Presentations

塔上比が大きく曲げ変形が卓越する建物に関し，幾何学的非線形性に起因するねじれモードの内部共振現象であるQ–Δ共振の特性を分析するため，縮小試験体を設計・製作し振動台実験を行った．その結果，Q–Δ共振条件を満たす場合にねじれ応答が大きくなることを確認した．また，塔上比がより小さく曲げ変形が生じにくい試験体とのねじれ応答の比較を行った結果，前者の方がねじれ応答のピークが急峻になることが確認された．

水平2方向の剛性が異なる建物に大変位が生じると幾何学的非線形性によりねじりモーメントが発生する．そのため，水平2方向の並進モードの固有振動数の和または差がねじれモードの固有振動数と近接するとねじれモードが励起されるQ–Δ共振が発生する．そこで，Q–Δ共振条件を満足する二軸対称な平面を持つセンターコア型の超高層建物の立体骨組モデルを作成し長周期地震動でねじれ応答が励起されるか検討を行った．弾性応答を仮定し幾何学的非線形性を考慮して時刻歴応答解析を行った結果，ねじれモードが励起されることを確認した．

日本建築学会大会学術講演梗概集（東海） Summaries of Technical Papers of Annual Meeting, Architectural Institute of Japan Paper No. 20019, Vol. 構造I, pp. 37-38

日本建築学会大会学術講演梗概集（東海） Summaries of Technical Papers of Annual Meeting, Architectural Institute of Japan Paper No. 21005, Vol. 構造II, pp. 9-10

日本建築学会大会学術講演梗概集（東海） Summaries of Technical Papers of Annual Meeting, Architectural Institute of Japan Paper No. 21381, Vol. 構造II, pp. 761-762

日本建築学会大会学術講演梗概集（東海） Summaries of Technical Papers of Annual Meeting, Architectural Institute of Japan Paper No. 21103, Vol. 構造II, pp. 205-206

日本建築学会大会学術講演梗概集（関東） Vol. 構造II, pp. 1067-1068

日本建築学会大会学術講演梗概集（関東） Vol. 構造I, pp. 129-130

Proceedings published in September, 2020 Conference postponed one year Sendai, Japan Paper ID: 2c-0204, pp. 1-12

We developed a computer-vision-based system to control the response of medical instruments with casters in a building subjected to earthquake excitation. The controller employed a neural network, and reinforced learning based on the dynamic simulations of a controlled instrument under building floor motion was conducted. In the dynamic simulations, we used the Open Dynamics Engine, which is a simulator library of three-dimensional rigid body dynamics and introduced a friction model to simulate the dynamic behavior of the casters. To accurately simulate the dynamic behavior of the controlled instrument, we considered the control delay of the proposed system and introduced stochastic models of errors to identify the displacement of the instrument using computer vision. To validate the proposed system, we performed dynamic simulations of a controlled instrument under building floor motion. It was revealed that the instrument response was significantly suppressed by introducing the stochastic model of errors in the training of NN. This result suggests the usefulness of stochastic models in enhancing the performance of reinforced learning based on dynamic simulations.