Bridge systems have many components, and their fragility assessment must consider high-dimensional uncertainties. This study constructed a deep kernel learning (DKL) surrogate model to reduce the computational cost of fragility analysis of bridge …
本研究では、地震リスク解析の計算コストを削減するために、深層カーネル学習による代替モデルを開発した。このモデルは、畳み込みニューラルネットワーク（CNN）を用いて地震荷重の特徴を抽出する。さらに、Gradient-weighted Class Activation Mapping（Grad-CAM）により地震荷重の各部の寄与を推定し、ARDにより各構造パラメータの寄与を推定することで、代替モデルの説明可能性を高める。検証では、免震RC橋脚の地震応答解析のために代替モデルを構築した。その結果、代替 …
Reliability assessment of civil structures under seismic loads requires probabilistic evaluation considering the uncertainty of input ground motion and material properties due to deterioration. However, Monte Carlo calculation for the structural …
This study developed a framework for real-time dynamic analysis of structural members using physics-informed neural networks (PINN). The interest in the use of augmented reality (AR) and virtual reality (VR) technologies to visualize the results of …
Monte Carlo simulation is often adopted for the structural reliability analysis of civil structures that considers the parameter uncertainties, such as uncertainties of structural properties and input loads in the structural analysis. However, as the …