ホーム > 西尾 真由子/ Nishio, Mayuko

西尾 真由子

Nishio, Mayuko

システム情報系 , 准教授
Faculty of Engineering, Information and Systems , Associate Professor

https://www.kz.tsukuba.ac.jp/~nishio/

オープンアクセス版の論文は「つくばリポジトリ」で読むことができます。
  1. 1. A Mesh Updating Framework for Shell-Element Finite Element Models Based on Projection of Anomaly Regions Extracted from Point-Cloud Data Jiexiu Wang; Mayuko Nishio
      Sensors 26: 5950 (2026) Semantic Scholar
  2. 2. Analytical Framework for System‐Level Fragility Surfaces of Reinforced Concrete Bridges Under Mainshock‐Aftershock Sequences Muhammad Rashid; Mayuko Nishio
      Earthquake Engineering & Structural Dynamics (2026) Semantic Scholar
  3. 3. Hybrid strategy of deep learning and SPH method for accelerating Lagrangian free-surface flow simulations Gen Matono; Mayuko Nishio
      Computational Particle Mechanics 15: 168 (2026)
  4. 4. 橋梁部材テンプレートモデルを用いた薄肉部材点群データからのFEモデル構築 中溝 智也; 西尾 真由子
      Japanese Journal of JSCE 82: 25-15066 (2026) Semantic Scholar
  5. 5. Structural anomaly detection and nonlinearity assessment by integrating phase angle and video-based analysis via a shaking table test Sifan Wang; Mayuko Nishio
      Journal of Sound and Vibration 619: 119372 (2025)
  6. 6. ExSRNet: Explainable deep learning model for seismic response prediction with frequency attention mechanism Taisei Saida; Mayuko Nishio
      Engineering Structures (2025) Semantic Scholar
  7. 7. Energy-based fragility assessment of reinforced concrete bridges considering cumulative seismic demand under mainshock-aftershock sequences Muhammad Rashid; Mayuko Nishio
      Bulletin of Earthquake Engineering (2025) Semantic Scholar
  8. 8. Uncertainty quantification using digital-image-correlation data for load-carrying capacity analysis of structural members with local damages Kai Sato; Mayuko Nishio
      Structures 79: 109431 (2025) Semantic Scholar
  9. 9. Shell Model Reconstruction of Thin-Walled Structures from Point Clouds for Finite Element Modelling of Existing Steel Bridges Tomoya Nakamizo; Mayuko Nishio
      Sensors 25: 4167 (2025) Semantic Scholar
  10. 10. Optimal intensity measure-based seismic fragility surfaces for curved bridges considering their sensitivity to seismic excitation direction Muhammad Rashid; Mayuko Nishio
      Earthquake Engineering and Engineering Vibration 24: 509 (2025) Semantic Scholar
  11. 11. Shell Model Reconstruction from Point Clouds for Efficient Analysis of Steel Bridges Tomoya Nakamizo; Mayuko Nishio
      IABSE Reports 121: 946 (2025) Semantic Scholar
  12. 12. Optical Flow‐Based Structural Anomaly Detection in Seismic Events From Video Data Combined With Computational Cost Reduction Through Deep Learning Sifan Wang; Taisei Saida; Mayuko Nishio
      Structural Control and Health Monitoring (2025) Semantic Scholar
  13. 13. Mainshock–aftershock seismic fragility assessment of civil structures: A state‐of‐the‐art review Muhammad Rashid; Mayuko Nishio
      Earthquake Engineering and Resilience 3: 548 (2024) Semantic Scholar
  14. 14. 視点重要度を考慮するUAV橋梁点検の飛行経路最適化と仮想空間内3Dモデルでの検証 佐藤駿成; 西尾真由子
      Artificial Intelligence and Data Science 5: 175 (2024)
  15. 15. System fragility analysis of highway bridge using multi-output Gaussian process regression surrogate model Taisei Saida; Muhammad Rashid; Mayuko Nishio
      Advances in Structural Engineering (2024) Semantic Scholar
  16. 16. Anomaly detection in structural dynamic systems via nonlinearity occurrence analysis using video data Sifan Wang; Mayuko Nishio
      Mechanical Systems and Signal Processing 216: 111506 (2024) Semantic Scholar
  17. 17. Enhancing prediction of landslide dam stability through AI models: A comparative study with traditional approaches Xianfeng Li; Mayuko Nishio; Kentaro Sugawara; Shoji Iwanaga (+4 著者) Pang-jo Chun
      Geomorphology 454: 109120 (2024) Semantic Scholar
  18. 18. Component‐level point cloud completion of bridge structures using deep learning Gen Matono; Mayuko Nishio
      Computer-Aided Civil and Infrastructure Engineering (2024) Semantic Scholar
  19. 19. Review for structural damage evaluation from video data in disasters focusing on structrual nonlinearity : from vision-based system identification to damage detection Wang, S; Nishio, M
      Smart Structures and Systems 33: 263 (2024)
  20. 20. 高速な深層学習メッシュフリー数値解析のためのニューラルモード分解の定式化と自由表面流れの再構成 的野 玄; 西尾 真由子
      日本計算工学会論文集 20241003: (2024)
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書籍等出版物情報はまだありません。

