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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張家銘 | zh_TW |
dc.contributor.advisor | Chia-Ming Chang | en |
dc.contributor.author | 楊耀畬 | zh_TW |
dc.contributor.author | Yao-Yu Yang | en |
dc.date.accessioned | 2024-01-26T16:20:36Z | - |
dc.date.available | 2024-01-27 | - |
dc.date.copyright | 2024-01-26 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-01-11 | - |
dc.identifier.citation | 1. Kim, S., S. Chang, and D. Castro-Lacouture, Dynamic modeling for analyzing impacts of skilled labor shortage on construction project management. Journal of Management in Engineering, 2020. 36(1): p. 04019035.
2. Karthick, S., et al., A review of construction workforce health challenges and strategies in extreme weather conditions. International Journal of Occupational Safety and Ergonomics, 2023. 29(2): p. 773-784. 3. Yeh, F.-Y., et al., A novel composite bridge for emergency disaster relief: Concept and verification. Composite Structures, 2015. 127(Supplement C): p. 199-210. 4. Vincent Viscomi, B., W.D. Michalerya, and L.-W. Lu, Automated construction in the ATLSS integrated building systems. Automation in Construction, 1994. 3(1): p. 35-43. 5. Yamazaki, Y. and J. Maeda, The SMART system: an integrated application of automation and information technology in production process. Computers in Industry, 1998. 35(1): p. 87-99. 6. Kim, C.-W., et al., Advanced steel beam assembly approach for improving safety of structural steel workers. Journal of Construction Engineering and Management, 2016. 142(4). 7. Liang, C.-J., S.-C. Kang, and M.-H. Lee, RAS: a robotic assembly system for steel structure erection and assembly. International Journal of Intelligent Robotics and Applications, 2017. 8. Scholl, M. kranXpert Software. May, 2022]; Available from: https://www.kranxpert.de/. 9. Lin, K.-L. and C.T. Haas, Multiple Heavy Lifts Optimization. Journal of Construction Engineering and Management, 1996. 122(4): p. 354-362. 10. Varghese, K., et al., A Heavy Lift Planning System for Crane Lifts. Computer-Aided Civil and Infrastructure Engineering, 1997. 12(1): p. 31-42. 11. MinayHashemi, S., et al., Automated rigging design for heavy industrial lifts. Automation in Construction, 2020. 112: p. 103083. 12. Rosignoli, M., Chapter 27 - Bridge construction equipment, in Innovative Bridge Design Handbook, A. Pipinato, Editor. 2016, Butterworth-Heinemann: Boston. p. 701-717. 13. André, J., R. Beale, and A. Baptista, Bridge Construction Equipment: An Overview of the Existing Design Guidance. Structural Engineering International, 2012. 22(3): p. 365-379. 14. Ramli, L., et al., Control strategies for crane systems: a comprehensive review. Mechanical Systems and Signal Processing, 2017. 95: p. 1-23. 15. Sadeghi, S., N. Soltanmohammadlou, and P. Rahnamayiezekavat, A systematic review of scholarly works addressing crane safety requirements. Safety Science, 2021. 133: p. 105002. 16. Tian, J., et al., Crane Lifting Optimization and Construction Monitoring in Steel Bridge Construction Project Based on BIM and UAV. Advances in Civil Engineering, 2021. 2021: p. 5512229. 17. Hu, S., Y. Fang, and Y. Bai, Automation and optimization in crane lift planning: a critical review. Advanced Engineering Informatics, 2021. 49: p. 101346. 18. Cho, S. and S. Han, Reinforcement learning-based simulation and automation for tower crane 3D lift planning. Automation in Construction, 2022. 144: p. 104620. 19. Sakawa, Y., Y. Shindo, and Y. Hashimoto, Optimal control of a rotary crane. Journal of Optimization Theory and Applications, 1981. 35(4): p. 535-557. 20. Terashima, K., Y. Shen, and K.i. Yano, Modeling and optimal control of a rotary crane using the straight transfer transformation method. Control Engineering Practice, 2007. 