請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85983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 | |
| dc.identifier.citation | Concrete Reinforcing Steel Institute-CRSI, Field Inspection of Reinforcing Bars (CRSI Technical Note CTN-M-1-11). Schaumburg, Illinois: Concrete Reinforcing Steel Institute, 2011, p. 8. 監造計畫暨品質計畫製作綱要, 行政院公共工程委員會, 2020. [Outline of supervision plan and outline of quality control plan, Department of Construction Management of Public Construction Commission of Executive Yuan in Taiwan, 2020.] 何姓溪滯洪池新建工程品質計畫, 經濟部水利署第二河川局, 2019. [Quality Control Plan of Hehsing River Retention Pond Construction Project, Second River Management Office of Water Resource Agency of Ministry of Economic Affairs in Taiwan, 2019.] 林信璁, '台灣營建業專業技術工種缺工問題之研究-以建築工程為例,' 碩士論文, 國立中央大學, 2014. [S.-T. Lin, 'A study on technical worker shortage in Taiwan's construction industry: A case study of architectural engineering,' M.S. thesis, National Central University, 2014.] 中華民國內政部營建署, '營造業經濟概況調查,' 2020. [Construction and Planning Agency Ministry of the Interior in Taiwan, 'The General Economic Survey Report of Construction Industry,' 2020.] R. Sacks, M. Girolami, and I. Brilakis, 'Building information modelling, artificial intelligence and construction tech,' Developments in the Built Environment, vol. 4, p. 100011, 2020. F. Tao et al., 'Digital twin-driven product design framework,' International Journal of Production Research, vol. 57, no. 12, pp. 3935-3953, 2019. S.-J. Chuang, 'Rebar Spacing Recognition with Instance Segmentation and Active Learning,' M.S. thesis, National Taiwan University, 2021. F. Bosché, M. Ahmed, Y. Turkan, C. T. Haas, and R. Haas, 'The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components,' Automation in Construction, vol. 49, pp. 201-213, 2015. M. Golparvar-Fard, B. Ghadimi, K. S. Saidi, G. S. Cheok, M. Franaszek, and R. R. Lipman, 'Image-based 3D mapping of rebar location for automated assessment of safe drilling areas prior to placing embedments in concrete bridge decks,' in Construction Research Congress 2012: Construction Challenges in a Flat World, 2012, pp. 960-970. Z. Xu, R. Kang, and R. Lu, '3D reconstruction and measurement of surface defects in prefabricated elements using point clouds,' Journal of Computing in Civil Engineering, vol. 34, no. 5, p. 04020033, 2020. Q. Wang, M.-K. Kim, J. C. Cheng, and H. Sohn, 'Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning,' Automation in Construction, vol. 68, pp. 170-182, 2016. M.-K. Kim, H. Sohn, and C.-C. Chang, 'Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning,' Automation in Construction, vol. 45, pp. 163-177, 2014. M.-K. Kim, Q. Wang, J.-W. Park, J. C. Cheng, H. Sohn, and C.-C. Chang, 'Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM,' Automation in Construction, vol. 72, pp. 102-114, 2016. Q. Wang, J. C. Cheng, and H. Sohn, 'Automated estimation of reinforced precast concrete rebar positions using colored laser scan data,' Computer‐Aided Civil and Infrastructure Engineering, vol. 32, no. 9, pp. 787-802, 2017. S. Yoon, Q. Wang, and H. Sohn, 'Optimal placement of precast bridge deck slabs with respect to precast girders using 3D laser scanning,' Automation in construction, vol. 86, pp. 81-98, 2018. R. Maalek, 'Field Information Modeling (FIM)™: Best Practices Using Point Clouds,' Remote Sensing, vol. 13, no. 5, p. 967, 2021. R. Maalek, D. D. Lichti, and J. Y. Ruwanpura, 'Robust segmentation of planar and linear features of terrestrial laser scanner point clouds acquired from construction sites,' Sensors, vol. 18, no. 3, p. 819, 2018. R. Maalek, D. D. Lichti, and J. Y. Ruwanpura, 'Automatic recognition of common structural elements