Position：Associate professor
Degree： Doctor
My research focuses on structural health monitoring and intelligent operation, with an emphasis on integrating digital twins, Bayesian inference, and artificial intelligence for critical infrastructure such as bridges and offshore wind turbines. It aims to develop next-generation engineering paradigms that tightly couple data-driven methods with physical principles.
The main research areas include:
Structural Health Monitoring (SHM)
Digital Twin
AI for Engineering (integration of deep learning and scientific computing)
UAV-based intelligent inspection and computer vision
Bayesian inference and uncertainty quantification
1. Digital Twin and Intelligent Structural Sensing
Targeting complex infrastructure such as bridges and wind turbines, this research develops high-fidelity digital twins by integrating physics-based models with multi-source monitoring data, establishing a unified paradigm that combines physical modeling, data-driven learning, and uncertainty quantification. Leveraging structural health monitoring and multi-modal sensing, it enables structural state perception and anomaly detection, intelligent operational modal identification under varying conditions, Bayesian-based model updating, full-field response reconstruction under sparse measurements, and real-time inversion of unknown loads (e.g., wind, traffic, seismic). The ultimate goal is to build an integrated digital twin framework with capabilities in real-time perception, diagnosis, prediction, and decision support, enabling interpretable, updatable, and predictive intelligent operation and maintenance for the safety and long-term management of critical infrastructure.
2. UAV-enabled Intelligent Inspection and Digital Sensing
Targeting large-scale infrastructure such as bridges, buildings, and wind turbines, this research develops a UAV-based intelligent inspection framework by integrating multi-modal sensing data, including RGB images, infrared, and LiDAR. It combines autonomous path planning, deep learning-based damage detection, and high-fidelity 3D reconstruction via oblique photogrammetry and data fusion to enable accurate spatial representation and quantitative assessment of structural defects. By further integrating with BIM/BrIM through geometric and semantic mapping, the framework supports component-level damage localization, lifecycle data management, and automated report generation for maintenance decision-making. In addition, computer vision-based video measurement techniques are employed to extract structural vibration responses from UAV footage, reducing reliance on traditional contact sensors and promoting the integration of inspection and monitoring. The ultimate goal is to establish a unified UAV-enabled platform for autonomous inspection, multi-source sensing, intelligent analysis, and decision support, advancing efficient, safe, and smart operation and maintenance of infrastructure.
3. Physics–Data Hybrid Intelligent Inference
Targeting the core needs of digital twins and intelligent operation of complex structures such as bridges and wind turbines— including full-field response prediction, unknown load inversion, and damage identification—this research develops next-generation intelligent inference methods that tightly integrate physical modeling with data-driven learning. By combining physics-based priors (e.g., finite element models, differential equations, and state-space representations) with multi-source monitoring data and deep learning, a unified framework is established for accurate sensing and inference of structural states and external actions. The approach is designed to enhance robustness, generalization, and practical applicability under sparse data, complex environments, and model uncertainties, providing reliable support for UAV-based inspection and structural health monitoring, and enabling state updating, performance prediction, and decision-making within digital twin systems. Ultimately, it aims to bridge the full pipeline from perception to modeling, prediction, and decision, advancing infrastructure management from experience-driven to physics–data fusion-driven paradigms.
1. National Natural Science Foundation of China (NSFC), Hybrid Digital Twin of Bridge Structures Based on Hierarchical Bayesian Algorithm and Artificial Intelligence (Grant number 52208199), 2023.1 - 2025.12, PI.
2. Shanghai oversees high-level talent program, 2022.3 – 2024.12, PI.
3. Open Project, Shanghai Offshore Wind Energy Resource Development and Utilization Engineering Technology Research Center, Shanghai Investigation, Design & Research Institute Co., Ltd.: “Structural Health Monitoring and Real-time State Assessment of Offshore Wind Structures” (No. FNZX2023KP01), Apr. 2025 – Mar. 2028, Principal Investigator.
