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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳俊杉(Chuin-Shan Chen) | |
dc.contributor.author | Chun-Cheng Chang | en |
dc.contributor.author | 張鈞程 | zh_TW |
dc.date.accessioned | 2023-03-19T23:31:33Z | - |
dc.date.copyright | 2022-10-14 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-09-19 | |
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McKay, 'A method for registration of 3-D shapes,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, 1992, doi: 10.1109/34.121791. 工程施工查核作業參考基準, 行政院公共工程委員會, 2016. [Guidelines for Construction Surveillance, Department of Construction Management of Public Construction Commission of Executive Yuan in Taiwan, 2016.] COCO. 'Detection Evaluation.' cocodataset.org. https://cocodataset.org/#detection-eval (accessed Jun. 19, 2022). R. Sacks, I. Brilakis, E. Pikas, H. S. Xie, and M. Girolami, 'Construction with digital twin information systems,' Data-Centric Engineering, vol. 1, 2020. R. Al-Sehrawy and B. Kumar, 'Digital twins in architecture, engineering, construction and operations. A brief review and analysis,' in International Conference on Computing in Civil and Building Engineering, 2020: Springer, pp. 924-939. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85983 | - |
dc.description.abstract | 近年來,基於數位孿生解析複雜資料的方法,展現出被運用在營建管理上的潛力。然而,過去數位孿生的研究在鋼筋查驗上仍有諸多限制,例如低現地可行性及低自動化程度。因此,此研究提出一套端對端的鋼筋尺寸性品質管制 (dimensional quality control) 架構,輸入影像後便能直接得到缺失報表。 首先透過運動回復結構 (SfM)、多視角立體視覺 (MVS) 及基於基準點的尺度校正演算法,獲取鋼筋結構的三維模型。接著混和二維實例分割 (instance segmentation) 深度學習模型與三維分群 (clustering) 電腦視覺演算法,辨識出個別鋼筋。後續以隨機採樣一致法 (RANSAC) 預測三維空間中的線,及霍夫變換法 (Hough transform) 預測二維平面上的圓,達成直徑查驗;而分群演算法及分割結果則被用於間距查驗中。最後,建築資訊塑模 (BIM) 技術被整合進此研究。透過現地與模型 (Scan-vs-BIM) 的匹配與比對,尺寸性缺失能被辨識及追蹤。而透過調整現地與模型匹配演算法與開發進度偵測演算法,可一次性查驗多個結構構件。 在實驗室與現地進行的實驗驗證了此框架可被應用於複雜的鋼筋結構。分割模組在實驗柱上達到超過90%的精確率 (precision) 與超過97%的查全率 (recall)。此外,查驗模組在大號數與小號數鋼筋上各取得95.5%與70.8%的直徑分類準確率 (accuracy);間距查驗方面則是取得0.98%的平均相對誤差 (MRE)。在缺失追蹤模組中,刻意安排的不合格間距被成功地辨識與追蹤。最終,進度偵測演算法的精確率與查全率皆為100%,具備將抽樣檢查擴展為全面查驗的能力。 | zh_TW |
dc.description.abstract | Recently digital twin (DT)-based approaches that perform analytics on comprehensive data show potential in assisting construction management. However, in terms of reinforcing bar (rebar) inspection, previous research on DT reveals limitations such as impracticality on construction site and low level of automation. Hence, this study proposes an end-to-end framework for rebar dimensional quality control (DQC) that takes images as input and directly outputs issue reports. The combination of structure from motion (SfM), multi-view stereo (MVS), and fiducial-marker-based scale calibration algorithm were leveraged to reconstruct 3D scenes of rebar structures. The rebars within the 3D model were recognized by a hybrid model of deep learning (DL)-based 2D instance segmentation and computer vision (CV)-based 3D clustering algorithm. Then, 3D line random sample consensus (RANSAC) and 2D circle Hough transform were utilized to inspect the diameter; while clustering and segmentation results were utilized to inspect the spacing. Finally, building information modeling (BIM) was incorporated. The dimensional issues were identified and tracked by Scan-vs-BIM registration and comparison. To inspect multiple structural components at once, Scan-vs-BIM registration was modified and progress detection was