以可靠度劣化預測模式評估橋梁構件生命週期維護成本之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：34

、訪客IP：18.119.107.96

姓名

毛一祥(I-shiang Mao) 查詢紙本館藏

畢業系所

營建管理研究所

論文名稱

以可靠度劣化預測模式評估橋梁構件生命週期維護成本之研究
(Lifecycle Assessment of Maintenance, Repair and Rehabilitation Costs: A Reliability Based Deterioration Modeling Approach for Bridge Components)

相關論文

★ 台灣太陽能光電系統故障原因調查與安裝施工檢查表製作之研究	★ 生命週期導向橋梁改善策略最佳化評選模式之研究
★ 國內建築廢棄物減量措施之分析探討及其成效評估之研究	★ 應用賽局理論分析高科技廠房工程競爭行為之研究
★ 橋梁維護管理生命週期成本評估模式之研究	★ 營建剩餘土石方及混合物處理與再利用法制化之研究
★ 建築工程產生廢棄物數量推估之研究	★ 橋梁生命週期成本評估-構件劣化預測模式之研究
★ 國道預力混凝土橋與鋼橋生命週期成本評估個案之研究	★ 單一建築物拆除工程混合物產生量推估之研究
★ 裝修工程廢棄物回收站與再利用廠設置最佳化區位評選之研究	★ 精簡營建應用於鋼構工程供應鏈之研究
★ 營建剩餘土石方評估指標建立及成效提升之研究	★ 建築廢棄物收容處理場所設置最佳區位評選之研究
★ 營建再生料源資訊交換網建置之研究	★ 運用資料探勘技術於臺灣鋼筋混凝土橋梁構件劣化因子之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

橋梁生命週期中的營運維護費用，可透過大量數據的蒐集、建立合理客觀的劣化預測模式及採用適當的資料處理方法，而得到較客觀的估計。本研究將以一系統性的分析流程，先找出影響各橋梁構件之劣化因子，並以該因子所組成之屬性條件，去篩選出資料庫中與目標橋梁具有相近劣化行為之代表橋群。再由橋梁檢測值去定義一新的構件狀況指標(NCI)，同時導入可靠度指標(Beta)的概念，將橋梁構件劣化程度予以量化。因此，利用所得代表橋群構件之檢測資料，即可歸納出目標橋梁之劣化模式。最後，再以該模式推估橋梁營運時期之維修成本。本研究成果依序主要達成以下目標：1.找出橋梁構件劣化之關鍵因子；2.建立以劣化因子為導向之橋群檢索系統；3.建立橋梁構件之劣化預測模式；4.估算橋梁生命週期之維修成本。

摘要(英)

Bridge MR&R costs can be more objectively estimated if adequate historical data are collected and well processed, also a proper deterioration model is adopted. This study will first demonstrate a systematic approach to explore the key factors leading to bridge deterioration. The representative samples of bridges with similar behaviors of deterioration are then classified by the identified factors. A new condition index (NCI) is defined and the reliability index (Beta) is introduced to measure the deterioration quantitatively. The deterioration trend of the representative samples can then be determined. Finally, the maintaining cost of a bridge with the same deterioration behavior can be estimated. This study achieved the following objects: 1. Identifying the key factors leading to deterioration towards bridge elements; 2. Developing a bridge matching system to retrieve bridge samples by their attributes; 3. Developing a reliability-based deterioration model of bridge elements; 4. Demonstrating a systematic approach to estimate the MR&R cost of bridges.

