臺灣島型狹長，島上多南北走向高山與東西向河流，縱向交通線與橫向河流交會處所形成之眾多橋梁，為經濟發展之重要基礎設施。跨河橋梁之劣化來自於老化、超載，尤其是河流沖刷。因夏季之暴雨所帶來之巨大水量於短時間內經由坡度為3%-5%之河道流向大海，河流沖刷對橋梁造成嚴重損壞。目前已有大量研究在探討橋梁下部構件受河川沖刷影響，例如現地試驗、水工模型試驗、數學模擬等。
第二代臺灣地區橋梁管理資訊系統已建立二萬五千餘座車行橋梁多年之維護管理資料，但尚未以系統化方式分析此大量資料中河川沖刷與橋梁下部構件劣化之關聯性。本研究針對第二代臺灣地區橋梁管理系統資料庫進行篩選、整理，進而分析臺灣地區車行橋梁下部結構劣化與水系之關聯特性。本研究利用視覺化圖表技術來製作互動式儀錶板，快速顯示臺灣118個水系地理位置與橋梁下部構件劣化之各種排列組合，並將橋梁下部構件之劣化數據依嚴重程度進行分級顯示，以找出易受河川沖刷之點位、水系及區域，以及利於管理之的隱藏資訊。本研究之成果將提供給橋梁管理單位作為橋梁維護管理決策之參考。

Taiwan is a long-shaped island with high north-south mountains and many east-west rivers. Longitudinal traffic lines cross with transversal rivers forming a large amount of bridges which are the most import infrastructures for economic development. These bridges deteriorate due to aging, over-loaded traffic, and especially serious river scouring problems due to an average of 3%-5% slope of rivers which carry huge amounts of water into the sea in a very short time from summer storms or torrential rains. There are many research efforts focus on these scouring problems, such as in situ tests, hydraulic model tests, and mathematical simulations.
Deterioration of car bridges in Taiwan are recorded in the 2nd generation of the Taiwan Bridge Management System (TBMS2), which has a huge inventory of 25,000 bridge data including inspection and maintenance data for several years. However, there was no systematic statistic study for analyzing such big data the relationships between the deterioration of bridge substructures and rivers in various areas. Thus, this research is aimed at finding the characteristics of such deteriorations located in various rivers using big data approaches. Techniques of chart visualization and interactive chart panel are used to rapidly display the various combinations of deterioration of bridge substructures and the 118 river systems in Taiwan. Deteriorations of bridge substructures are classified based on severity to find out vulnerable locations, arears, river systems, and possible hidden information which are useful to bridge management. Results of this research will be provided to the bridge management agencies for reference in making decisions for bridge management.

[1] F. Harvey(2019). "Climate crisis linked to at least 15 $1bn-plus disasters in 2019." <https://www.theguardian.com/world/2019/dec/27/climate-crisis-linked-to-at-least-15-1bn-plus-disasters-in-2019>.
[2] 莊友涵(2010)，「氣候變遷對橋梁規劃與設計階段影響之研究」，碩士，國立中央大學，桃園縣。
[3] 國立中央大學(2021)，110年度「臺灣地區橋梁管理資訊系統」維護管理服務 服務建議書。
[4] 第二代臺灣地區橋梁管理系統及二代橋檢(2019). "廖先格." <https://www.maintenance.ntpc.gov.tw/uploaddowndoc?file=govdata/201910290947211.pdf&filedisplay=TBMS2%E5%8F%8A%E4%BA%8C%E4%BB%A3%E6%A9%8B%E6%AA%A2%2820191101%29.pdf&flag=doc>.
[5] CSIR(1994),TANFB Bridge Management System Description of Modules and Computer Options,Taipei,Taiwan Area National Freeway Bureau.
