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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 卿建業 | zh_TW |
| dc.contributor.advisor | Jian-Ye Ching | en |
| dc.contributor.author | 許澄毓 | zh_TW |
| dc.contributor.author | Cheng-Yu Xu | en |
| dc.date.accessioned | 2025-08-04T16:06:09Z | - |
| dc.date.available | 2025-08-05 | - |
| dc.date.copyright | 2025-08-04 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-28 | - |
| dc.identifier.citation | Adhikari, P., Gebreslasie, Y. Z., Ng, K. W., Sullivan, T. A., and Wulff, S. S. (2018). Static and dynamic analysis of driven piles in soft rocks considering LRFD using a recently developed electronic database. IFCEE 2018, 83–92.
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Moghaddam, R. B., Jayawickrama, P. W., Lawson, W. D., Surles, J. G., and Seo, H. (2018). Texas cone penetrometer foundation design method: Qualitative and quantitative assessment. DFI Journal-The Journal of the Deep Foundations Institute, 12 (2), 69–80. O'Neill, M., Ata, A., Vipula-dan, C., and Yin, S. (2002). Axial Performance of ACIP Piles in Texas Coastal Soils. Deep Foundations 2002: An International Perspective on Theory, Design, Construction, and Performance, 1290–1304. O'Neil, M. W. and Reese, L. C. (1999). Drilled shafts: Construction procedures and design methods. United States. Federal Highway Administration. Office of Infrastructure. Paikowsky, S. G. (2010). LRFD Design and Construction of Shallow Foundations for Highway Bridge Structures. Transportation Research Board. Pando, M. A., Fer-dez, A. L., and Filz, G. M. (2004). Pile settlement predictions using theoretical load transfer curves and seismic CPT data. Geotechnical and Geophysical Site Characterization Vols 1 and 2, 1525–1531. Petek, K., Mitchell, R., and Ellis, H. (2016). FHWA deep foundation load test database version 2.0 user manual. United States. Federal Highway Administration. Phoon, K.-K. and Tang, C. (2019). Characterisation of geotechnical model uncertainty. georisk: Assessment and management of risk for engineered systems and geohazards, 13 (2), 101–130. Phoon, K.-K. (2006). Modeling and Simulation of Stochastic Data. GeoCongress 2006: Geotechnical Engineering in the Information Technology Age, 1–17. Powell, J. J. and Lunne, T. (2005). A comparison of different sized piezocones in UK clays. Proceedings of Proceedings of the 16th International Conference on Soil Mechanics and Geotechnical Engineering, IOS Press, 729–734. Radharkrishna, H., Cragg, C., Tsang, R., and Bozozuk, M. (1986). Uplift and compression behavior of drilled piers in Leda clay. Proceedings of 39 th Canadian Geotechnical Conference, Ottawa, 123–130. Roling, M. J., Sritharan, S., and Suleiman, M. T. (2011). Development of LRFD procedures for bridge pile foundations in lowa-volume I: an electronic database for PIle LOad Tests (PILOT). Iowa State University. Institute for Transportation. Ruberti, M. (2015). Investigation of Installation Torque and Torque-to-Capacity Relationship of Screw-Piles and Helical Anchors. M.S. Thesis, University of Massachusetts. Samtani, N. C., Allen, T. M., and NCS GeoResources, L. (2018). Implementation report-expanded database for service limit state calibration of immediate settlement of bridge foundations on soil. United States. Department of Transportation. Federal Highway Administration . Smith, T., Banas, A., Gummer, M., and Jin, J. (2011). Recalibration of the GRLWEAP LRFD resistance factor for Oregon DOT. Oregon. Dept. of Transportation. Research Section. Takada, M., Fujisawa, H., and Nishikawa, T. (2018). HMLasso: Lasso with High Missing Rate. arXiv preprint arXiv:1811.00255. Takesue, K., Sasao, H., and Makihara, Y. (1996). Cone penetration testing in volcanic soil deposits. Proceedings of Advances in Site Investigation Practice Proceedings of The International Conference,London Tang, C. and Phoon, K.