關鍵字(中)

★ 橋梁
★ 劣化
★ 可靠度指標
★ 維修成本

關鍵字(英)

★ Bridge
★ Deterioration
★ Reliability Index
★ MR&R Cost

論文目次

第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的及重要性 3
1.3 研究方法與流程 4
1.4 論文架構 7
第二章過去相關研究 8
2.1 生命週期成本研究 8
2.2 維護費用評估研究 10
2.3 檢測資料庫之建立 12
2.4 劣化因子研究 13
2.5 橋梁劣化模式研究 15
2.6 資料庫知識發掘與資料探勘技術之應用 19
2.7 小結 21
第三章模型發展與建置方法 23
3.1 問題解構與研究流程 23
3.2 樣本屬性資料整理 27
3.3 尋找相似樣本群 32
3.4 建構劣化預測模型 34
3.5 模擬維護作業 37
3.6 產生維護成本 41
3.7 維護效益之評估 41
3.8 劣化及維護歷程模擬 43
3.9 小結 45
第四章資料庫之建置與操作 47
4.1 橋梁樣本資料蒐集 47
4.2 橋梁屬性資料整理 48
4.3 相似橋群之篩選方法 52
4.4 橋梁檢測資料整理 59
4.5 維護成本資料整理 61
第五章案例演算與驗證 64
5.1 關鍵劣化因子之萃取 64
5.2 演算案例條件設定及橋群篩選 67
5.3 構件劣化預測模型 68
5.4 維護等級啟動及維護效益之模擬 73
5.5 蒙地卡羅模擬與成本計算 76
5.6 模型趨勢驗證 83
5.7 模型實例比較驗證 84
5.8 小結 86
第六章結論與建議 88
6.1 結論 88
6.2 建議 90
參考文獻 93

參考文獻

[中文]
1.林志棟、李孝安，「公共工程維護管理經費編審及查核制度之研究」，財團法人台灣營建研究院，2002。
2.倪茂榮，「中山高速公路汐止五股高架拓寬段大貨車通行管制載重之研究」，國立台灣大學土木工程學系碩士論文，1996。
3.國立中央大學土木工程學系橋梁工程研究中心等，「台灣地區橋梁管理系統-橋梁基本資料建立說明手冊」，2000。
4.黃榮堯、許凱麟、王仲宇、廖肇昌、陳純敬、顏上堯、張國鎮、李俊憲、謝斌麒、吳伋，「橋梁生命週期成本評估方法與結構使用年限之建立」，交通部科技顧問室成果報告(1/2)、(2/2)，2002-2003。
5.黃榮堯，「橋梁生命週期管理與成本分析之研究」，成果報告，財團法人中華顧問工程司，2012
6.楊熾宗，「公路橋樑維護管理決策模式之研究-以中山高速公路為例」，國立臺灣大學土木工程研究所碩士論文，1998。
7.蘇暉傑，「既有橋梁損壞相關影響因素之研究－以台中縣為例」，逢甲大學碩士論文，2003。
8.陳添宇，「類神經網路於橋梁老劣化之研究」，國立台北科技大學碩士論文，2005。
9.許文政，「橋梁生命週期成本評估構件劣化預測模式之研究」，國立中央大學碩士論文，2005。
10.廖先格，「橋梁目視檢測自動化系統之研究」，國立中央大學碩士論文，2005。
11.李豪剛，「運用資料探勘技術於臺灣鋼筋混凝土橋梁構件劣化因子之研究」，國立中央大學碩士論文，2007。