15(9): p. 1179-1192. 21. Maleki, E. and W. Singhose, Dynamics and control of a small-scale boom crane. Journal of Computational and Nonlinear Dynamics, 2011. 6(3). 22. Maleki, E. and W. Singhose, Swing dynamics and input-shaping control of human-operated double-pendulum boom cranes. Journal of Computational and Nonlinear Dynamics, 2012. 7(3). 23. Uchiyama, N., H. Ouyang, and S. Sano, Simple rotary crane dynamics modeling and open-loop control for residual load sway suppression by only horizontal boom motion. Mechatronics, 2013. 23(8): p. 1223-1236. 24. Abe, A. and K. Okabe, Antisway control for a rotary crane by using evolutionary computation. Journal of Robotics and Mechatronics, 2016. 28(5): p. 646-653. 25. Alhassan, A., et al., Input shaping techniques for sway control of a rotary crane system. Jurnal Teknologi, 2017. 80(1). 26. Uchiyama, N., S. Takagi, and S. Sano. Robust control of rotary cranes based on pole placement approach. in Advanced Motion Control, 2006. 9th IEEE International Workshop on. 2006. IEEE. 27. Liu, Z., et al., Nonlinear sliding mode tracking control of underactuated tower cranes. International Journal of Control, Automation and Systems, 2021. 19(2): p. 1065-1077. 28. Huang, J., E. Maleki, and W. Singhose, Dynamics and swing control of mobile boom cranes subject to wind disturbances. IET Control Theory & Applications, 2013. 7(9): p. 1187-1195. 29. Masoud, Z.N., A.H. Nayfeh, and A. Al-Mousa, Delayed Position-Feedback Controller for the Reduction of Payload Pendulations of Rotary Cranes. Journal of Vibration and Control, 2003. 9(1-2): p. 257-277. 30. Lewis, F.L., D. Vrabie, and V.L. Syrmos, Optimal control. 2012: John Wiley & Sons. 31. Wu, Z. and T.T. Soong, Modified bang-bang control law for structural control implementation. Journal of Engineering Mechanics, 1996. 122(8): p. 771-777. 32. Padhi, R. and M. Kothari, Model predictive static programming: a computationally efficient technique for suboptimal control design. International Journal of Innovative Computing Information and Control, 2009. 5(2): p. 399-411. 33. Wang, Y. and F. Topputo, Robust bang-off-bang low-thrust guidance using model predictive static programming. Acta Astronautica, 2020. 176: p. 357-370. 34. Fontes, F. and L. Magni, Min-max model predictive control of nonlinear systems using discontinuous feedbacks. IEEE Transactions on Automatic Control, 2003. 48(10): p. 1750-1755. 35. Crowe, C.T., D.F. Elger, and J.A. Roberson, Engineering fluid mechanics. Vol. 9. 2005: Wiley. 36. Lin, Y.-C., et al., Estimation of high discharge using measured surface velocity. Journal of Chinese Soil and Water Conservation, 2011. 42(1): p. 23-36. 37. Costa, J.E., et al., measuring stream discharge by non‐contact methods: A Proof‐of‐Concept Experiment. Geophysical Research Letters, 2000. 27(4): p. 553-556. 38. Costa, J., et al., Use of radars to monitor stream discharge by noncontact methods. Water Resources Research, 2006. 42(7). 39. Welber, M., et al., Field assessment of noncontact stream gauging using portable surface velocity radars (SVR). Water Resources Research, 2016. 40. Lee, M.-C., Development of non-contact methods for water surface velocity and river discharge measurements, in Hydraulic and Ocean Engineering. 2003, National Cheng Kung University. 41. Adrian, R.J., Particle-imaging techniques for experimental fluid mechanics. Annual review of fluid mechanics, 1991. 23(1): p. 261-304. 42. Grant, I., Particle image velocimetry: a review. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 1997. 211(1): p. 55-76. 43. Hart, D.P., PIV error correction. Experiments in fluids, 2000. 29(1): p. 13-22. 