4. NSFC, Bridge digital modeling based on hybrid monitoring (Grant number 52378187), 2024.1 - 2027.12, Co-PI.
5. NSFC, Bridge Network Evaluation based on Multi-source Residual Data (Grant number 52278313), 2023.1 - 2026.12, Co-PI.
1. 孙利民, 王艺晴, 宋明明*, 夏烨. 基于循环神经网络辅助卡尔曼滤波法的动力响应重构方法. Journal of Southeast University/Dongnan Daxue Xuebao. 2025 Nov 1;55(6).
2. 罗岚炘，宋明明，钟华强，何天涛，孙利民*. 考虑运营荷载的大跨径拱桥层次贝叶斯模型修正方法. 振动与冲击. 2025; 44(1).
3. Wang T*, Zhang S, Song M, Sun L*. Dictionary Learning-Based Data Pruning for System Identification. Applied Sciences. 2025 Aug 26;15(17):9368.
4. Liu J, Li Y*, Sun L, Luo L, Song M. Physics-encoded interpretable self-supervised learning for structural damage identification. Engineering Structures. 2025 Nov 15;343:121045.
5. Luo L, Sun L, Song M, Liu J, Li Y, Xia Y. Joint load-parameter-response identification using a physics-encoded neural network. Mechanical Systems and Signal Processing. 2025 May 1;230:112597.
6. Komarizadehasl S, Shen Z, Xia Y*, Song M, Turmo J. An innovative drive-through approach for structural testing and experimental insights from two cable stayed bridges. Developments in the Built Environment. 2025 Apr 1;22:100653.
7. Qu G, Song M*, Xia Y, Sun L. Bridge Girder‐End Displacement Reconstruction Using a Novel Hybrid Attention Mechanism Leveraging Multisource Information. Structural Control and Health Monitoring. 2025;2025(1):8249455.
8. Luo W, Gong F, Song M, Xia Y*. A structural 3D displacement measurement method using monocular camera based on multiple feature points tracking. Measurement. 2024 Dec 9:116406.
9. Qu G, Song M*, Sun L. Bridge deformation quantiles prediction with MVO-CNN-BiLSTM based on mixed attention mechanism and periodic multi-source information fusion. Journal of Civil Structural Health Monitoring. 2024 Dec 9:1-22.
10. Song M, Moaveni B*, Hines E. Hierarchical Bayesian quantification of aerodynamic effects on an offshore wind turbine under varying environmental and operational conditions. Mechanical Systems and Signal Processing. 2025 Feb 1;224:112174.
11. Valikhani M, Nabiyan M, Song M, Jahangiri V, Ebrahimian H*, Moaveni B. Bayesian finite element model inversion of offshore wind turbine structures for joint parameter-load estimation. Ocean Engineering. 2024 Dec 1;313:119458.
12. Wang Y, Song M*, Wang A, Sun L. Structural Dynamic Response Reconstruction Based on Recurrent Neural Network–Aided Kalman Filter. Structural Control and Health Monitoring. 2024;2024(1):7481513.
13. Qu G, Song M*, Xin G, Shang Z, Sun L. Time-convolutional network with joint time-frequency domain loss based on arithmetic optimization algorithm for dynamic response reconstruction. Engineering Structures. 2024 Dec 15;321:119001.
14. Qu G, Song M*, Sun L. Bayesian dynamic noise model for online bridge deflection prediction considering stochastic modeling error. Journal of Civil Structural Health Monitoring. 2024 Aug 18:1-8.
15. Qu G, Song M*, Sun L. Real-Time Bridge Deflection Prediction Based on a Novel Bayesian Dynamic Difference Model and Nonstationary Data. Journal of Bridge Engineering. 2024 Sep 1;29(9):04024064.
16. Luo L, Song M*, Li Y, Sun L*. A hierarchical Bayesian model updating method for bridge structures by fusing multi-source information. Structural Health Monitoring. 2024 Jun 13:14759217241253361.
17. Teymouri D, Sedehi O, Song M, Moaveni B, Papadimitriou C, Katafygiotis LS*. Hierarchical Bayesian finite element model updating: Optimal weighting of modal residuals with application to FINO3 offshore platform. Mechanical Systems and Signal Processing. 2024 Apr 1;211:111150.
