宋明明
发布时间:2020-07-17        浏览次数:3767

姓名:宋明明

职称:助理教授、特聘研究员

电子邮件:mingmingsong@tongji.edu.cn

研究领域:结构健康监测、风力发电机健康监测、贝叶斯理论、卡尔曼滤波理论、人工智能

所属研究室:桥梁健康监测与振动控制研究室



个人简介
宋明明,浙江大学本科,同济大学硕士,美国Tufts University博士。毕业后留校做博后一年,2020年8月受聘为研究助理教授。2022年3月入职同济大学土木工程学院桥梁工程系,任助理教授、特聘研究员、博导。研究方向为结构健康监测,涵盖贝叶斯理论、卡尔曼滤波理论,人工智能、风力发电机健康监测、损伤识别、结构可靠性分析等。发表11篇SCI论文、1篇Scopus论文和5篇国际会议论文,一作JCR Q1论文7篇,谷歌统计引用量277次,在领域顶刊Mechanical Systems and Signal Processing上发表论文5篇。参加7次国际学术会议,做9次学术报告。在2020年第38届IMAC会议和2022年国际桥协大会上受邀担任Session Chair。主持国家自然科学基金青年项目,参与多项美国国家科学基金(NSF)等项目,经费合计超过1600万元。获上海市领军人才(海外)青年人才项目资助。担任Buildings(JCR Q2)客座编辑,是Mechanical Systems and Signal Processing,Structural Health Monitoring,Journal of Sound and Vibration,Engineering Structures等多本SCI期刊审稿人。为美国土木工程师协会结构健康监测与控制委员会会员、美国实验力学协会会员。

 

主要学历

  • 2014.9 – 2019.8美国塔夫茨大学 (Tufts University),结构工程,博士学位

  • 2010.9 – 2013.5同济大学,桥梁工程,硕士学位

  • 2006.9 – 2010.7浙江大学,土木工程,学士学位

 

任职经历

  • 2022.3 – 至今同济大学,桥梁工程系,研究员

  • 2020.8 – 2022.2美国塔夫茨大学,土木与环境工程系,研究助理教授

  • 2019.9 – 2020.7美国塔夫茨大学,土木与环境工程系,博后


研究项目

  • 国家自然科学基金青年项目,基于层次贝叶斯理论和人工智能的桥梁结构混合数字孪生方法(编号52208199),2023.1 - 2025.12,主持

  • National Offshore Wind Research & Development Consortium, Physics Based Digital Twins for Optimal Asset Management (154719), 2021.1 - 2022.12, 80万美元, 参与

  • National Science Foundation (NSF), An Adaptive System Identification Approach Using Mobile Sensors (1903972), 2019.6 - 2022.5, 52万美元, 参与

  • Bureau of Safety and Environmental Enforcement (BSEE), The Block Island Structural Monitoring Joint Project (140E0119C0003), 2020.5 - 2021.12, 60万美元, 参与

  • United States Geological Survey (USGS), A Hierarchical Bayes Inversion Approach for Site Characterization Using Surface Wave Measurements (G18AP00034), 2018.6 - 2020.5, 8万美元, 参与

  • National Science Foundation (NSF), CAREER: Probabilistic Nonlinear Structural Identification for Health Monitoring of Civil Structures (1254338), 2013.6 - 2019.5, 40万美元, 参与


荣誉奖励

  • 获2021年上海市领军人才(海外)青年人才项目资助

  • 塔夫茨大学2019年度Littleton奖

  • 塔夫茨大学2016年度Kentaro Tsutsum奖学金

社会服务

  • 美国土木工程师协会 结构健康监测与控制委员会会员ASCE EMI Structural Health Monitoring and Control (SHMC) technical committee

  • 美国实验力学协会会员Society for Experimental Mechanics (SEM)

  • 2022年国际桥梁及结构工程协会(IABSE)大会(南京)组织委员

  • 担任杂志Buildings(JCR Q2)的特刊 “Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data”客座编辑

  • 担任以下期刊审稿人:
    Mechanical Systems and Signal Processing
    Structural Health Monitoring
    Journal of Sound and Vibration
    Engineering Structures
    Structural Control and Health Monitoring
    Soil Dynamics and Earthquake Engineering
    Journal of Bridge Engineering

 

期刊论文

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

 

会议论文

  1. Akhlaghi MM, Song M, Pontrelli M, Moaveni B, Baise LG. Site Characterization Through Hierarchical Bayesian Model Updating Using Dispersion and H/V Data. In Model Validation and Uncertainty Quantification, Volume 3 2020 (pp. 333-335). Springer, Cham.

  2. Song, M., Astroza, R., Ebrahimian, H., Moaveni, B., Papadimitriou, C. (2020). Nonlinear Model Updating Using Recursive and Batch Bayesian Methods. Proc. of 38th International Modal Analysis Conference (IMAC-XXXVIII), Houston, Texas, USA.

  3. Song, M., Behmanesh, I., Moaveni, B., Papadimitriou, C. (2018). Hierarchical Bayesian calibration and response prediction of a 10-Story building model. Proc. of 36th International Modal Analysis Conference (IMAC-XXXVI), Orlando, Florida, USA.

  4. Yousefianmoghadam S, Song M, Stavridis A, Moaveni B. System identification of a two-story infilled RC building in different damage states. In Improving the Seismic Performance of Existing Buildings and Other Structures 2015 (pp. 607-618).

  5. Song M, Shen Z, Tang P. Data quality-oriented 3D laser scan planning. In Construction Research Congress 2014: Construction in a Global Network 2014 (pp. 984-993).            

 

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