| 研究課題/領域番号 |
22KF0087
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| 補助金の研究課題番号 |
22F21785 (2022)
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 (2023)
補助金 (2022) |
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| 応募区分 | 外国 |
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| 審査区分 |
小区分22020:構造工学および地震工学関連
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| 研究機関 | 東京大学 |
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研究代表者 |
楠 浩一 東京大学, 地震研究所, 教授 (00292748)
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| 研究分担者 |
YEOW TREVOR 東京大学, 地震研究所, 外国人特別研究員
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| 研究期間 (年度) |
2023-03-08 – 2024-03-31
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| 研究課題ステータス |
完了 (2023年度)
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| 配分額 *注記 |
2,300千円 (直接経費: 2,300千円)
2023年度: 1,100千円 (直接経費: 1,100千円)
2022年度: 1,200千円 (直接経費: 1,200千円)
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| キーワード | Structural monitoring / Damage assessment / Collapse mode / Machine learning / Numerical modelling / Earthquake engineering / Residual displacement / Shake-table test / 構造ヘルスモニタリング / 振動台実験 / 構造応答 / 変形モード / 機械学習 / 構造解析 |
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| 研究開始時の研究の概要 |
Damage inspection is an intensive process that result in socio-economic losses. To address this, a structural health monitoring system was developed. Refinement is needed to identify building deformation mode. This research addresses this using machine-learning techniques.
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| 研究実績の概要 |
(1) We performed shake-table tests of steel frame structures exhibiting various collapse modes (e.g., total yield, soft-story). This data was then used to verify the accuracy of numerical models to capture building response and the correct failure mode.
(2) We proposed building response features based on the cumulation of plastic displacement response for use in developing a machine-learning classification model.
(3) We developed a database containing acceleration and displacement response of RC frame buildings considering different number of floors, floor stiffness distribution and collapse modes using numerical simulations. Using this database and experimental results, we verified the importance of the feature proposed in (2). This work was awarded an “Excellent Presentation Award” at the 2022 JAEE Annual Meeting.
(4) Using the input features identified from (2), we trained a collapse mode machine-learning classification model and obtained 95% accuracy. We rained another model using response features which are easier to obtain (i.e., peak floor accelerations), but were only able to obtain 78% accuracy. This shows that using our proposed feature as an input into training the model results in greater accuracy.
(5) We performed analyses of single-degree-of-freedom systems exhibiting Takeda hysteretic behavior. This data was used to derive an expression for estimating residual displacements following seismic events. This expression was found to be more accurate compared to existing equations in the Specifications for Highway Bridges by the Japan Road Association.
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