| 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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| 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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