This study presents a regularized Deep Kernel Learning (DKL) model for reliability analysis with high-dimensional uncertainties. Combining Deep Learning (DL) and Gaussian Process Regression (GPR), the model leverages DL for feature extraction and GPR …
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 …
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 …
This study proposes the surrogate modeling by the Gaussian process regression with the transfer learning (TL-GPRSM). The TL-GPRSM can reduce the computational cost by using data with input-output relationships close to those of the target analysis …
This study proposes the surrogate modeling by the Gaussian process regression with the transfer learning (TL-GPRSM). The TL-GPRSM can reduce the computational cost by using data with input-output relationships close to those of the target analysis …
Monte Carlo calculation is used in the seismic risk analysis of infrastructures that consider various parameter uncertainties; however, the calculation cost increases as the parameters become higher in the non-linear time history analysis with …