-K. (2018). Statistics of model factors and consideration in reliability-based design of axially loaded helical piles. Journal of geotechnical and geoenvironmental engineering, 144 (8), 04018050. Tucker, K. D. (1988). Performance evaluation of pile foundation using CPT data. Proceedings of the Second International Conference on Case Histories in Geotechnical Engineering(Paper No. 6.58). St. Louis, MO, United States. Yang, Z., Jardine, R., Guo, W., and Chow, F. (2015). A Comprehensive Database of Tests on Axially Loaded Piles Driven in Sand. Academic Press. Zhang, L., Shek, L. M., Pang, H. W., and Pang, C. F. (2006). Knowledge-based design and construction of driven piles. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 159 (3), 177–185. Zhou, Z.-H. (2025). Ensemble Methods: Foundations And Algorithms. CRC Press. 李芯茹 (2022) 垂直載重下淺基礎行為的數據驅動預測 (碩士學位論文)。國立台灣大學，台北市 李宜庭 (2023) 以數據分析方法預測鑽掘樁於軸向載重下之行為 (碩士學位論文)。國立台灣大學，台北市 李明澤 (2024) 以數據分析方法預測鑽掘樁於軸向載重下之行為 (碩士學位論文)。國立台灣大學，台北市 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98344 | - |
| dc.description.abstract | 載重試驗在大地工程中扮演關鍵角色，其量測之極限承載力為樁基礎與淺基礎設計時的重要依據，直接影響結構物地基之穩定與安全。然而，載重試驗往往需較大作業空間，且試驗成本高昂，促使數據驅動之預測方法逐漸受到重視，以期降低時間與成本。
本研究以 Phoon and Tang (2019) 及李宜庭 (2023) 所建構之載重試驗資料庫為基礎，並採用 Ching et al. (2021) 所提出之準場址特定階層貝氏模型 (Hierarchical Bayesian Model, HBM) 進行極限承載力預測。但文獻指出，HBM 在資料稀缺或資料變異性較高時，預測結果易出現不穩定性與信賴區間過寬之現象。因此，本研究提出三項改進策略，分別為模型簡化、客製化聚類區域化 (Tailored Clustering Enabled Regionalization, TCER) 及資料變數降維 (Dimension Reduction, DR) ，以期提升模型預測之穩健性與適用性。 然而，不同基礎型式與資料情境下，並無法找到單一通用最佳模型。故本研究進一步整合多種模型，發展以最小微分熵為選擇準則之 HBM 堆疊集成學習(HBM-Based Stacking Ensemble Learning)，以動態篩選表現最佳之模型。本研究驗證此方法於四種基礎型式、共十二個資料庫中的應用成效，並以交叉驗證下之均方根誤差 (Root Mean Square Error, RMSE) 作為驗證指標，系統性分析並驗證該方法於多基礎承載力預測之可行性與優勢。 | zh_TW |
| dc.description.abstract | Load testing plays a critical role in geotechnical engineering, as the measured ultimate bearing capacity serves as a key reference for the design of pile and shallow foundations, directly influencing the stability and safety of structural foundations. However, load testing typically requires large working space and incurs high costs, which has driven increasing attention toward data-driven prediction methods to reduce both time and expenses.
This study is based on the load test databases compiled by Phoon and Tang (2019) and Li (2023), and adopts the Quasi-Site-Specific Hierarchical Bayesian Model (HBM) proposed by Ching et al. (2021) to predict the ultimate bearing capacity. However, previous studies have indicated that HBM tends to produce unstable predictions and overly wide confidence intervals when the available data is limited or exhibits high variability. Therefore, this study proposes three improvement strategies: model simplification, Tailored Clustering Enabled Regionalization (TCER), and dimension reduction (DR), aiming to enhance the robustness and practical applicability of the model predictions. Nonetheless, there is no universally optimal model applicable across different foundation types and data conditions. To address this, this study further integrates multiple models and develops an HBM-Based Stacking Ensemble Learning approach that employs minimum differential entropy as the selection criterion to dynamically identify the best-performing model. The effectiveness of this method is validated across four foundation types and twelve independent databases, with Root Mean Square Error (RMSE) under cross-validation adopted as the performance indicator. A systematic analysis is conducted to verify the feasibility and advantages of the proposed method in multi-foundation bearing capacity prediction. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-04T16:06:09Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-04T16:06:09Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii 目次 v 圖次 x 表次 xix 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 研究流程 3 1.4 本文內容 4 第二章 文獻回顧與理論背景 5 2.1 大地工程資料特性 5 2.2 Johnson分布 7 2.3 貝氏定理 12 2.3.1 機率密度函數 12 2.3.2 累積分布函數 13 2.3.3 先驗PDF 13 2.3.4 概似函數 13 2.3.5 後驗PDF 14 2.3.6 貝氏定理 14 2.4 階層貝氏模型(Hierarchical Bayesian Model, HBM) 16 2.4.1 場址變異性的建模挑戰 16 2.4.2 準場址特定 16 2.4.3 階層貝氏模型架構 17 2.4.4 共軛先驗PDF (Conjugate Prior PDF) 19 2.4.5 學習階段 22 2.4.6 推論階段 26 2.5 傳統資料分群 (Classical Clustering) 28 2.6 整合學習 (Ensemble Learning) 30 第三章 資料庫 32 3.1 資料庫簡介 32 3.2 資料處理 34 3.2.1場址編號方式 34 3.2.2轉換模型的選擇方式 34 3.2.3輸入資料變數選擇方式 35 3.3 資料庫介紹 36 3.3.1淺基礎資料庫 (FLTD_ShalFound) 36 3.3.2打擊樁資料庫 (FLTD_DrivenPile) 39 3.3.3鑽掘樁資料庫 (DS/9/328) 44 3.3.4螺旋樁資料庫 (FLTD_HelicalPile) 47 第四章 多模型建構與HBM堆疊集成學習 50 4.1 模型的簡化前言與動機 50 4.1.1 Generic Model的架構 51 4.1.2 Generic Model的共軛先驗PDF 52 4.1.3 Generic Model的學習與推論階段 53 4.1.4 Cg-HBM的架構 55 4.1.5 Cg-HBM的共軛先驗PDF 56 4.1.6 Cg-HBM的學習與推論階段 58 4.2 客製化聚類區域化 (Tailored Clustering Enabled Regionalization, TCER) 63 4.2.1 TCER的流程架構 63 4.2.2 離群場址偵測機制：Maximum Site Similarity (MSS) 65 4.2.3 場址相似度計算：Modified Inter-Dataset Similarity Measure (MIDSM) 66 4.2.4 相似場址集群與推論模型整合 68 4.3 降維 (Dimension Reduction, DR) 69 4.3.1 資料變數的降維動機與前言 69 4.3.2 降維的架構與流程 69 4.3.3 HMLasso降維 70 4.4 HBM堆疊集成學習 (HBM-Based Stacking Ensemble Learning) 74 4.4.1堆疊集成的動機與前言 74 4.4.2堆疊集成的原理 75 4.4.3堆疊集成的運作 77 第五章 場址案例分析與驗證 78 5.1 預測說明 79 5.1.1 95% CI的視覺化處理 79 5.1.2 預測情境 81 5.1.3 預測介紹 81 5.2 HBM堆疊集成學習驗證說明 82 5.2.1 均方根誤差 (RMSE) 82 5.2.2 驗證介紹 83 5.3 場址一：Brie Plateau, France 84 5.3.1 基礎貝氏模型預測表現 85 5.3.2 TCER 模型預測表現 87 5.3.3 TCER+DR與DR模型預測表現 90 5.3.4 HBM堆疊集成學習預測表現與驗證 95 5.4 場址二：Canons Park, UK 102 5.4.1 基礎貝氏模型預測表現 103 5.4.2 TCER 模型預測表現 105 5.4.3 TCER+DR與DR模型預測表現 108 5.4.4 HBM堆疊集成學習預測表現與驗證 112 5.5 場址三：Hinds County, Mississippi, USA 119 5.5.1 基礎貝氏模型預測表現 120 5.5.2 TCER 模型預測表現 122 5.5.3 TCER+DR與DR模型預測表現 125 5.5.4 HBM堆疊集成學習預測表現與驗證 129 5.6 場址四：Amherst, Massachusetts, USA 136 5.6.1 基礎貝氏模型預測表現 137 5.6.2 TCER 模型預測表現 139 5.6.3 TCER+DR與DR模型預測表現 142 5.6.4 HBM堆疊集成學習預測表現與驗證 146 第六章 HBM堆疊集成學習的資料庫結果驗證 153 6.1 Leave-One-Site-Out (LOSO) 153 6.2資料庫案例結果驗證 154 6.2.1 FLTD_ShalFound Compression (soil) 154 6.2.2 FLTD_DrivenPile Compression (clay) 158 6.2.3 其他資料庫結果驗證整合 161 6.3 Ensemble Learning 之預測改善率 171 6.3.1 以貝氏模型架構下之實務模型表現比較 171 6.3.2以貝氏模型架構下之現有最佳模型表現比較 177 第七章 結論與建議 183 7.1 結論 183 7.2 未來建議 185 參考文獻 186 附錄 Ⅰ 現地載重試驗資料 192 附錄 Ⅱ 案例分析相似場址資訊 194 附錄 Ⅲ 資料庫驗證的偏離場址資訊 202 附錄 Ⅳ 其他資料庫RMSE交叉驗證結果 206 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 客製化聚類區域化 | zh_TW |
| dc.subject | 降維 | zh_TW |
| dc.subject | 可靠度設計 | zh_TW |
| dc.subject | HBM堆疊集成學習 | zh_TW |
| dc.subject | 載重試驗資料庫 | zh_TW |
| dc.subject | 階層式貝氏模型 | zh_TW |
| dc.subject | Tailored Clustering Enabled Regionalization (TCER) | en |
| dc.subject | Load Test Database | en |
| dc.subject | HBM-Based Stacking Ensemble Learning | en |
| dc.subject | Dimension Reduction (DR) | en |
| dc.subject | Reliability-Based Design | en |
| dc.subject | Hierarchical Bayesian Model (HBM) | en |
| dc.title | 利用載重試驗資料庫進行基礎承載力預測：準場址特定方法 | zh_TW |
| dc.title | Foundation Capacity Prediction Using Load-Test Databases: Quasi-Site-Specific Approach | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林志平;王瑞斌 | zh_TW |
| dc.contributor.oralexamcommittee | Chih-Ping Lin;Jui-Pin Wang | en |
| dc.subject.keyword | 階層式貝氏模型,可靠度設計,客製化聚類區域化,降維,HBM堆疊集成學習,載重試驗資料庫, | zh_TW |
| dc.subject.keyword | Hierarchical Bayesian Model (HBM),Reliability-Based Design,Tailored Clustering Enabled Regionalization (TCER),Dimension Reduction (DR),HBM-Based Stacking Ensemble Learning,Load Test Database, | en |
| dc.relation.page | 215 | - |
| dc.identifier.doi | 10.6342/NTU202502445 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-30 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2025-08-05 | - |
| 顯示於系所單位： | 土木工程學系 | |
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