machine learning

地震による構造物の被災度を確率論的に評価するために、地震動の不確定性を考慮する必要がある。しかし、不確定性を考慮し、数値計算を繰り返すのは計算コストが高い。本研究では、地震応答解析を深層学習器で置き換える代替モデルを用いることで計算コストの低減を図る。代替モデルには、Attention機構と説明性を有する畳み込み演算によって、なぜその予測結果に至ったのかを、周波数領域への寄与度計算を通して説明可能なモデル構築を行った。検証では橋脚の地震応答解析に対する代替モデルを構築し、寄与度の高かった周波数帯 …

Civil structures should keep functionality during earthquake events, necessitating detailed seismic evaluations. However, to account for uncertainties in seismic loading, nonlinear time history analyses must be repeated many times, increasing the …

In this study, an explainable seismic response surrogate model, which can be deemed valid from an engineering perspective, was developed. The model employs SincNet convolution to segment features by frequency band and an attention mechanism to …

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 …

Real-time simulation of structures and structural members enables intuitive and immediate understanding of displacements and stresses, contributing to efficient decision making in design and maintenance. In this study, real-time simulation of …

The particle method does not require any computational grid and is an effective numerical method for simulating behaviors of the continuum mechanics. However, the computational cost is the issue to apply it to more complex physics or to the Monte …