[6] 國立中央大學土木工程系橋梁工程研究中心(1997)，混凝土橋梁檢查手冊，臺灣省交通處公路局。
[7] 廖先格(2015)，「臺灣地區橋梁管理目視檢測效能提升之研究」，博士，國立中央大學，桃園縣。
[8] 交通部(2019)，公路養護規範。
[9] 陳俊仲(2007)，「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，國立中央大學，桃園縣。
[10] 經濟部水利署"河川概況." <https://www.wrap.gov.tw/pro12.aspx?type=0201000000>.
[11] 行政院經濟部(2020). "河川管理辦法." <https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=J0110029>.
[12] 賈奕騰(2014)，「跨河橋梁耐洪能力之可靠度分析」，碩士，國立臺灣科技大學，台北市。
[13] H. Wang. Forensic Diagnosis on Flood-Induced Bridge Failure. I: Determination of the Possible Causes of Failure. Journal of Performance of Constructed Facilities, 28(1), pages 76-84, 2014.
[14] 呂家賢(2013)，「自行車參與者持續涉入模式之研究」，碩士，大葉大學，彰化縣。
[15] 吳宗遠(2011)，「橋墩上鉤式套環流場與沖刷特性之渠槽實驗」，碩士，國立中興大學，台中市。
[16] 吳邦豪(2015)，「橋梁耐洪可靠度之初步評估」，碩士，國立臺灣科技大學，台北市。
[17] 任以永(1999)，「地理資訊系統在橋梁管理之應用」，碩士，國立中央大學，桃園縣。
[18] 張世昇(2018). "機器學習於橋墩沖刷預測之應用." 土木水利. (5, 45).
[19] Y. Itoh. Network-Level Bridge Life-Cycle Management System. Journal of Infrastructure Systems, 3(1), pages 31-39, 1997.
[20] L. Liu. Network-Level Risk-Based Framework for Optimal Bridge Adaptation Management Considering Scour and Climate Change. Journal of Infrastructure Systems, 26(1), pages 04019037, 2020.
[21] H. P. Tserng. Health Assessment and Maintenance Strategy for Bridge Management Systems: Lessons Learned in Taiwan. Journal of Infrastructure Systems, 13(3), pages 235-246, 2007.
[22] Z. Medina-Cetina. Probabilistic Evaluation of Unknown Foundations for Scour Susceptible Bridges. Journal of Bridge Engineering, 25(10), pages 04020074, 2020.
[23] S. Lim. Developing a Pattern Model of Damage Types on Bridge Elements Using Big Data Analytics. 34, pages 1-7, 2017.
[24] C. Ware(2000),Information Visualization: Perception for Design.
[25] o. H. S. T. K. Tolle(2009),The Fourth Paradigm: Data-Intensive Scientific Discovery.
[26] 唐澤聖(1999)，3D資料視覺化。
[27] F. J. Anscombe. Graphs in Statistical Analysis. The American Statistician, 27(1), pages 17-21, 1973.
[28] G. G. Matthew O. Ward, Daniel Keim (2010),Interactive Data Visualization: Foundations, Techniques, and Applications.
[29] 沈. 陳為, 陶煜波(2014)，視覺化資料：100%全腦吸收大數據，直入神經元。
[30] M. Friendly(2008),A Brief History of Data Visualization,15-56.
[31] L. Gitelman, & V. Jackson(2013),"Raw Data" Is an Oxymoron.
[32] E. R. Tufte(1986),The visual display of quantitative information.
[33] P. Kochevar(1994),Database management for data visualization.
[34] D. Beer. Popular Culture and New Media The Politics of Circulation. pages, 2013.
[35] W. S. Cleveland(1993),Visualizing Data.
[36] D. F. Swayne(2003),GGobi: evolving from XGobi into an extensible framework for interactive data visualization,423–444.
[37] L. Wilkinson(2004),The Grammar of Graphics.
[38] W. S. Cleveland. Graphical Perception: The Visual Decoding of Quantitative Information on Graphical Displays of Data. Journal of the Royal Statistical Society. Series A (General), 150(3), pages 192-229, 1987.