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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96787完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳柏翰 | zh_TW |
| dc.contributor.advisor | Po-Han Chen | en |
| dc.contributor.author | 游添隆 | zh_TW |
| dc.contributor.author | Adrianto Oktavianus | en |
| dc.date.accessioned | 2025-02-21T16:33:03Z | - |
| dc.date.available | 2025-02-22 | - |
| dc.date.copyright | 2025-02-21 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-12-30 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96787 | - |
| dc.description.abstract | 地震後的建築復原是一項高度複雜且時間緊迫的過程，需要創新方法來提升效率和精確性。本研究聚焦於兩種主要方法，以實現整合尖端技術的策略，包括網路地圖服務 (WMS)、深度學習 (DL) 和建築資訊模型 (BIM)，以改善並加速地震後建築復原過程。第一種方法著重於開發基於BIM的應用程式，作為建築復原計劃的規劃平台。該平台功能涵蓋檢查每個結構元素的狀態、制定修復計劃、估算總復原成本，並根據復原計劃更新BIM模型。在此過程中，DL用於結構元素評估，自動化損壞分類和損壞區域量化，有助於提升建築復原規劃的精確性和效率。第二種方法則結合BIM、DL與WMS，以分析結構元素損壞並為長期建築修復做準備。透過WMS整合地理空間數據，此方法加強了受損區域的情境分析，並支援可持續的修復策略。採用Vision Transformer模型進行圖像分類，並利用Detectron2進行分割，可精確檢測和評估裂縫、剝落及裸露鋼筋等問題，並直接連接至BIM數據，以實現全面的復原規劃。案例研究強調了這些方法的實際應用，顯示了計劃速度和資源分配的改善，計劃時間縮短了約10-15%。此整合框架的重要優勢在於，通過考量低碳材料和節能技術，提升可持續性，加速復原計劃的制定，並提高損壞分類的精準度。儘管目前數據集的限制影響了DL模型的精度，隨著數據的持續收集和系統改進，結果預期會逐步提升。總體而言，本研究強調了結合BIM應用開發和整合BIM、DL與WMS的雙方法在地震後復原中的變革潛力。不僅支援有效且可持續的建築復原，所建議的方法亦為未來朝向完全自動化、數據驅動的智慧復原系統奠定基礎，符合現代建築目標和可持續發展策略。 | zh_TW |
| dc.description.abstract | Building recovery after an earthquake is a highly complicated and time-sensitive process requiring novel approaches to improve efficiency and accuracy. The study concentrates on two primary approaches to accomplish an integrated strategy that makes use of cutting-edge technologies: Web Map Services (WMS), deep learning (DL), and Building Information Modeling (BIM) in order to improve and expedite the post-earthquake building recovery process. The first methodology focuses on developing a BIM-based application to serve as a platform for planning building recovery. This platform's features involve reviewing each structural element's status, generating rehabilitation plans, estimating the total cost of building recovery, and updating the BIM model following the recovery plan. This approach, which DL supports for structural element evaluation, improves the precision and effectiveness of building recovery planning by automating damage classification and quantifying damage areas. The second methodology integrates BIM, DL, and WMS to analyze structural element damage and prepare for long-term building rehabilitation. By facilitating the incorporation of geospatial data using WMS, this method improves the contextual analysis of damaged regions and supports sustainable rehabilitation strategies. Vision Transformer models for image classification and Detectron2 for segmentation allow for the exact detection and evaluation of problems such as cracks, spalling, and exposed rebar, which can then be connected directly to BIM data for comprehensive recovery planning. Case studies highlight the practical implementation of these approaches, demonstrating gains in planning speed and resource allocation, with possible reductions in planning time of 10-15%. Significant advantages of this integrated framework include improved sustainability by considering low-carbon materials and energy-efficient techniques, quicker recovery plan formulation, and more precise damage classification. Even though dataset limits now impact the accuracy of DL models, these results should eventually increase due to ongoing data collecting and system improvements. Overall, this study emphasizes the transformational potential of a dual-methodological approach to post-earthquake recovery that combines BIM-based application development with integrating BIM, DL, and WMS. Along with supporting effective and sustainable building recovery, the suggested approaches lay the foundation for future developments in the direction of fully automated, data-driven intelligent recovery systems that complement contemporary construction goals and sustainable development strategies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-21T16:33:03Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-21T16:33:03Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 iii Abstract iv Table of Contents vi List of Figures x List of Tables xiii Chapter 1: Introduction 1 1.1 Background and motivation 1 1.2 Problem statement 4 1.3 Research objectives 5 1.4 Scope of the Study 7 1.4.1 Research Scope 7 1.4.2 Assumptions 9 1.5 