| 摘要: | 台灣橋梁眾多，對交通與經濟發展具有極大之影響。目前台灣橋齡超過30年者已有10,246座，顯示台灣橋梁已進入老劣化時期，橋梁檢測與維修作業日見重要。因此，本研究旨在利用台灣地區橋梁管理資訊系統(Taiwan Bridge Management System, TBMS)之資料庫，利用大數據分析的研究方法，找出橋梁構件發生劣化與橋梁基本資料間之關聯性，以提升橋梁檢測之成效。
本研究分為兩階段，第一階段初步測試採R軟體之迴歸分析法，以省道上一個工務段之75座橋梁為樣本，分析橋梁構件劣化與橋梁基本資料欄位間之關聯性。因樣本數量相對少，此測試之分析結果並非理想，部分基本資料內容與構件劣化間之關聯性並無法解釋。因此，本研究第二階段，改以台灣中南部七縣市共2,849座跨中央管與縣市管河川之橋梁為樣本，將樣本分為單孔橋梁、2-3孔橋梁與4孔以上橋梁三大類。各類橋梁之橋梁基本資料與檢測資料皆須進行資料前處理，以確保資料內容之正確性與可用性，之後再以SPSS軟體進行分群與關聯性分析。研究成果可顯示橋梁構件劣化機率與橋梁基本資料內容間之關連性，例如：第一大類單孔橋梁之橋台、大梁及橋面板等構件，其劣化機率分別為25%、32.18%、38.96%、28.8%，視橋梁基本資料之內容而定。
本研究所找出橋梁構件劣化與橋梁基本資料間之關連性，可提供給檢測人員與橋梁維護管理單位之參考。如在執行橋梁定期檢測時、或在汛期前後及天災後執行特別巡查時，檢測人員可依橋梁基本資料之特性，特別注意可能發生劣化之橋梁構件。本研究之成果，對於提升橋梁檢測之正確性與效率有相當之助益。;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. |