44. Yu, H. and Q. Lv. Study on particle image velocimetry technique in the surface flow field of river model. in International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC). 2013. 45. Ishikawa, M., et al. PIV measurement of a contraction flow using micro-bubble tracer. in Journal of Physics: Conference Series. 2009. IOP Publishing. 46. Cameron, S.M., PIV algorithms for open-channel turbulence research: accuracy, resolution and limitations. Journal of Hydro-environment Research, 2011. 5(4): p. 247-262. 47. Fujita, I., M. Muste, and A. Kruger, Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications. Journal of hydraulic Research, 1998. 36(3): p. 397-414. 48. Jasek, M., M. Muste, and R. Ettema, Estimation of Yukon River discharge during an ice jam near Dawson City. Canadian Journal of Civil Engineering, 2001. 28(5): p. 856-864. 49. Bradley, A.A., et al., Flow measurement in streams using video imagery. Water Resources Research, 2002. 38(12). 50. Creutin, J., et al., River gauging using PIV techniques: a proof of concept experiment on the Iowa River. Journal of Hydrology, 2003. 277(3): p. 182-194. 51. Muste, M., I. Fujita, and A. Hauet, Large‐scale particle image velocimetry for measurements in riverine environments. Water resources research, 2008. 44(4). 52. Admiraal, D.M., J.S. Stansbury, and C.J. Haberman, Case study: Particle velocimetry in a model of lake Ogallala. Journal of Hydraulic Engineering, 2004. 130(7): p. 599-607. 53. Sun, X., et al., Discharge estimation in small irregular river using LSPIV. Proceedings of the Institution of Civil Engineers: Water Management, 2010. 163(5): p. 247-254. 54. Gunawan, B., et al., The application of LS-PIV to a small irregular river for inbank and overbank flows. Flow Measurement and Instrumentation, 2012. 24: p. 1-12. 55. Fujita, I., H. Watanabe, and R. Tsubaki, Development of a non‐intrusive and efficient flow monitoring technique: The space‐time image velocimetry (STIV). International Journal of River Basin Management, 2007. 5(2): p. 105-114. 56. Fujita, I. and T. Hino, Unseeded and seeded PIV measurements of river flows videotaped from a helicopter. Journal of Visualization, 2003. 6(3): p. 245-252. 57. Hauet, A., et al., Experimental system for real-time discharge estimation using an image-based method. Journal of Hydrologic Engineering, 2008. 13(2): p. 105-110. 58. Kim, Y., et al., Stream discharge using mobile large‐scale particle image velocimetry: A proof of concept. Water Resources Research, 2008. 44(9). 59. Fujita, I. and Y. Kunita, Application of aerial LSPIV to the 2002 flood of the Yodo River using a helicopter mounted high density video camera. Journal of Hydro-environment Research, 2011. 5(4): p. 323-331. 60. Detert, M. and V. Weitbrecht, A low-cost airborne velocimetry system: proof of concept. Journal of Hydraulic Research, 2015. 53(4): p. 532-539. 61. Tsubaki, R., I. Fujita, and S. Tsutsumi, Measurement of the flood discharge of a small-sized river using an existing digital video recording system. Journal of Hydro-Environment Research, 2011. 5(4): p. 313-321. 62. Li, D.X., et al., Large-scale particle tracking velocimetry with multi-channel CCD cameras. International Journal of Sediment Research, 2013. 28(1): p. 103-110. 63. Zhang, Z., et al., River surface target enhancement and background suppression for unseeded LSPIV. Flow Measurement and Instrumentation, 2013. 30: p. 99-111. 64. Wang, X., et al., Balloon-borne spectrum–polarization imaging for river surface velocimetry under extreme conditions. Infrared Physics & Technology, 2013. 58: p. 5-11. 65. Kalman, R.E. and R.S. Bucy, New results in linear filtering and prediction theory. Journal of Basic Engineering, 1961. 83(1): p. 95-108. 66. Åström, K.J., Introduction to stochastic control theory. 