Organization of the dissertation 9 Chapter 2: Literature Review 11 2.1 Post-earthquake building assessment and recovery 11 2.1.1 Visual inspection in the structural element assessment 11 2.1.2 Post-earthquake building recovery processes 13 2.1.3 Sustainability in post-disaster planning 15 2.2 Deep learning for building damage assessment 16 2.2.1 Application scope for deep learning in post-earthquake damage assessment 17 2.2.2 Image classification models 18 2.2.3 Image segmentation models 22 2.3 Building information modeling (BIM) application in the post-disaster context 27 2.4 Web map service in construction management and disaster recovery planning 31 2.5 Summary of challenges and research gaps 33 Chapter 3: Research Methodology 35 3.1 Research Initiation and Scoping 35 3.2 Framework Design and System Development 37 3.3 System Implementation and Validation 39 3.4 Findings Consolidation and Recommendations 41 Chapter 4: A Framework of Intelligent Post-Earthquake Building Recovery System 42 4.1 Introduction to the Framework 42 4.2 Overview of the Proposed System Framework 44 4.2.1 Inspection process 47 4.2.2 Deep learning process for damage classification and segmentation 49 4.2.3 Detailed engineering evaluation and rehabilitation planning process 52 4.2.4 BIM-based application process 56 4.3 Case study: Framework validation 59 Chapter 5: Sustainable Post-Earthquake Recovery Planning System with Integrated BIM, DL, and WMS 72 5.1 Introduction to the Integrated System 72 5.1.1 Deep learning model training 73 5.1.2 BIM-based plugin development 78 5.1.3 Case study implementation 79 5.2 Integrating DL-BIM-WMS for structural assessment and recovery planning 80 5.3 Deep learning for structural element assessment 83 5.3.1 Vision transformer for image classification 83 5.3.2 Detectron2 model for instance segmentation 85 5.4 Development of the BIM-DL-WMS plugin for sustainable building recovery planning 90 5.5 Case study: Application of the integrated system of BIM-DL-WMS 98 Chapter 6: Result and Discussion 107 6.1 Evaluation of the intelligent post-earthquake building recovery framework 107 6.2 Sustainability considerations in recovery planning 107 6.3 System performance and deep learning model evaluation 108 6.4 Benefits and challenges of the system implementation 109 6.5 Research limitations and future work 111 Chapter 7: Conclusions and Recommendations 113 7.1 Summary of research findings 113 7.2 Research contributions 114 7.3 Recommendations for future work 115 References 117 Appendixes 127 Appendix 1. Pixel-wise comparison of instance segmentation model for structural elements 127 Appendix 2. Expert interview details for framework development 129 Appendix 3. Rehabilitation method for recovery planning in the case study 149 Appendix 4. Expert feedback details for system evaluation and case study implementation 151 | - |
| dc.language.iso | en | - |
| dc.subject | 網路地圖服務 | zh_TW |
| dc.subject | 地震 | zh_TW |
| dc.subject | 數據驅動復原計劃 | zh_TW |
| dc.subject | 可持續建築 | zh_TW |
| dc.subject | 建築復原 | zh_TW |
| dc.subject | 建築資訊模型 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Building Information Modeling | en |
| dc.subject | Building Recovery | en |
| dc.subject | Sustainable Construction | en |
| dc.subject | Data-Driven Recovery Planning | en |
| dc.subject | Earthquake | en |
| dc.subject | Web Map Service | en |
| dc.subject | Deep Learning | en |
| dc.title | 透過整合建築資訊模型、深度學習與網路地圖服務提升地震後建築復原系統 | zh_TW |
| dc.title | Enhancing Post-Earthquake Building Recovery System Through Integration of BIM, Deep Learning, and Web Map Services | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 林之謙 | zh_TW |
| dc.contributor.coadvisor | Jacob Je-Chian Lin | en |
| dc.contributor.oralexamcommittee | 張陸滿;曾惠斌;詹瀅潔;謝尚賢;楊亦東 | zh_TW |
| dc.contributor.oralexamcommittee | Luh-Maan Chang ;Hui-Ping Tserng ;Ying-Chieh Chan;Shang-Hsien Hsieh;I-Tung Yang | en |
| dc.subject.keyword | 建築資訊模型,深度學習,網路地圖服務,建築復原,可持續建築,數據驅動復原計劃,地震, | zh_TW |
| dc.subject.keyword | Building Information Modeling,Deep Learning,Web Map Service,Building Recovery,Sustainable Construction,Data-Driven Recovery Planning,Earthquake, | en |
| dc.relation.page | 151 | - |
| dc.identifier.doi | 10.6342/NTU202404793 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-12-30 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2029-12-27 | - |
| 顯示於系所單位： | 土木工程學系 | |
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