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Journal Papers (Trainees are underlined)

16. Talebi-Kalaleh M, and Mei Q (2023) "A mobile sensing framework for bridge modal identification through an inverse problem solution procedure and moving-window time series models." Sensors, 23(11), 5154. (PDF)

15. Zeng J, Mei Q and Gül M (2022) "A computer vision-based method to identify the international roughness index of highway pavements." Journal of Infrastructure Intelligence and Resilience, 1(1), 100004. (PDF)

14. Mei Q, Shirzad‐Ghaleroudkhani N, Gül M, Ghahari F and Taciroglu E (2021) "Bridge mode shape identification using moving vehicles at traffic speeds through non-parametric sparse matrix completion." Structural Control and Health Monitoring, 28(7), e2747. (PDF)

13. Baclig MMErgezinger N, Mei Q, Gül M, Adeeb S and Westover L (2020) "A deep learning and computer vision based multi-player tracker for squash." Applied Sciences, 10(24), pp.8793. (PDF)

12. Mei Q, Gül M and Azim MR (2020) "Densely connected deep neural network considering connectivity of pixels for automatic crack detection." Automation in Construction, 110, pp.103018. (PDF)

11. Mei Q, and Gül M (2020) "A cost effective solution for road crack inspection using cameras and deep neural networks." Construction and Building Materials, 156, pp.119397. (PDF)

10. Mei Q, and Gül M (2020) "Multi-level feature fusion in densely connected deep learning architecture and depth first search for crack segmentation on images collected with smartphones." Structural Health Monitoring, pp.1475921719896813. (PDF)

9. Mei Q, Gül M and Shirzad‐Ghaleroudkhani N (2020) "Towards smart cities: crowdsensing-based monitoring of transportation infrastructure using moving vehicles." Journal of Civil Structural Health Monitoring, 10, pp. 653–665. (PDF)

8. Mei Q, Asgar-Deen D, Chainey J and Aalto D (2020) “Detection of suture needle using deep learning.” Journal of Medical Robotics Research, pp.1-9.

7. Shirzad‐Ghaleroudkhani NMei Q, and Gül M (2020) "Frequency identification of bridges using smartphones on vehicles with variable features." Journal of Bridge Engineering, 25(7), pp. 04020041. (PDF)

6. Mei Q, Gül M and Boay M (2019) "Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis." Mechanical Systems and Signal Processing, 119, pp.523-546. (PDF)

5. Mei Q, and Gül M (2019) "A crowdsourcing-based methodology using smartphones for bridge health monitoring." Structural Health Monitoring, 18(5-6), pp.1602-1619. (PDF)

4. Do NT, Mei Q, and Gül M (2019) "Damage assessment of shear-type structures under varying mass effects." Structural Monitoring and Maintenance, 6(3), pp.237-254. (PDF)

3. Gislason GPMei Q, and Gül M (2019) "Rapid and automated damage detection in buildings through ARMAX analysis of wind induced vibrations." Frontiers in Built Environment, 5(16). (PDF)

2. Mei Q, and Gül M (2016) "A fixed-order time series model for damage detection and localization." Journal of Civil Structural Health Monitoring, 6(5), pp.763-777. (PDF)

1. Mei Q, and Gül M (2014) "Novel sensor clustering–based approach for simultaneous detection of stiffness and mass changes using output-only data." Journal of Structural Engineering, 141(10), pp.04014237. (PDF)

Book Chapters (Trainees are underlined)

1. Shirzad-Ghaleroudkhani N, Mei, Q and Gül M (2022) "A crowdsensing-based platform for transportation infrastructure monitoring and management in smart cities." In The Rise of Smart Cities (pp. 609-624). Butterworth-Heinemann. (PDF)

Conference Papers (Trainees are underlined)

11. Fan C, Mei Q, Yang Q and Li X (2022) “Computer-vision based rapid entire body analysis (REBA) estimation.” Modular and Offsite Construction Summit, July 27-29, Edmonton, AB, Canada.

10. Yang Q, Mei Q, Fan C, Ma M and Li X (2022) “Environment-aware worker trajectory prediction using surveillance camera on modular construction sites.” Modular and Offsite Construction Summit, July 27-29, Edmonton, AB, Canada.

9. Mei Q (2022) “The application of compressed sensing on crowdsensing-based indirect bridge condition monitoring.” Canadian Society for Civil Engineering (CSCE) Annual Conference, May 25-28, Whistler, BC, Canada.

8. Mei Q, and Gül M (2019) “Monitoring a population of bridges in smart cities using smartphones.” Structures Congress 2019, April 24–27, Orlando, FL, United States.

7. Mei Q, and Gül M (2018) “A novel indirect damage assessment method for short and medium span bridges,” 10th International Conference on Short and Medium Span Bridges (SMSB 2018), July 31-August 3, Quebec City, Quebec, Canada.

6. Mei Q, and Gul M (2014) “Damage assessment of a 4-span bridge type structure using time series analysis.” Istanbul Bridge Conference, August 11-13, Istanbul, Turkey. 

5. Mei Q, and Gül M (2014) “Experimental study for a time series based method for damage detection using output data only,” 9th International Conference on Short and Medium Span Bridges (SMSB 2014), July 15-18, Calgary, AB, Canada.

4. Mei Q, and Gül M (2014) “Application of a novel time series based method for damage detection, localization and quantification using output only acceleration data.” 7th International Conference on Bridge Maintenance, Safety, and Management (IABMAS 2014), Jul. 7-11, Shanghai, China. 

3. Mei Q, and Gül M (2013) “Anomaly detection using a novel time series approach: application to the ASCE benchmark problem.” The 6th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-6), December 9-11, Hong Kong, China. 

2. Mei Q, and Gül M (2013) "Detection, localization and quantification of anomalies in mass, stiffness and damping based on time series modelling using output-only data. " A Roadmap to Intelligent Structures: Proceedings of the Ninth International Workshop on Structural Health Monitoring (IWHSM 2013), Volume 4. DEStech Publications Lancaster, 112-119. 

1. Mei Q, and Gül M (2013) "An improved methodology for anomaly detection based on time series modeling." Topics in Dynamics of Civil Structures: Proceedings of 31th International Modal Analysis Conference (IMAC XXXI), Volume 4. Springer New York, 277-281. 

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