[英文]
1.AASHTO LRFD Bridge Design Specifications, 6th edition, 2012, Washington, DC.
2.AASHTO, Pontis Release 4.4 User′s Manual, 2005, Washington, DC.
3.ASTM E833-13b, Standard Terminology of Building Economics, Nov. 2013.
4.ASTM E917-94, Standard practice for measuring life-cycle costs of buildings and building systems, Nov. 1994.
5.Barco, A.L. (1994). Budgeting for facility repair and maintenance. Journal of Management in Engineering, Vol.10 No.4, pp. 28-34.
6.Bazan, J., Nguyen, H. S., Nguyen, S. H., Synak, P. and Wróblewski, J. (2000). Rough set algorithms in classification problem. In Polkowski, L., Tsumoto, S. and Lin, T., editors, Rough Set Methods and Applications, Physica-Verlag, Heidelberg New York, pp. 49–88.
7.Behmardi, B., Doolen, T. and Winston, H. (2013). Comparison of predictive cost models for bridge replacement projects. Journal of Management in Engineering, DOI: 10.1061/(ASCE)ME.1943-5479.0000269.
8.BLCCA (Bridge Life-Cycle Cost Analysis), NCHRP Report #483, Transportation Research Board, National Academies, 2003.
9.Catbasa, F.N., Susoyb M. and Frangopol, D.M. (2008). Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data. Engineering Structure, Vol. 30, pp. 2347-2359.
10.Chmielewski, M. R. and Grzymala-Busse, J. W. (1996). Global discretization of continuous attributes as preprocessing for machine learning. International Journal of Approximate Reasoning, Vol.15, pp.319-331.
11.Ehlen, M.A. (1997). Life-cycle cost of new construction materials. Journal of Infrastructure Systems, Vol. 3, No. 4, pp. 129-133.
12.Ehlen, M.A., BridgeLCC 2.0 Users Manual, NIST, 2003.
13.Energy Dept. of, U.S., Cost Estimating Guide, 1997.
14.Frangopol, D.M., Bridge Safety and Reliability, SEI & ASCE, 1999.
15.Frangopol, D.M., Kong, J.S. and Gharaibeh, E.S. (2001). Reliability-based life-cycle management of highway bridges. Journal of Computing in Civil Engineering, Vol. 15, No. 1, pp. 27-34.
16.Frangopol, D.M., Strauss, A. and Kim S. (2008). Use of monitoring extreme data for the performance prediction of structures: General approach. Engineering Structure, Vol. 30, pp. 3644-3653.
17.Frangopol, D.M., Strauss, A. and Kim S. (2008). Bridge reliability assessment based on monitoring. Journal of Bridge Engineering, Vol. 13, No. 3, pp. 258-270.
18.Freyermuth, C.L. (2001). Life-cycle cost analysis for large segmental bridges. Concrete International, Vol. 23, No. 2, pp. 89-95.
19.Gao, H. and Zhang, X. (2013). A Markov-based Road Maintenance Optimization Model Considering User Costs, Journal of Computer-Aided Civil and Infrastructure Engineering, 28(6), 451-464.
20.Han, J.W. and Kamber M., Data Mining: Concepts and Techniques, 3rd Ed., Morgan Kaufmann Publishers, 2011.
21.Hawk, H., and Small E. P. (1998). The BRIDGIT Bridge Management System. Structural Engineering International, Vol.8, No.4, pp. 309-314.
22.Huang, R. Y. (2006). A performance-based bridge LCCA model using visual inspection inventory data. Construction Management and Economics, Vol.132, No.10, pp.1069-81.
23.Huang, Y.H., Modelling Deterioration of Concrete Bridge Decks Using Neural Networks, PhD Thesis at UW, Madison, Wisconsin, 2003.
24.Kong, J.S. and Frangopol, D.M. (2003). Life-cycle reliability-based maintenance cost optimization of deteriorating structures with emphasis on bridges. Journal of Structural Engineering, Vol. 129, No.6, pp. 818-828.
25.MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, Vol.1, pp.281-297.
26.Marsha, P.S. and Frangopol, D.M. (2008). Reinforced concrete bridge deck reliability model incorporating temporal and spatial variations of probabilistic corrosion rate sensor data. Reliability Engineering and System Safety, Vol. 93, pp. 394-409.
27.Miyamoto, A., Kawamura, K. and Nukamura, H. (2001). Development of a bridge management for existing bridges. Advances in Engineering Software, Vol. 32, pp. 821-833.
