| Transfer learning Gaussian process regression surrogate model with explainability for structural reliability analysis under variation in uncertainties T Saida, M Nishio Computers & Structures 281, 2023 | 23 | 2023 |
| CNN-based segmentation frameworks for structural component and earthquake damage determinations using UAV images T Saida, M Rashid, Y Nemoto, S Tsukamoto, T Asai, M Nishio Earthquake Engineering and Engineering Vibration 22 (2), 359-369, 2023 | 8 | 2023 |
| Digital twin framework for real-time dynamic analysis visualization with detecting dynamic changes in structures properties using PINN T Okuda, T Saida, G Matono, M Nishio Sensors and Smart Structures Technologies for Civil, Mechanical, and …, 2023 | 2 | 2023 |
| System fragility analysis of highway bridge using multi-output Gaussian process regression surrogate model T Saida, M Rashid, M Nishio Advances in Structural Engineering 27 (16), 2803-2822, 2024 | 1 | 2024 |
| Gaussian Process Regression Surrogate Model for Seismic Vulnerability Assessment of Highway Bridge Structure System T Saida, R Muhammad, M Nishio International Conference on Experimental Vibration Analysis for Civil …, 2023 | 1 | 2023 |
| Construction of gaussian process regression surrogate model for nonlinear seismic response analysis using ard kernel T Saida, M Nishio Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM …, 2021 | 1 | 2021 |
| Optical Flow‐Based Structural Anomaly Detection in Seismic Events From Video Data Combined With Computational Cost Reduction Through Deep Learning S Wang, T Saida, M Nishio Structural Control and Health Monitoring 2025 (1), 4702519, 2025 | | 2025 |
| ExSRNet: Explainable Deep Learning Model for Seismic Response Prediction with Frequency Attention Mechanism T Saida, M Nishio Available at SSRN 4987290, 2024 | | 2024 |
| AttentionCNN を用いた工学的説明性の高い地震応答予測サロゲートモデルの構築 才田大聖， 西尾真由子 計算工学講演会論文集= Proceedings of the Conference on Computational …, 2024 | | 2024 |
| TL-GPRSM: A python software for constructing transfer learning Gaussian process regression surrogate model with explainability T Saida, M Nishio Software Impacts 16, 100515, 2023 | | 2023 |
| Gaussian process regression surrogate model for dynamic analysis to account for uncertainties in seismic loading T Saida, M Nishio Sensors and Smart Structures Technologies for Civil, Mechanical, and …, 2023 | | 2023 |
| Improvement of explainability of surrogate model for particle method by deep learning that include differential operations based on the SPH method GEN MATONO, T SAIDA, M NISHIO 計算工学講演会論文集 (CD-ROM) 28, 9-1, 2023 | | 2023 |
| SPH 法に基づく微分演算を内包した深層学習による粒子法代替モデルの説明性向上 的野玄， 才田大聖， 西尾真由子 計算工学講演会論文集= Proceedings of the Conference on Computational …, 2023 | | 2023 |
| PINN 構造振動解析の AR によるリアルタイム可視化 奥田東子， 西尾真由子， 才田大聖， 的野玄 計算工学講演会論文集= Proceedings of the Conference on Computational …, 2023 | | 2023 |
| 深層カーネル学習サロゲートモデルによる高次元不確定性をもつ構造信頼性解析の効率化 才田大聖， 西尾真由子 計算工学講演会論文集= Proceedings of the Conference on Computational …, 2023 | | 2023 |
| 転移学習ガウス過程回帰サロゲートモデルによる構造性能解析の計算負荷低減 才田大聖， 西尾真由子 計算工学講演会論文集= Proceedings of the Conference on Computational …, 2022 | | 2022 |
| ARD カーネルによる非線形地震応答解析のガウス過程回帰代替モデル構築 才田大聖， 西尾真由子 土木学会論文集 A2 (応用力学) 77 (2), I_93-I_104, 2021 | | 2021 |
Mayuko NISHIOUniversity of TsukubaVerified email at kz.tsukuba.ac.jp