from point clouds for automated progress monitoring and dimensional quality control in reinforced concrete construction,' Remote Sensing, vol. 11, no. 9, p. 1102, 2019. R. Maalek, D. D. Lichti, R. Walker, A. Bhavnani, and J. Y. Ruwanpura, 'Extraction of pipes and flanges from point clouds for automated verification of pre-fabricated modules in oil and gas refinery projects,' Automation in Construction, vol. 103, pp. 150-167, 2019. K. Ishida, N. Kano, and K. Kimoto, 'Shape recognition with point clouds in rebars,' in ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 2012, vol. 29: IAARC Publications, p. 1. M.-K. Kim, J. P. P. Thedja, H.-L. Chi, and D.-E. Lee, 'Automated rebar diameter classification using point cloud data based machine learning,' Automation in Construction, vol. 122, p. 103476, 2021. M.-K. Kim, J. P. P. Thedja, and Q. Wang, 'Automated dimensional quality assessment for formwork and rebar of reinforced concrete components using 3D point cloud data,' Automation in construction, vol. 112, p. 103077, 2020. K. Nishio, N. Nakamura, Y. Muraki, and K.-i. Kobori, 'A method of core wire extraction from point cloud data of rebar,' 2017. M. Ahmed, C. T. Haas, and R. Haas, 'Autonomous modeling of pipes within point clouds,' in ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 2013, vol. 30: IAARC Publications, p. 1. M. F. Ahmed, C. T. Haas, and R. Haas, 'Automatic detection of cylindrical objects in built facilities,' Journal of Computing in Civil Engineering, vol. 28, no. 3, p. 04014009, 2014. C. Kim and H. Son, 'Knowledge-based approach for 3D reconstruction of as-built industrial plant models from laser-scan data,' in ISARC 2013-30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress, 2013, pp. 885-893. H. Son, C. Kim, and C. Kim, 'Fully automated as-built 3D pipeline extraction method from laser-scanned data based on curvature computation,' Journal of Computing in Civil Engineering, vol. 29, no. 4, p. B4014003, 2015. Y. Furukawa and C. Hernández, 'Multi-view stereo: A tutorial,' Foundations and Trends® in Computer Graphics and Vision, vol. 9, no. 1-2, pp. 1-148, 2015. S. Agarwal et al., 'Building rome in a day,' Communications of the ACM, vol. 54, no. 10, pp. 105-112, 2011. N. Snavely, S. M. Seitz, and R. Szeliski, 'Photo tourism: exploring photo collections in 3D,' in ACM siggraph 2006 papers, 2006, pp. 835-846. N. Snavely, S. M. Seitz, and R. Szeliski, 'Modeling the world from internet photo collections,' International journal of computer vision, vol. 80, no. 2, pp. 189-210, 2008. C. Zhang, 'Surface Defect Detection, Segmentation and Quantification for Concrete Bridge Assessment Using Deep Learning and 3D Reconstruction,' Ph.D. dissertation, Civil and Environmental Engineering, The Hong Kong University of Science and Technology, 2020. M. Akula, R. R. Lipman, M. Franaszek, K. S. Saidi, G. S. Cheok, and V. R. Kamat, 'Real-time drill monitoring and control using building information models augmented with 3D imaging data,' Automation in Construction, vol. 36, pp. 1-15, 2013. K. Han et al., 'Vision-based field inspection of concrete reinforcing bars,' in Proceedings of the 13th International Conference on Construction Applications of Virtual Reality, London, UK, 2013, pp. 30-31. 陳翊翔 et al., '以深度學習與數位孿生打造工地鋼筋查驗新法,' 土木水利, vol. 48, no. 2, pp. 15-21, 2021. [Y.-H. Chen et al., 'Novel rebar inspection using deep learning and digital twin,' The Magazine of The Chinese Institute of Civil and Hydraulic Engineering, vol. 48, no. 2, pp. 15-21, 2021.] T.