  1. 1. Data-driven simulation modeling for performance analysis of civil structures Mayuko Nishio
    CSHM-10 workshop: Physics-informed structural health monitoring 2026年6月9日 招待有り
  2. 2. Application of physics-assisted machine learning to data assimilation structural analysis Mayuko Nishio
    The 16th International Workshop on Advanced Smart Materials and Smart Structures Technology and The 4th ZHITU Symposium on Advances in Civil Engineering (ANCRiSST2025 & ZHITU2025) 2025年7月20日 招待有り
  3. 3. PINNを用いた圧密沈下予測とパラメータの推定 久保栞; 西尾真由子; 井上純哉; 宮本崇; 全邦釘
    第23回計測自動制御学会システムインテグレーション部門講演会SI2022 2022年12月
  4. 4. Capacity evaluation of existing bridges by integration of SHM data and numerical analysis
    11th International Conference on Structural Health Monitoring of Intelligent Infrastructure 2022年8月 招待有り
  5. 5. Physics-Informed Neural Networks for Prediction of Ground Settlement
    15th World Congress on Computational Mechanics & 8th Asian Pacific Congress on Computational Mechanics 2022年8月
  6. 6. System Fragility Analysis of a Geometrically Complex Bridge System for Seismic Risk Evaluation of Existing Highway Bridge Network
    8th World Conference on Structural Control and Monitoring 2022年7月
  7. 7. Dynamic response evaluation of an existing bridge structure based on Finite Element Modelling Rashid, M; Nishio, M
    9th Experimental Vibration Analysis for Civil Engineering Structures (EVACES 2021) 2021年9月
  8. 8. 地震発生後の車両通行性に着目した既存橋梁システムのフラジリティ解析 サキャ 摩耶; 西尾 真由子
    第23回応用力学シンポジウム 2020年5月16日
  9. 9. Bayesian posterior distributions of nonlinear model parameters by using varying seismic responses Tran, T; Nishio, M
    Engineering Mechanics Institute (EMI) Conference 2018 2018年5月29日
  10. 10. 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
    第28回計算工学講演会
  11. 11. 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
    第28回計算工学講演会
  12. 12. 地震荷重特徴抽出を備えた深層カーネル学習代替モデルによる地震リスク解析の効率化 才田 大聖; 西尾 真由子
    第26回応用力学シンポジウム
  13. 13. PINNを用いたリアルタイム構造振動解析の構築 奥田 東子; 西尾 真由子
    第26回応用力学シンポジウム
  14. 14. Detect and evaluate undesired nonlinearities in engineering structural dynamics
    IUTAM Symposium on Nonlinear dynamics for design of mechanical systems across different length/time scales
  15. 15. 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
    第28回計算工学講演会

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