18. Luo L, Song M*, Zhong H, He T, Sun L*. Hierarchical Bayesian model updating of a long-span arch bridge considering temperature and traffic loads. Mechanical Systems and Signal Processing. 2024 Mar 15;210:111152.
19. Song M, Mehr NP, Moaveni B*, Hines E, Ebrahimian H, Bajric A. One year monitoring of an offshore wind turbine: Variability of modal parameters to ambient and operational conditions. Engineering Structures. 2023 Dec 15;297:117022.
20. Partovi-Mehr N, Branlard E, Song M, Moaveni B*, Hines EM, Robertson A. Sensitivity Analysis of Modal Parameters of a Jacket Offshore Wind Turbine to Operational Conditions. Journal of Marine Science and Engineering. 2023 Jul 30;11(8):1524.
21. Song M, Moaveni B*, Ebrahimian H, Hines E, Bajric A. Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements. Mechanical Systems and Signal Processing. 2023 Sep 1;198:110425.
22. Hines EM*, Baxter CD, Ciochetto D, Song M, Sparrevik P, Meland HJ, Strout JM, Bradshaw A, Hu SL, Basurto JR, Moaveni B. Structural instrumentation and monitoring of the Block Island Offshore Wind Farm. Renewable Energy. 2023 Jan 1;202:1032-45.
23. Song M, Christensen S, Moaveni B*, Brandt A, Hines E. Joint parameter-input estimation for virtual sensing on an offshore platform using output-only measurements. Mechanical Systems and Signal Processing. 2022 May 1;170:108814.
24. Mehrjoo A, Song M, Moaveni B*, Papadimitriou C, Hines E. Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost. Mechanical Systems and Signal Processing. 2022 Apr 15;169:108787.
25. Song M, Renson L*, Moaveni B, Kerschen G. Bayesian Model Updating and Class Selection of a Wing-Engine Structure with Nonlinear Connections using Nonlinear Normal Modes. Mechanical Systems and Signal Processing. 2022 Feb 15;165:108337.
26. Zhang Y, Lian J, Zhang G, Liu Y, Song M*, Li S. Ground vibration characteristics induced by flood discharge of a high dam: An experimental investigation. Journal of Renewable and Sustainable Energy. 2021 Jan 4;13(1):014502.
27. Liu P*, Huang S, Song M, Yang W. Bayesian Model Updating of a Twin-Tower Masonry Structure through Subset Simulation Optimization Using Ambient Vibration Data. Journal of Civil Structural Health Monitoring. 2020 Oct 23:1-20.
28. Song M, Behmanesh I, Moaveni B*, Papadimitriou C. Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview. Sensors. 2020 Jan;20(14):3874.
29. Song M, Astroza R, Ebrahimian H, Moaveni B*, Papadimitriou C. Adaptive Kalman filters for nonlinear finite element model updating. Mechanical Systems and Signal Processing. 2020 Sep 1;143:106837.
30. Yousefianmoghadam S, Song M, Mohammadi M, Packard B, Stavridis A*, Moaveni B, Wood RL, Packard B. Nonlinear dynamic tests of a reinforced concrete frame building at different damage levels. Earthquake Engineering & Structural Dynamics. 2020 May 11.
31. Song M, Behmanesh I, Moaveni B*, Papadimitriou C. Modeling error Estimation and response prediction of a 10-Story building model through a hierarchical Bayesian model updating framework. Frontiers in Built Environment. 2019;5:7.
32. Song M, Moaveni B*, Papadimitriou C, Stavridis A. Accounting for amplitude of excitation in model updating through a hierarchical Bayesian approach: Application to a two-story reinforced concrete building. Mechanical Systems and Signal Processing. 2019 May 15;123:68-83.
33. Song M, Renson L, Noël JP, Moaveni B*, Kerschen G. Bayesian model updating of nonlinear systems using nonlinear normal modes. Structural Control and Health Monitoring. 2018 Dec;25(12):e2258.
34. Song M, Yousefianmoghadam S, Mohammadi ME, Moaveni B*, Stavridis A, Wood RL. An application of finite element model updating for damage assessment of a two-story reinforced concrete building and comparison with lidar. Structural Health Monitoring. 2018 Sep;17(5):1129-50.