2012: Courier Corporation. 67. Nezu, I. and M. Sanjou, PIV and PTV measurements in hydro-sciences with focus on turbulent open-channel flows. Journal of Hydro-environment Research, 2011. 5(4): p. 215-230. 68. Healey, C. and J. Enns, Attention and visual memory in visualization and computer graphics. IEEE Transactions on Visualization and Computer Graphics, 2012. 18(7): p. 1170-1188. 69. Fujita, I., Y. Kunita, and R. Tsubaki, Image analysis and reconstruction of the 2008 Toga River Flash Flood in an urbanised area. Australian Journal of Water Resources, 2013. 16(2): p. 151-162. 70. Le Boursicaud, R., et al., Gauging extreme floods on YouTube: application of LSPIV to home movies for the post‐event determination of stream discharges. Hydrological Processes, 2016. 30(1): p. 90-105. 71. Le Coz, J., et al., Crowdsourced data for flood hydrology: Feedback from recent citizen science projects in Argentina, France and New Zealand. Journal of Hydrology, 2016. 541: p. 766-777. 72. Vičan, J. and M. Farbák, Analysis of high - strength steel pin connection. Civil and Environmental Engineering, 2020. 16(2): p. 276-281. 73. Autodesk. Autodesk. 2022 [cited 2022 Aug. 4]; Available from: www.autodesk.com. 74. Unity. Unity Real-Time Development Platform. 2022 [cited 2022 July 20]; Available from: https://unity.com/. 75. VirtualMethod. Filo - The Cable Simulator. 2022 [cited 2022 July 20]; Available from: https://assetstore.unity.com/packages/tools/physics/filo-the-cable-simulator-133620. 76. Rusik3Dmodels. Crane Simulator. 2022 [cited 2022 July 20]; Available from: https://assetstore.unity.com/packages/3d/vehicles/land/crane-simulator-134402. 77. Construction, A.I.o.S., Companion to the AISC STEEL CONSTRUCTION MANUAL. 15.1 ed. 2019. 78. Mohan, N., Electric machines and drives: a first course. 2012: Wiley. 79. Anderson, B.D. and J.B. Moore, Optimal control: linear quadratic methods. 2007: Courier Corporation. 80. Hahn, W., H.H. Hosenthien, and H. Lehnigk, Theory and application of Liapunov''s direct method. Vol. 3. 1963: prentice-hall Englewood Cliffs. 81. Zak, S.H., Systems and control. Vol. 198. 2003: Oxford University Press New York. 82. Hiwin. HIWIN MIKROSYSTEM. 2022 [cited 2022 Aug. 13]; Available from: https://www.hiwinmikro.tw/en/. 83. Kim, S.B., B.F. Spencer, and C.-B. Yun, Frequency domain identification of multi-input, multi-output systems considering physical relationships between measured variables. Journal of Engineering Mechanics, 2005. 131(5): p. 461-472. 84. Abdel-Aziz, Y. Direct linear transformation from comparator coordinates in close-range photogrammetry. in ASP Symposium on Close-Range Photogrammetry in Illinois. 1971. 85. Canny, J., A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, 1986(6): p. 679-698. 86. Wu, H.-Y., et al., Eulerian video magnification for revealing subtle changes in the world. 2012. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91401 | - |
dc.description.abstract | 便橋建造對於救災來說相當重要。而因為技術人力短缺與極端氣候的影響,施工自動化科技需要被應用在橋梁建造上。傳統的快速橋梁建造法使用輕質材料與螺栓連接之節塊結構,搭配許多人力與機具的使用完成一個快速建造的節塊橋樑。然而,不管是人力還是機具,兩者在救災時都難以取得。另外,一個自動化的橋梁建造工程需要有河流觀測系統來觀測河流狀態,以防河流變化造成的災害,但河流流速特徵難以捉摸,每個河流都要客製化找出該河流的特徵來做流速分析。為了要解決上述施工自動化與河流觀測的問題,本研究開發了一個結構接頭設計搭配一個特殊施工法、一個吊車控制方法來減緩被吊物也就是橋梁節塊的擺盪,和一個使用群眾來觀測河流流速之方法。數值模型與小型實驗模型驗證了本研究提出的結構接頭設計與吊車控制方法。再者,簡化後的吊車控制法則以伸縮式吊車驗證;最後流速測量法則以一群受試者測試其可行性。本研究提出之自動化橋梁建造系統利用了創新橋梁接頭設計來達到自動化組裝橋梁節塊的目的、減少吊車吊裝節塊時的晃動來減低被吊物碰撞與掉落風險、監控河流流速狀況來警示是否該撤離該施工地段。 | zh_TW |
