由於台灣所處之地理位置特殊，颱風、地震等自然災害頻仍，在台灣因颱風夾帶豪大雨導致溪水暴漲進而沖毀橋梁的事件屢見不鮮，因地震導致橋體受損的案例亦不勝枚舉，而其所造成的直接與間接損失更是難以估計，因此為確保橋梁結構在營運階段的安全性、耐久性與服務性，須建立一套完善之橋梁健康監測系統及預測模型，以便未來對橋梁性能進行評估，及作為後續橋梁修繕與補強之依據。本研究係以高雄市甲仙區之甲仙大橋為研究目標，橋梁結構為三跨鋼拱橋，為甲仙區對外聯繫之要道，研究主要目的為建立甲仙大橋有限元預測模型，首先依照設計圖說利用MIDAS Civil 2010建立橋梁初始有限元模型，隨後以現地橋梁微振量測結果為依據，透過工作模態分析實驗以及選用峰值挑選法、頻率域分解法、協方差型隨機子空間識別法作為分析橋梁模態參數之工具，藉由交互驗證模態參數識別結果來決定橋梁的動態特性，隨後以識別出之模態頻率與振形作為模型更新基準，研究中選擇基因演算法對所選之模型參數進行最佳化調整，考慮溫度對環境微振之影響，並於模型更新時對各參數予以綜合群組化，以期工作模態之結構動態參數能與有限元模型達成高度契合。模型校正完成後，便能據以預測實際結構於各種災害—例如地震、颱風、暴雨、衝擊載重、土石流與河床沖刷等情境下其結構行為與殘餘性能之依據。研究結果顯示，三種模態參數識別方法其分析結果具有良好的一致性，而基因演算法於參數更新之收斂上能提供良好的穩定性，適用於橋梁結構參數更新，且更新後之有限元模型能提供數個與工作模態分析結果相當接近之低頻高能模態頻率與振形，顯示基因演算法適用於本研究類型之橋梁有限元模型更新。

In the public construction, bridge structures play an important role of the transportation network. At present, they are facing some serious problems such as the aging of the infrastructure, excessive loading, river scouring and natural disasters. These factors may lead to the bridge components fail. To assurance the safety, durability, and serviceability condition during bridge operation, it is necessary to establish a comprehensive system of bridge health monitoring and a bridge baseline model. This paper presents the implementation of the finite-element model updating for the Jia-Sian Bridge, a multi-span steel arch bridge in Taiwan, Kaohsiung. First, the dynamic characteristics of the bridge have been estimate through field ambient vibration testing. Using the peak-picking method (PP), frequency domain decomposition method (FDD), and covariance-driven stochastic subspace identification method (SSI-COV), experimental modal properties of the bridge are found. Then the bridge initial finite element model built in accordance with the design drawings. When a finite element model was created, it is updated based on the field measured dynamic properties. The model parameters including the stiffness of joints and material parameters are updated using the genetic algorithm (GA) by minimizing the differences between the predicted and the measured modal frequencies. After model updating, the model can represent the structure, allowing for damage detection. Moreover, it can also predict the bridge behavior before a catastrophe happens. Results show that the three modal parameter identification methods have good agreement for identifying Jia-Sian Bridge modal properties. The optimization algorithm also works well and has good stability in the updating parameters convergence.

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