-W. Huang, 'Improving on-site reinforcing bar inspection with deep learning and digital twin ' B.S. thesis, National Taiwan University, 2021. A. Nguyen and B. Le, '3D point cloud segmentation: A survey,' in 2013 6th IEEE conference on robotics, automation and mechatronics (RAM), 2013: IEEE, pp. 225-230. M. A. Fischler and R. C. Bolles, 'Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,' Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981. P. V. C. Hough, 'Method and Means for Recognizing Complex Patterns,' (in English), United States, 1962. [Online]. Available: https://www.osti.gov/servlets/purl/4746348 M. Bueno, L. Díaz-Vilariño, H. González-Jorge, J. Martínez-Sánchez, and P. Arias, 'Quantitative evaluation of CHT and GHT for column detection under different conditions of data quality,' Journal of Computing in Civil Engineering, vol. 31, no. 5, p. 04017032, 2017. L. Díaz-Vilariño, B. Conde, S. Lagüela, and H. Lorenzo, 'Automatic detection and segmentation of columns in as-built buildings from point clouds,' Remote Sensing, vol. 7, no. 11, pp. 15651-15667, 2015. K. He, G. Gkioxari, P. Dollár, and R. Girshick, 'Mask r-cnn,' in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961-2969. S. Ren, K. He, R. Girshick, and J. Sun, 'Faster r-cnn: Towards real-time object detection with region proposal networks,' Advances in neural information processing systems, vol. 28, 2015. Y. Kardovskyi and S. Moon, 'Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision,' Automation in Construction, vol. 130, p. 103850, 2021. H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, 'Point transformer,' in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 16259-16268. X. Zhang et al., 'A high precision quality inspection system for steel bars based on machine vision,' Sensors, vol. 18, no. 8, p. 2732, 2018. M.-K. Kim, J. C. Cheng, H. Sohn, and C.-C. Chang, 'A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning,' Automation in Construction, vol. 49, pp. 225-238, 2015. National Taiwan University Research Center for Building & Infrastructure Information Modeling and Management, Level of Development Specification (V.2017), 2017. J. L. Schonberger and J.-M. Frahm, 'Structure-from-motion revisited,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4104-4113. J. L. Schönberger, E. Zheng, J.-M. Frahm, and M. Pollefeys, 'Pixelwise view selection for unstructured multi-view stereo,' in European Conference on Computer Vision, 2016: Springer, pp. 501-518. E. Olson, 'AprilTag: A robust and flexible visual fiducial system,' in 2011 IEEE international conference on robotics and automation, 2011: IEEE, pp. 3400-3407. J. Wang and E. Olson, 'AprilTag 2: Efficient and robust fiducial detection,' in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016: IEEE, pp. 4193-4198. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, 'A density-based algorithm for discovering clusters in large spatial databases with noise,' in kdd, 1996, vol. 96, no. 34, pp. 226-231. R. B. Rusu, N. Blodow, and M. Beetz, 'Fast point feature histograms (FPFH) for 3D registration,' in 2009 IEEE international conference on robotics and automation, 2009: IEEE, pp. 3212-3217. P. J. Besl and N. D. 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.subject | 尺寸性品質管制 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 端對端 | zh_TW |
| dc.subject | 建築資訊塑模 | zh_TW |
| dc.subject | 電腦視覺 | zh_TW |
| dc.subject | 數位孿生 | zh_TW |
| dc.subject | 鋼筋查驗 | zh_TW |
| dc.subject | dimensional quality control | en |
| dc.subject | building information modeling | en |
| dc.subject | computer vision | en |
| dc.subject | deep learning | en |
| dc.subject | digital twin | en |
| dc.subject | rebar inspection | en |
| dc.subject | end-to-end | 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1909202216162100.pdf | 32.53 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。