近年來，隨著智慧建築與結構健康監測技術的發展，如何於無外力激發條件下準確識別建築物之動態特性，已成為結構工程領域的重要研究課題。為此，本文探討結合隨機遞減法（Random Decrement, RD）與修正亞伯拉罕時域分析法（Modified Ibrahim Time Domain Method, MITD）之無輸入系統識別方法，進行結構動態參數（自然頻率與阻尼比）之準確估算。
本文首先從理論層面推導RD與MITD之數學基礎，說明其適用於處理白噪訊下之結構反應，並可萃取自由振動訊號以進行模態參數識別。接著，透過MATLAB與ETABS建立單自由度與多自由度數值模型，進行模擬驗證，探討不同物理量（位移、速度、加速度）、門檻值與自由度數對識別結果之影響。結果顯示：RD具良好的主模態頻率識別能力，加速度訊號在多模態系統中表現最穩定；MITD則在單模態下表現優異，但對阻尼比之辨識敏感度較高，於多模態情況下穩定性較差。
此外，本文亦建構簡化建築模型進行實務模擬，並探討濾波器階數對信號振幅與相位之影響，驗證經由二階濾波與雙重積分所得之位移結果具良好準確性，能有效還原結構動態反應。研究結果可提供未來於結構健康監測中，進行模態參數識別與位移重建之參考依據。

In recent years, with the development of smart buildings and structural health monitoring technologies, accurately identifying the dynamic characteristics of structures without external excitations has become a critical issue in structural engineering. This study investigates an output-only system identification approach by combining the Random Decrement (RD) with the Modified Ibrahim Time Domain Method (MITD) to estimate dynamic parameters such as natural frequencies and damping ratios.
Theoretical derivations of RD and MITD are first presented to establish their applicability in extracting free vibration responses from structures under white noise excitation. Numerical simulations using single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) models, implemented in MATLAB and ETABS, are conducted to validate the proposed methods and examine the influence of different physical quantities (displacement, velocity, acceleration), threshold levels, and system degrees of freedom. Results demonstrate that RD is effective in identifying dominant modal frequencies, with acceleration signals providing the most stable performance in multi-modal systems. MITD shows high accuracy in single-mode identification but reveals sensitivity in damping estimation under complex modal conditions.
Furthermore, a simplified structural model is built to simulate real-world practical cases. The study examines the influence of filter order on signal amplitude and phase, and confirms that displacement results obtained through second-order filtering and double integration from acceleration data are accurate and consistent with the structural model’s output. These results suggest that the method is suitable for reconstructing displacement in practical use.
The findings of this study serve as a valuable reference for future structural health monitoring and output-only modal analysis applications.

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