| A cost effective solution for pavement crack inspection using cameras and deep neural networks Q Mei, M Gül Construction and Building Materials 256, 119397, 2020 | 157 | 2020 |
| Densely connected deep neural network considering connectivity of pixels for automatic crack detection Q Mei, M Gül, MR Azim Automation in Construction 110, 103018, 2020 | 125 | 2020 |
| Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis Q Mei, M Gül, M Boay Mechanical Systems and Signal Processing 119, 523-546, 2019 | 125 | 2019 |
| A crowdsourcing-based methodology using smartphones for bridge health monitoring Q Mei, M Gül Structural Health Monitoring 18 (5-6), 1602-1619, 2019 | 79 | 2019 |
| Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones Q Mei, M Gül Structural Health Monitoring 19 (6), 1726-1744, 2020 | 48 | 2020 |
| Frequency identification of bridges using smartphones on vehicles with variable features N Shirzad-Ghaleroudkhani, Q Mei, M Gül Journal of Bridge Engineering 25 (7), 04020041, 2020 | 35 | 2020 |
| Towards smart cities: crowdsensing-based monitoring of transportation infrastructure using in-traffic vehicles Q Mei, M Gül, N Shirzad-Ghaleroudkhani Journal of Civil Structural Health Monitoring 10 (4), 653-665, 2020 | 34 | 2020 |
| Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion Q Mei, N Shirzad‐Ghaleroudkhani, M Gül, SF Ghahari, E Taciroglu Structural Control and Health Monitoring 28 (7), e2747, 2021 | 30 | 2021 |
| Novel sensor clustering–based approach for simultaneous detection of stiffness and mass changes using output-only data Q Mei, M Gül Journal of Structural Engineering 141 (10), 04014237, 2015 | 25 | 2015 |
| A fixed-order time series model for damage detection and localization Q Mei, M Gül Journal of Civil Structural Health Monitoring 6, 763-777, 2016 | 19 | 2016 |
| A Deep Learning and Computer Vision Based Multi-Player Tracker for Squash MM Baclig, N Ergezinger, Q Mei, M Gül, S Adeeb, L Westover Applied Sciences 10 (24), 8793, 2020 | 13 | 2020 |
| An improved methodology for anomaly detection based on time series modeling Q Mei, M Gul Topics in Dynamics of Civil Structures, Volume 4: Proceedings of the 31st …, 2013 | 10 | 2013 |
| Rapid and Automated Damage Detection in Buildings Through ARMAX Analysis of Wind Induced Vibrations GP Gislason, Q Mei, M Gül Frontiers in Built Environment 5, 16, 2019 | 8 | 2019 |
| A crowdsensing-based platform for transportation infrastructure monitoring and management in smart cities N Shirzad-Ghaleroudkhani, Q Mei, M Gül The Rise of Smart Cities, 609-624, 2022 | 7 | 2022 |
| A computer vision-based method to identify the international roughness index of highway pavements J Zeng, M Gül, Q Mei Journal of Infrastructure Intelligence and Resilience 1 (1), 100004, 2022 | 5 | 2022 |
| Detection of Suture Needle Using Deep Learning Q Mei, J Chainey, D Asgar-Deen, D Aalto Journal of Medical Robotics Research 4 (03n04), 1942005, 2019 | 5 | 2019 |
| Damage assessment of shear-type structures under varying mass effects NT Do, Q Mei, M Gul Structural Monitoring and Maintenance 6 (3), 237-254, 2019 | 5 | 2019 |
| A Deep Learning Model to Predict the Lateral Capacity of Monopiles AH Taherkhani, Q Mei, F Han Geo-Congress 2023, 220-227, 2023 | 2 | 2023 |
| Investigation of Time Series Analysis Based Damage Detection Methodologies for Structural Health Monitoring Q Mei | 2 | 2014 |
| Environment-aware worker trajectory prediction using surveillance camera on modular construction sites Q Yang, Q Mei, C Fan, M Ma, X Li Modular and Offsite Construction (MOC) Summit Proceedings, 82-89, 2022 | 1 | 2022 |
Mustafa GülUniversity of AlbertaVerified email at ualberta.ca
