Construction of a high-efficiency performance evaluation system for existing bridges by the uncertainty quantification of model parameters using structural sensing data

2020 Fiscal Year Final Research Report

• Project/Area Number: 17H04934
• Research Category: Grant-in-Aid for Young Scientists (A)
• Allocation Type: Single-year Grants
• Research Field: Structural engineering/Earthquake engineering/Maintenance management engineering
• Research Institution: University of Tsukuba (2018-2020); Yokohama National University (2017)
• Principal Investigator: Nishio Mayuko 筑波大学, システム情報系, 准教授 (00586795)
• Project Period (FY): 2017-04-01 – 2021-03-31
• Keywords: データ同化 / 構造モニタリング / ベイズ推定 / 構造信頼性 / 性能評価 / 地震リスク解析 / スパースモデリング

Outline of Final Research Achievements

For future sustainable operations of existing civil infrastructures, it will be effective to take measures against risks both due to aging and disasters by quantitatively evaluating the current performance in considerations of actual structural conditions. This study showed the inference of Bayesian posterior distributions that quantifies the uncertainties of structural parameters of the numerical model for the performance analysis under the current structural conditions, by the data acquisition using acceleration or strain sensors. The measurements on multiple actual bridges were also conducted, and the data acquisition that could lead to the traffic load performance evaluation and the seismic risk evaluation were also demonstrated. Furthermore, it was shown that the computational cost of the Monte-Carlo structural reliability calculation using posterior distributions can be effectively reduced by the surrogate model by applying the Lasso sparse modeling method.

Free Research Field

構造工学

Academic Significance and Societal Importance of the Research Achievements

構造性能解析のモデルパラメータ事後分布推定に適切なデータ取得を行うために逆解析での重要な検討である観測計画に関する成果を得たこと，そして実橋梁のセンシングと数値モデルでその有効性を検証できた成果は，データ同化研究において学術的意義が大きい．また本研究の成果によって，インフラ構造物の維持管理に有用となる性能指標を導出できることを示した点で，センシングとデータ活用の意義と貢献を具体的に示せた点で社会的意義も大きい．