My research interests include structural health monitoring, digital twin, Bayesian inference, deep learning, and hybrid modeling.

1.      Digital twin

Building digital twins for large-span bridges and wind turbines based on Bayesian model updating, Bayesian filtering, and hybrid modeling, for damage identification, input load (wind loads, traffic loads, etc.) estimation, and structural response prediction.

2.      Physics and Data-driven hybrid modeling

Integrating physics-based modeling techniques (finite element models, ordinary/partial differential equations, state-space models, etc.) and data-driven modeling methods (supervised learning, unsupervised learning, reinforcement learning, and adversarial learning, etc.) to build hybrid models and improve prediction accuracy, generalizability and interpretability.

3.      Bayesian system identification

Applying Bayesian inference and Hierarchical Bayesian method for modal identification, model updating, data fusion, and uncertainty quantification; Developing Bayesian filtering and smoothing methods, including Kalman filter, nonlinear Bayesian filter, general Gaussian filter, partial filter, and RTS smoother, to accurately identify structural parameters, input loads and system states.

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.      NSFC, Bridge digital modeling based on hybrid monitoring (Grant number 52378187), 2024.1 - 2027.12, Co-PI.

4.      NSFC, Bridge Network Evaluation based on Multi-source Residual Data (Grant number 52278313), 2023.1 - 2026.12, Co-PI.

1.      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.

2.      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.

3.      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.

4.      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.

5.      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.

6.      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.

7.      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.

8.      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.

9.      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.

10.  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.

11.  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.

12.  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.

13.  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.

14.  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.

15.  Song M, Renson L, Noël JP, et al. Bayesian model updating of nonlinear systems using nonlinear normal modes. Structural Control and Health Monitoring. 2018 Dec;25(12):e2258.

16.  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.