A Study on Data Assimilation of Finite Element Analysis Models Using the Digital Image Correlation Method

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K12006
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 60100:Computational science-related
• Research Institution: Chiba Institute of Technology
• Principal Investigator: Akita Takeshi 千葉工業大学, 工学部, 准教授 (20405343)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: Data Assimilation / Inverse Problem / Finite Element Method / Kalman Filter / Damage Detection

Outline of Final Research Achievements

In this study, we developed a technique for effective condition monitoring of structures by performing a sequential data assimilation of finite element analysis models using many displacement measurement data obtained from the Digital Image Correlation (DIC) method. In this technique, the stiffness parameters in each finite element of the models are estimated as a state vector in the sequential data assimilation where the stiffness parameter distributions estimated over finite element model are utilized to detect regions damaged. In addition, we considered an efficient estimation technique by using the observation equation for displacement differences to remove errors common to the observation. Numerical experiments and experiments with real data validated the effectiveness of these technique presented in this study.

Free Research Field

Computational Mechanics

Academic Significance and Societal Importance of the Research Achievements

デジタル画像相関法による計測技術は構造物の状態監視や損傷検知に有効な技術であるが，計測から得られる情報は，撮影領域の変位場，またその勾配から得られるひずみ場のみとなり，物性値や損傷度合いを評価するためには別途評価式を用いる必要があった．本研究は逐次データ同化技術を適用することで，デジタル画像相関法の計測情報のみでは把握できない状態量を，解析モデルを援用して取得する方法を提示するものである．本研究で得られた成果は，画像計測技術や損傷同定技術の分野の高度化において有用であると考える．