28.Melhem, H.G., Cheng Y., Kossler D. and Scherschligt, D. (2003). Wrapper methods for inductive learning: example application to bridge decks. Journal of Computing in Civil Engineering, Vol. 17, No. 1, pp. 46-57.
29.Melhem, H.G. and Cheng Y. (2003). Prediction of remaining service life of bridge decks using machine learning. Journal of Computing in Civil Engineering, Vol. 17, No. 1, pp. 1-9.
30.Mondal, P. and DeWolf, J. T. (2007), Development of computer-based system for the temperature monitoring of a post-tensioned segmental concrete box-girder bridge, Computer-Aided Civil and Infrastructure Engineering, 22(1), pp. 65-77.
31.Morcous, G., Rivard, H., and Hanna, A.M. (2002). Modeling bridge deterioration using case-based reasoning. Journal of Infrastructure Systems, Vol. 8, No. 3, pp. 86-95.
32.Morcous, G., Rivard, H., and Hanna, A.M. (2002). Case-based reasoning system for modeling infrastructure deterioration. Journal of Computing in Civil Engineering, Vol. 16, No. 2, pp. 104-114.
33.Nguyen, H. S. (1998). Discretization Problem for Rough Sets Methods, Rough Sets and Current Trends in Computing. Proceedings of the 1st International Conference, RSCTC’98 Warsaw, Poland, June 22–26, pp.545-552.
34.Nowak, A.S. (1995). Calibration of LRFD bridge code. Journal of Structural Engineering, Vol. 121, No.8, pp. 1245-1251.
35.Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Science, Vol.11, No.5, pp.341-356.
36.Sanchez, R., Cost Estimating Guide. U.S. Department of Energy, 2011.
37.Scherer, W.T. and Glagola, D.M. (1994). Markovian models for bridge maintenance management. Journal of Transportation Engineering, Vol.120, No.1, pp. 37-51.
38.Simoudis, E. (1996). Reality check for data mining. IEEE Expert: Intelligent Systems and Their Applications, Vol.11, No.5, pp.26-33.
39.Sobanjo J.O. and Thompson, P. D. (2011). Enhancement of the FDOT’s project level and network level bridge management analysis tools, Final Report.
40.Soibelman, L. and Kim, H, (2002). Data preparation process for construction knowledge generation through knowledge discovery in databases. Journal of Computing in Civil Engineering, Vol. 16, No. 1, pp. 39-48.
41.Thompson, P.D., Merlo, T., Kerr, B., Cheetham, A. and Ellis, R. (1999). The New Ontario Bridge Management System. Transportation Research Circular 498, Vol. 2, F-6.
42.Thompson, P.D., Small, E.P., Johnson, M and Marshall. A.R. (1998). The Pontis bridge management system. Structural Engineering International, Vol. 8, No. 4, pp. 303-308.
43.Warsaw University (2005). RSES 2.2 User’s Guide. http://logic.mimuw.edu.pl/ ∼rses.
44.Walczak B., Massart D.L. (1999). Tutorial Rough Set Theory. Chemometrics and Intelligent Laboratory Systems, Vol.47, pp.1-16.
45.Woodward, R.J., Haardt, P., Astudillo, R., Godart, M., Markey, I. and Bevc, L., Bridge Management in Europe (BRIME). Final Report, Forum of European Highway Research Laboratories, 2001.
46.Yang, Y., Webb, G. I., and Wu, X. (2010). Discretization methods. Data mining and knowledge discovery handbook, Chapter 6, Edited by Oded Maimon & Lior Rokach, Springer, U.S.
47.Yokoyama, K., Sato, H., Ogihara, K. and Toriumi, R. (1996). Development of a bridge management system in Japan. Bridge Management: Proceedings of the Third International Conference, Edited by Harding, J. E., Parke, G.A.R. and Ryall, M.J., Taylor & Francis, article No. 73.
48.Zhao, Z. and Chen, C. (2002). A fuzzy system for concrete bridge damage diagnosis. Computers and Structures, Vol. 80, pp. 629-641.
49.Zhu, Z., Gu, M. and Chen, Z. Q. (2007). Wind tunnel and computational fluid dynamics study of identification of flutter derivatives of a long-span self-anchored suspension bridge, Computer-Aided Civil and Infrastructure Engineering, Vol. 22, No. 8, pp.541-554.

指導教授

黃榮堯(Rong-yau Huang)

審核日期

2015-7-14

推文