dc.description.abstract | Temporary bridge construction is essential in disaster relief. Due to skilled worker shortage and adverse weather conditions, the bridge construction needs construction automation technology. The conventional temporary bridge construction uses light-weighted material and bolt-connected segmental structures and employs many construction workers and machines. Additionally, an automated bridge construction requires a river observation system to monitor the river flow condition in case of immediate evacuation. To resolve these problems, this research developed a structural connector design with a specific construction method, a crane control method reducing the payload sway, and a flow velocity measurement approach with crowd-sourcing. The numerical simulation and scaled experiment verifies the designed structural connector and the control method. Moreover, the simplified control method has been tested on a telescopic crane. The developed flow velocity measurement method is also verified by a user test. The proposed automated bridge construction system takes advantage of the connector design to assemble the bridge segments, reduces the segment sway to mitigate the construction risks, and monitors the river flows effectively. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-26T16:20:36Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-01-26T16:20:36Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Background 1 1.2 Literature review 4 1.2.1 Automated construction 4 1.2.2 Crane sway reduction 6 1.2.3 Velocity measurement for river surface flows 8 1.3 Objectives 11 Chapter 2 Gravity-Triggered Rotational Connecting Method for Automated Segmental Bridge Construction 14 2.1 Gravity-Triggered Rotational Connecting Method 15 2.1.1 Connector design 17 2.1.2 Rigging assessment 23 2.1.3 Crane path planning 24 2.1.4 Overall design and construction process 32 2.2 Example of Gravity-Triggered Rotational Connecting Method for a Segmental Bridge 35 2.2.1 Connector design 39 2.2.2 Finite element analysis 43 2.2.3 Rigging assessment 45 2.2.4 Crane path planning 47 2.2.5 Discussions for the bridge example 53 2.3 Summary 54 Chapter 3 Bang-Off-Bang Model-Based Predictive Control with Acceleration Feedback for Rotary Cranes 56 3.1 Problem Formulation 57 3.2 Control design 60 3.2.1 Output shaping filter 61 3.2.2 Kalman filter 62 3.2.3 Model-based predictor 63 3.2.4 Stability analysis 64 3.3 Numerical verification 65 3.3.1 BOBMPC performance under different initial conditions 66 3.3.2 Filter performance 69 3.4 Laboratory-scale experiment 71 3.5 BOBMPC simplification and verification 81 3.5.1 Controller simplification 81 3.5.2 Numerical verification 83 3.5.3 Full-scale experiment 87 3.6 Summary 91 Chapter 4 Crowd-Based Velocimetry for Surface Flows 93 4.1 Crowd-Based Velocimetry 94 4.1.1 Video processing 96 4.1.2 Crowd processing using FlowScope 99 4.1.3 Statistical processing 101 4.2 Experiments and Results 102 4.2.1 Video acquisition 102 4.2.2 PIV analysis 103 4.2.3 CBV analysis 104 4.3 Discussion and summary 114 4.3.1 Discussion 114 4.3.2 Summary 116 Chapter 5 Conclusions and future studies 117 5.1 Conclusions 117 5.2 Future studies 119 Reference 122 | - |
dc.language.iso | en | - |
dc.title | 自動化施工: 橋樑節塊組裝與流速監控 | zh_TW |
dc.title | Automated Construction for Segmental Bridge and River Flow Monitoring | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 博士 | - |
dc.contributor.oralexamcommittee | 康仕仲;林沛群;張國鎮;葉芳耀 | zh_TW |
dc.contributor.oralexamcommittee | Shih-Chung Kang;Pei-Chun Lin;Kuo-Chun Chang;Fang-Yao Yeh | en |
dc.subject.keyword | 自動化施工,吊車控制,水流測速,結構接頭,橋梁自動化組裝, | zh_TW |
dc.subject.keyword | automated construction,crane control,flow velocity measurement,structural connector,automated bridge assembly, | en |
dc.relation.page | 126 | - |
dc.identifier.doi | 10.6342/NTU202400072 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2024-01-12 | - |
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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