基於梯度之參數識別方法應用於參數擬合與模型更新之研究

Abstract

地震工程研究人員在模擬結構試驗反應時，對於非線性材料模型的參數設定通常需要使用試誤法，因而耗費大量的時間。為了改善此現況，發展一套可以有效應用於自動化模型校正，又稱參數擬合（off-line model fitting）的參數識別（parameter identification）方法有其重要性。為呼應參數擬合技術的發展需求，本研究提出一套基於梯度之參數識別方法（the gradient-base method for off-line model fitting, GBM_MF），並以國家地震工程研究中心經常執行的挫屈束制支撐（buckling-restrained brace, BRB）實驗為例，展示對於常見於鋼結構非線性模擬時所採用的基於雙面理論的塑性硬化材料模型（two-surface model），以GBM_MF方法進行參數擬合的應用成效。此外，根據文獻回顧，近年來參數識別的應用範疇，逐漸由參數擬合擴展到模型更新（on-line model updating）的相關研究。因此，針對執行先進的擬動態試驗（hybrid simulation）所需要的模型更新技術，本研究另外提出一套基於梯度之參數識別方法（the gradient-base method for on-line model updating, GBM_MU）。GBM_MU方法特別在擬動態試驗進行中，參數識別的過程裡特別考量雙面理論，來控制塑性硬化材料模型的待識別參數項目，進而達到更有效率的參數識別結果。在本研究，作者採用2009年日本E-defense含BRB之五層樓鋼構架振動台試驗的數據，來驗證以GBM_MF方法進行參數擬合校正BRB模型，近而提升整體構架模擬品質的成果。另外，亦採用此數值模型，以數值模擬的方式來進行含模型更新的擬動態試驗（simulated hybrid test with model updating），藉以展現採用本研究所提出之GBM_MU參數識別方法其應用成效。綜合上述所言，本研究針對參數擬合與模型更新分別提出基於梯度之參數識別方法，並且透過BRB構件試驗模擬、以數值模擬含模型更新的擬動態試驗等方式，來進行參數識別方法的效能驗證與成果展示。

In the nonlinear structural response simulations, researchers often need to calibrate the parameters of a nonlinear material model with the experimental data by using the trial and error method. This can be very tedious and time consuming. In order to improve the calibration efficiency, the efficient method of parameter identification is desired. This study is to present two gradient-based parameter identification methods (GBM_MF and GBM_MU) for off-line model fitting and on-line model updating, respectively. The proposed GBM_MF method for off-line model fitting can assist the engineers and researchers, who are engaged in the nonlinear structural analyses, in model calibration. In addition, for the advanced hybrid simulation with on-line model updating, the proposed parameter identification method (GBM_MU) with innovative modification is presented in this dissertation. The shaking table test of a five-story BRB frame (BRBF) conducted in E-defense Japan in 2009 is utilized to verify the effectiveness of the proposed methods in the off-line and on-line applications. Compared with the measured responses, the results of off-line model fitting application can confirm that the proposed gradient-based method (GBM_MF) allows the efficient model calibration for the accurate simulation of the nonlinear responses of the BRBF. Moreover, the advantage of the on-line model updating with the proposed parameter identification method (GBM_MU) is demonstrated through the simulated hybrid tests. As a result, the proposed gradient-based methods of parameter identification for off-line model fitting and on-line model updating can advance the earthquake engineering research and practice.