台灣地處環太平洋火山地震帶，地震導致結構損壞事件頻繁，長期板塊運動亦導致國土地形狹長、高山林立、河流坡度大。再者，台灣氣候屬亞熱帶地區，每年夏季受颱風侵害，挾帶之豪雨常因河道短急引致水位暴漲，洪水沖毀橋樑之災害亦是屢見不鮮，而橋樑結構面臨如此嚴苛環境，建立完善橋樑結構健康檢測系統實屬必要，如此方能確保國內交通基礎建設之安全性。本研究以高雄市甲仙區之甲仙大橋為目標橋樑，由現地之環境微振試驗蒐集微振訊號，透過工作模態分析以及峰值挑選、頻率域分解兩套系統識別方法尋求結構系統之動態特徵；另一方面，本研究亦藉由設計圖說建立目標橋樑之初始有限元素模型，並以適當地盤反力係數模擬地盤、基樁之交互作用。初始模型由於實際結構體之複雜化和劣化等因素，使得其動態特徵與實際量測之動態特徵存在誤差，因此本研究以現地量測訊號所識別之模態頻率為基準，對初始有限元素模型進行更新，透過對結構參數進行敏感性分析，以敏感程度決定更新參數及參數更新順序，再以基因演算法作為更新最佳化方法來對結構參數進行調整，嘗試尋求最佳結構參數，期使模型模態頻率與現地識別結果能達到高度契合。研究過程中對識別方法、量測訊號、模型更新均進行一系列檢核，結果顯示吾人所採用之識別方法具有良好可信度，量測訊號亦屬可靠，而模型更新亦明顯呈現預期結果，大幅增進模型與現地橋樑之一致性，以提升有限元模型模擬結構反應之可信度。更新後具參考價值之有限元素模型可有效用於環境載重分析，探討地震、強風、洪水等自然災害影響，並提供預警性。此外，如持續對橋樑進行監測並配合模型更新，可以透過動態特性之變化掌握營運中橋樑之安全性，提供適當補強與維修之參考依據。

Taiwan is located on the Pacific Ring of Fire, earthquakes lead to frequent damage on the structure, and due to the geographical location is in the subtropical region, every summer typhoons bring heavy rain caused the water level skyrocketed, the destroyed of bridge by floods is common. Considering such a harsh environment that we are facing, establishing a comprehensive system of bridge health monitoring is necessary. Jia-Sian Bridge was taken as a target bridge in the present study, which is located in Kaohsiung city, Taiwan. The acceleration signals under the ambient vibration were actually collected, then the dynamic characteristics of the bridge were estimated through peak-picking method (PP) and frequency domain decomposition method (FDD). Midas civil 2016 was used to establish initial finite model, the study also modeled the soil-structure interaction between site and pile through inputting appropriate subgrade coefficient. Due to the complication and deterioration of the actual structure, there is an error exists between the dynamic characteristics of the initial model and the actual measurement, the initial finite element model was updated based on the modal frequency identified by the field test. First, the sensitivity analysis was done to determine the updated parameters. Then, the genetic algorithm (GA) is used to adjust the structural parameters. After model updating, it successfully sought the best structural parameters to make the model modal frequency coincide with the results of field test.

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