses of slope reliability in 3d. Engineering Geology, 196:12–23, 2015. [23] C. N. Liu and C.-H. Chen. Spatial correlation structures of cpt data in a liquefaction site. Engineering Geology, 111(1):43–50, 2010. [24] W. F. Liu and Y. F. Leung. Characterising three-dimensional anisotropic spatial correlation of soil properties through in situ test results. Géotechnique, 68(9):805–819, 2018. [25] M. Lloret-Cabot, G. Fenton, and M. Hicks. On the estimation of scale of fluctuation in geostatistics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 8, 08 2014. [26] K. K. Phoon and F. H. Kulhawy. Characterization of geotechnical variability. Canadian Geotechnical Journal, 36(4):612–624, 1999. [27] Probedrill. Inclined cpt. https://www.probedrill.com.au/wp-content/uploads/Inclined-CPT-Dec-2018.pdf, 2018. [28] X. H. Qi and H. X. Liu. Estimation of autocorrelation distances for in-situ geotechnical properties using limited data. Structural Safety, 79:26–38, 2019. [29] A. W. Stuedlein, S. L. Kramer, P. Arduino, and R. D. Holtz. Geotechnical characterization and random field modeling of desiccated clay. Journal of Geotechnical and Geoenvironmental Engineering, 138(11):1301–1313, 2012. [30] E. H. Vanmarcke. Probabilistic modeling of soil profiles. Journal of the Geotechnical Engineering Division, 103(11):1227–1246, 1977. [31] T. Xiao, D. Q. Li, Z. J. Cao, and L. M. Zhang. Cpt-based probabilistic characterization of three-dimensional spatial variability using mle. Journal of Geotechnical and Geoenvironmental Engineering, 144(5):04018023, 2018. [32] I. Yoshida, Y. Tomizawa, and Y. Otake. Estimation of trend and random components of conditional random field using gaussian process regression. Computers and Geotechnics, 136:104179, 2021. [33] J. Zhang, D. M. Zhang, H. Huang, K. K. Phoon, C. Tang, and G. Li. Hybrid machine learning model with random field and limited cpt data to quantify horizontal scale of fluctuation of soil spatial variability. Acta Geotechnica, 17:1–17, 04 2022. [34] L. Zhang and D. Murty. Spatial variability of in situ weathered soil. Géotechnique, 62:375–384, 05 2012. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90088 | - |
| dc.description.abstract | 土壤參數的空間變異性可藉由自相關函數(auto-correlation function, ACF)的參數來量化，具體而言是關聯性長度。過去的研究主要關注垂直自相關參數的辨識；只有極少數的研究關注水平自相關參數的辨識。本研究提出了一種新方法：使用傾斜錐貫入試驗(cone penetration test, CPT)作為輔助，以提升水平向自相關參數的辨識能力。本研究首先分析了各個場址的CPT數據，以展示僅從多個垂直CPT辨識水平自相關參數的弱勢。本研究亦通過數值案例探討了有效辨識水平自相關參數的CPT排佈方式。最後，本研究探討了通過多個垂直CPT，外加一個（模擬的）傾斜CPT數據，來辨識水平自相關參數的可行性。 | zh_TW |
| dc.description.abstract | The spatial variability of soil data is characterized by the auto-correlation function (ACF) parameters, specifically scale of fluctuation. In the literature, the study mainly focuses on identification of vertical auto-correlation parameters; the identification of horizontal auto-correlation parameters received limited attention. In this study, a novel approach to enhance the identification of horizontal auto-correlation parameters using an inclined cone penetration test (CPT) as auxiliary sounding is proposed. The CPT data from various real site locations are analyzed, to demonstrate the shortcomings of identifying horizontal auto-correlation parameters from multiple vertical CPTs. Numerical study is conducted to investigate the possible approach (CPTs layout) for identifying horizontal parameters. Subsequently, the potential of identifying horizontal auto-correlation parameters from a combination of vertical CPTs and synthetic data from an inclined auxiliary CPT is examined. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:21:39Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T17:21:39Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 iii Abstract v Contents vii List of Figures xi List of Tables xv Denotation xvii Chapter 1 Introduction 1 1.1 Background 1 1.2 Objectives 2 1.3 Organization of the Thesis 2 1.3.1 Chapter 2: Literature Review 3 1.3.2 Chapter 3: Methodology 3 1.3.3 Chapter 4: Real Case Histories with Multiple Vertical CPTs 3 1.3.4 Chapter 5: Numerical Examples 3 1.3.5 Chapter 6: Generalization to Real Site with Synthetic Soundings 4 1.3.6 Chapter 7: Conclusion and Future Work 4 Chapter 2 Literature Review 5 Chapter 3 Methodology 7 3.1 Random field 7 3.2 Auto-correlation Function (ACF) 8 3.2.1 Scale of Fluctuation (SOF) 10 3.2.2 Smoothness 12 3.3 Modelling of Spatial Variability 14 3.4 Modelling of Trend Function 15 3.5 Likelihood function 16 3.6 Transitional Markov Chain Monte-Carlo (TMCMC) 17 Chapter 4 Real Case Histories with Multiple Vertical CPTs 19 4.1 Real Site Data 19 4.1.1 Examples of satisfactory identification results 20 4.1.2 Examples of poor identification results 23 4.1.3 Summary 31 Chapter 5 Numerical Examples 33 5.1 Introduction 33 5.1.1 Presentation of The Results 34 5.1.2 Case 1: Insufficient Identification with a Single Inclined CPT 35 5.1.3 Case 2: Limited Effectiveness of Multiple Inclined CPTs at Large Spacings 36 5.1.4 Case 3: Minimum Number of Vertical CPTs for Effective Identification 36 5.1.5 Case 4: The Significance of Configuration in Inclined CPTs 38 5.1.6 Summarization 46 Chapter 6 Generalization to Real Site with Synthetic Inclined Sounding 51 6.1 Site introduction 51 6.2 Simulating Inclined CPT Data 52 6.3 Configuration 53 6.4 Identification Results 55 Chapter 7 Conclusion and Future Research 61 7.1 Conclusion 61 7.2 Future research 62 7.2.1 Improving Computing Efficiency 63 7.2.2 Optimization of Inclined Auxiliary Sounding Configuration 63 References 65 Appendix A — Supplmentary 71 A.1 Boundary of Prior Distributions in Each Analysis 71 Appendix B — Oral Defense Segment 73 B.1 Oral Defense Committee 73 B.1.1 Oral Defense Content 73 B.1.1.1 Question 1: 73 B.1.1.2 Question 2: 74 B.1.1.3 Question 3: 74 B.1.1.4 Question 4: 75 B.1.1.5 Question 5: 75 B.1.1.6 Question 6: 76 B.1.1.7 Question 7: 76 B.1.1.8 Question 8: 77 B.1.1.9 Question 9: 77 B.1.1.10 Question 10: 78 B.1.1.11 Question 11: 78 B.2 Suggestions 79 B.2.0.1 Suggestion 1: 79 B.2.0.2 Suggestion 2: 79 B.2.0.3 Suggestion 3: 80 B.2.0.4 Suggestion 4: 80 | - |
| dc.language.iso | en | - |
| dc.title | 以傾斜錐貫入試驗識別水平自相關參數 | zh_TW |
| dc.title | Identification of horizontal auto-correlation parameters using inclined cone penetration tests | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉家男;王瑞斌 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Nan Liu;Jui-Pin Wang | en |
| dc.subject.keyword | 空間變異性,關聯性長度,水平自相關參數,傾斜錐貫入試驗, | zh_TW |
| dc.subject.keyword | spatial variability,scale of fluctuation,horizontal auto-correlation parameters,inclined cone penetration test, | en |
| dc.relation.page | 80 | - |
| dc.identifier.doi | 10.6342/NTU202301836 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-11 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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