Construction of a high-efficiency performance evaluation system for existing bridges by the uncertainty quantification of model parameters using structural sensing data
| Project/Area Number |
17H04934
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Single-year Grants |
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| Research Field |
Structural engineering/Earthquake engineering/Maintenance management engineering
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| Research Institution | University of Tsukuba (2018-2020)
Yokohama National University (2017) |
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Principal Investigator |
Nishio Mayuko 筑波大学, システム情報系, 准教授 (00586795)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥21,190,000 (Direct Cost: ¥16,300,000、Indirect Cost: ¥4,890,000)
Fiscal Year 2019: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2018: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
Fiscal Year 2017: ¥11,310,000 (Direct Cost: ¥8,700,000、Indirect Cost: ¥2,610,000)
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| 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 / 地震モニタリング / 画像計測 / 機械学習 / 保有性能 / 非線形動的解析 / 非線形時刻歴応答解析 / 無線加速度センサ / グローバル感度解析 / センシング / モンテカルロ計算 / 最適観測計画 / 現場計測 |
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| 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.
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| 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．
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Report
(4 results)
Research Products
(16 results)
