导师姓名

王天宇

导师性别

男

职务职称

讲师

所在院系

城市建设与生态技术学部

一级学科

土木工程

二级学科

结构工程

研究方向

结构非线性；结构健康监测；建筑信息模型；人工智能在土木工程中的应用

联系电话

18817392531

电子邮箱

ty_wang@sit.edu.cn

个人简介

王天宇，男，1993年生，博士毕业于东南大学，2022年起在城市建设与生态技术学部（原城市建设与安全工程学院）任讲师，土木工程系副主任。主要研究方向为结构非线性；结构健康监测；建筑信息模型；人工智能在土木工程中的应用。讲授本科课程：《结构力学》、《人工智能与大数据分析》、《基础工程》、《桥梁防灾设计》、《BIM技术基础及应用》；研究生课程：《土木工程人工智能新技术》

学习与工作经历

2022.10-至今 上海应用技术大学 城市建设与生态技术学部 讲师

2018.07-2022.07 东南大学 土木工程 博士

2015.09-2018.07 上海大学 结构工程 硕士

2011.09-2015.07 上海大学 土木工程 本科

科研工作与成果

本人与所在团队围绕人工智能结构非线性分析与健康监测等领域的应用、动力响应分析与结构安全评价开展了大量研究，发表论文20余篇被引900余次，主持上海市教委人工智能促进科研范式改革赋能学科跃升计划项目一项，企业横向课题2项，主要发表论文如下：

[1]. Wang, T., et al., Symbolic deep learning-based method for modeling complex rate-independent hysteresis. Computers & Structures, 2025. 311: p. 107702.

[2]. Ge, J., Y. Gou and T. Wang, Static Analysis of Euler-Bernoulli Beam with Arbitrary Number of Oblique Cracks: a Semi-Analytical Method. Mechanics of Solids, 2025. 60(2): p. 1253--1271.

[3]. Li, H., T. Wang and H. Yan, Dynamic analysis of coupled train and cracked bridge systems using multiscale finite element modeling. International Journal of Structural Stability and Dynamics, 2024. 24(06): p. 2450057.

[4]. Li, H., T. Wang and G. Wu, Nonlinear vibration analysis of beam-like bridges with multiple breathing cracks under moving vehicle load. Mechanical Systems and Signal Processing, 2023. 186: p. 109866.

[5]. Li, H., T. Wang and G. Wu., Deep learning models for time-history prediction of vehicle-induced bridge responses: A comparative study. International Journal of Structural Stability and Dynamics, 2023. 23(01): p. 2350004.

[6]. Li, H., T. Wang and G. Wu, Probabilistic safety analysis of coupled train-bridge system using deep learning based surrogate model. Structure and Infrastructure Engineering, 2023. 19(8): p. 1138--1157.

[7]. Wang, T., et al., Seismic response prediction of structures based on Runge-Kutta recurrent neural network with prior knowledge. Engineering Structures, 2023. 279: p. 115576.

[8]. Li, H., T. Wang and H. Yan, Real-time prediction of dynamic irregularity and acceleration of HSR bridges using modified LSGAN and in-service train. Smart Structures and Systems, 2023. 31(5): p. 501--516.

[9]. Wang, T., et al., From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems. Mechanical Systems and Signal Processing, 2023. 204: p. 110785.

[10]. Wang, T., et al., Probabilistic seismic response prediction of three-dimensional structures based on Bayesian convolutional neural network. Sensors, 2022. 22(10): p. 3775.

[11]. Li, H., T. Wang and G. Wu, A Bayesian deep learning approach for random vibration analysis of bridges subjected to vehicle dynamic interaction. Mechanical Systems and Signal Processing, 2022. 170: p. 108799.

[12]. Li, H., T. Wang and G. Wu, Dynamic response prediction of vehicle-bridge interaction system using feedforward neural network and deep long short-term memory network. 2021, Elsevier. p. 2415--2431.

[13]. Wang, T., H. Li and M. Noori, Response Prediction of Random Structure Based on Bayesian Neural Network. 2021, Springer International Publishing Cham. p. 19--25.

[14]. Wang, T., et al., Parameter identification and dynamic response analysis of a modified Prandtl--Ishlinskii asymmetric hysteresis model via least-mean square algorithm and particle swarm optimization. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2021. 235(12): p. 2639--2653.

[15]. Wang, T., M. Noori and W.A. Altabey, Identification of cracks in an Euler--Bernoulli beam using Bayesian inference and closed-form solution of vibration modes. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2021. 235(2): p. 421--438.

[16]. Wang, T., et al., A deep learning based approach for response prediction of beam-like structures. Structural Durability \& Health Monitoring, 2020. 14(4): p. 315.

社会学术团体兼职

无

主要研究方向

结构非线性分析；结构健康监测；建筑信息模型；人工智能在土木工程中的应用

近3年指导研究生的就业情况