explored. Experimental results on lab and on-site structures show that the framework is promising to be applied to complex rebar structures. The segmentation model reached a precision of over 90% and recall of over 97% on the lab column. Besides, the diameter inspection module achieved a classification accuracy of 95.5% and 70.8% for large-size and small-size rebars. A mean relative error (MRE) of 0.98% was reported during the spacing inspection. In issue tracking, the purposely defective spacing was identified and traced. Finally, both precision and recall reached 100% in the progress detection, which extends spot check to total inspection. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T23:31:33Z (GMT). No. of bitstreams: 1 U0001-1909202216162100.pdf: 33315225 bytes, checksum: 5c271ab14afce20a3f577a4ea0033251 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | Oral Examination Committee Approval i Acknowledgements ii 摘要 iii Abstract iv Table of Contents vi List of Figures ix List of Tables xiii List of Algorithms xiv List of Abbreviations xv Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Rebar Dimensional Quality Control 1 1.1.2 Traditional Approach 3 1.1.3 Novel Approach 6 1.2 Research Objectives 6 1.3 Organization of Thesis 7 Chapter 2 Literature Review 8 2.1 3D Reconstruction in Construction 8 2.2 Point Cloud Segmentation in Construction 10 2.3 Diameter and Spacing Inspection of Cylindrical Objects 11 2.4 Issue Identification in Construction 13 2.5 Research Gaps 14 Chapter 3 Methodology 16 3.1 Workflow 20 3.2 Digital Twin Generation 21 3.2.1 Data Collection 22 3.2.2 3D Reconstruction 23 3.3 Segmentation 27 3.3.1 2D Instance Segmentation 28 3.3.2 3D Clustering 29 3.4 Inspection 32 3.4.1 Diameter Inspection 32 3.4.2 Spacing Inspection 35 3.5 Issue Identification and Issue Tracking 37 3.5.1 Issue Identification 38 3.5.2 Issue Tracking 42 3.5.3 Alternatives 43 3.6 Localization 45 3.7 Experimental Validation 47 Chapter 4 Results and Discussion 51 4.1 Lab Column 51 4.1.1 Digital Twin Generation 51 4.1.2 Segmentation 54 4.1.3 Inspection 59 4.1.4 Issue Identification and Issue Tracking with LOD 400 BIM Models 64 4.2 On-Site Slab 69 4.2.1 Digital Twin Generation 69 4.2.2 Segmentation 71 4.2.3 Inspection 72 4.2.4 Issue Identification without BIM Models 74 4.3 On-Site Column and Wall 77 4.3.1 Digital Twin Generation 77 4.3.2 Localization 79 4.3.3 Segmentation, Inspection, and Issue Identification 81 4.4 Discussion 85 4.4.1 Improvements 86 4.4.2 Limitations 89 4.4.3 Advancements in Rebar DQC 90 Chapter 5 Conclusions and Future Work 97 5.1 Conclusions 97 5.2 Future Work 100 References 102 | |
dc.language.iso | en | |
dc.title | 運用數位孿生與深度學習之端對端鋼筋查驗框架 | zh_TW |
dc.title | End-to-End Rebar Inspection Framework using Digital Twins and Deep Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林之謙(Je-Chian Lin),周頌安(Sung-An Chou) | |
dc.subject.keyword | 端對端,鋼筋查驗,尺寸性品質管制,數位孿生,深度學習,電腦視覺,建築資訊塑模, | zh_TW |
dc.subject.keyword | end-to-end,rebar inspection,dimensional quality control,digital twin,deep learning,computer vision,building information modeling, | en |
dc.relation.page | 107 | |
dc.identifier.doi | 10.6342/NTU202203585 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2022-09-21 | |
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 | 2025-10-01 | - |
顯示於系所單位: | 土木工程學系 |
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