| 摘要(英) |
Taiwan has a large number of bridges that play a significant role in transportation and economic development. Currently, there are 10,246 bridges in Taiwan that are over 30 years old, indicating that Taiwan′s bridges have entered a period of serious deterioration, making bridge inspection and maintenance increasingly important. Therefore, this study aims to use the Taiwan Bridge Management System (TBMS) database and big data approaches to identify the correlation between deterioration of bridge component and bridge inventory data to improve the effectiveness of bridge inspection.
The study is divided into two stages. In the first stage, 75 bridges on some provincial roads were analyzed using the regression method in R software, but due to the relatively small sample size, some correlations between inventory fields and component deterioration could not be reasonably explained. Therefore, in the second stage, a total of 2,849 bridges across central and county rivers in seven counties and cities in central and southern Taiwan, were selected and divided into three categories based on span number. Both the bridge inventory data and inspection data of various types of bridges need to be preprocessed to ensure the accuracy and usability of the data. After preprocessing, data clustering and correlation analysis were conducted using SPSS software. Research results showed a correlation between the bridge inventory fields and the deterioration of bridge components. For instance, for the first category of single-span bridges, the probabilities of deterioration of the bridge piers, girders, and bridge deck were 25%, 32.18%, 38.96%, and 28.8%, respectively, depending on the contents of the bridge inventory data..
The correlation between deterioration of bridge component and bridge inventory data found in this study can provide references for inspection personnel and bridge management agencies. When conducting regular bridge inspections or special inspections before and after flood seasons and natural disasters, inspection personnel can pay special attention to bridge components that may deteriorate according to the characteristics of the bridge inventory data. The results of this study are of great help in improving the accuracy and efficiency of bridge inspection. |
| 參考文獻 |
1.交通部運輸研究所：<台灣地區橋梁管理資訊系統>，2022年05月03日，取自https://bms.iot.gov.tw
2.交通部技監室：<公路橋梁檢測及補強規範>，2020年01月03日，取自https://www.motc.gov.tw/ch/app/divpubreg_list/view?module=divpubreg&id=740&serno=425
3.陳俊仲：<臺灣地區橋梁管理系統維護管理決策支援模組之建立-以公路總局為例>。碩士論文，國立中央大學，民國96年6月。
4.翁愷翔：<橋梁維修與整建效益評估比較之研究>。碩士論文，國立中央大學，民國98年6月。
5.賴鈺蒨：<系統性橋梁維修評選之研究>。碩士論文，國立中央大學，民國89年6月。
6.黃薰毅：<橋梁構件共同維護模式之建構>。碩士論文，國立中央大學，民國95年6月。
7.Jinsong Zhu and Boqi Liu,” Performance Life Cost-Based Maintenance Strategy Optimization for Reinforced Concrete Girder Bridges,” Journal of Bridge Engineering, Vol 18, February 2013.
8.Mohammed Safi; Håkan Sundquist; and Raid Karoumi,” Cost-Efficient Procurement of Bridge Infrastructures by Incorporating Life-Cycle Cost Analysis with Bridge Management Systems,” Journal of Bridge Engineering, Vol 20, June 2015.
9.Radovic, M.; Ghonima, O.; Schumacher, T. Data Mining of Bridge Concrete Deck Parameters in the National Bridge Inventory by Two-Step Cluster Analysis. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A: Civ. Eng. 2017, 3, F4016004. https://doi.org/10.1061/ajrua6.0000889.
10.Chang, K.; Chi, S. Bridge Clustering for Systematic Recognition of Damage Patterns on Bridge Elements. J. Comput. Civ. Eng. 2019, 33, 04019028. https://doi.org/10.1061/(asce)cp.1943-5487.0000846.
11.Diaz Arancibia, M.; Rugar, L.; Okumus, P. Role of Skew on Bridge Performance. Transp. Res. Rec. J. Transp. Res. Board 2020, 2674, 282–292. https://doi.org/10.1177/0361198120914617.
12.Kim, Y.J.; Queiroz, L.B. Big Data for condition evaluation of constructed bridges. Eng. Struct. 2017, 141, 217–227. https://doi.org/10.1016/j.engstruct.2017.03.028.
13.Kim, Y.J.; Yoon, D.K. Identifying Critical Sources of Bridge Deterioration in Cold Regions through the Constructed Bridges in North Dakota. J. Bridg. Eng. 2010, 15, 542–552. https://doi.org/10.1061/(asce)be.1943-5592.0000087.
14.Li, T.; Alipour, M.; Harris, D.K. Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention. Autom. Constr. 2021, 129, 103801. https://doi.org/10.1016/j.autcon.2021.103801.
15.Martinez, P.; Mohamed, E.; Mohsen, O.; Mohamed, Y. Comparative Study of Data Mining Models for Prediction of Bridge Future Conditions. J. Perform. Constr. Facil. 2020, 34, 04019108. https://doi.org/10.1061/(asce)cf.1943-5509.0001395.
16.Kim, H.; Soibelman, L.; Grobler, F. Factor selection for delay analysis using Knowledge Discovery in Databases. Autom. Constr. 2008, 17, 550–560. https://doi.org/10.1016/j.autcon.2007.10.001.
17.Fayyad, U.; Stolorz, P. Data mining and KDD: Promise and challenges. Futur. Gener. Comput. Syst. 1997, 13, 99–115. https://doi.org/10.1016/s0167-739x(97)00015-0.
18.Li, T.; Alipour, M.; Harris, D.K. Mapping textual descriptions to condition ratings to assist bridge inspection and condition assessment using hierarchical attention. Autom. Constr. 2021, 129, 103801. https://doi.org/10.1016/j.autcon.2021.103801.
19.Lei, X.; Xia, Y.; Deng, L.; Sun, L. A deep reinforcement learning framework for life-cycle maintenance planning of regional deteriorating bridges using inspection data. Struct. Multidiscip. Optim. 2022, 65, 149. https://doi.org/10.1007/s00158-022-03210-3.
20.Lei, X.; Xia, Y.; Deng, L.; Sun, L. A deep reinforcement learning framework for life-cycle maintenance planning of regional deteriorating bridges using inspection data. Struct. Multidiscip. Optim. 2022, 65, 149. https://doi.org/10.1007/s00158-022-03210-3.
21.Radovic, M.; Ghonima, O.; Schumacher, T. Data Mining of Bridge Concrete Deck Parameters in the National Bridge Inventory by Two-Step Cluster Analysis. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A: Civ. Eng. 2017, 3, F4016004. https://doi.org/10.1061/ajrua6.0000889.
22.Kim, Y.J.; Yoon, D.K. Identifying Critical Sources of Bridge Deterioration in Cold Regions through the Constructed Bridges in North Dakota. J. Bridg. Eng. 2010, 15, 542–552. https://doi.org/10.1061/(asce)be.1943-5592.0000087.
23.Diaz Arancibia, M.; Rugar, L.; Okumus, P. Role of Skew on Bridge Performance. Transp. Res. Rec. J. Transp. Res. Board 2020, 2674, 282–292. https://doi.org/10.1177/0361198120914617.
24.Alogdianakis, F.; Charmpis, D.C.; Balafas, I. Macroscopic effect of distance from seacoast on bridge deterioration—Statistical data assessment of structural condition recordings. Structures 2020, 27, 319–329. https://doi.org/10.1016/j.istruc.2020.05.052.s
25.Kim, Y.J.; Queiroz, L.B. Big Data for condition evaluation of constructed bridges. Eng. Struct. 2017, 141, 217–227. https://doi.org/10.1016/j.engstruct.2017.03.028. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 ( 永不開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit