| A dynamic Bayesian network-based approach to resilience assessment of engineered systems Q Tong, M Yang, A Zinetullina Journal of Loss Prevention in the Process Industries 65, 104152, 2020 | 85 | 2020 |
| A novel fuzzy dynamic Bayesian network for dynamic risk assessment and uncertainty propagation quantification in uncertainty environment X Guo, J Ji, F Khan, L Ding, Q Tong Safety science 141, 105285, 2021 | 76 | 2021 |
| Risk-based domino effect analysis for fire and explosion accidents considering uncertainty in processing facilities J Ji, Q Tong, F Khan, M Dadashzadeh, R Abbassi Industrial & Engineering Chemistry Research 57 (11), 3990-4006, 2018 | 51 | 2018 |
| An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks X An, Z Yin, Q Tong, Y Fang, M Yang, Q Yang, H Meng Reliability Engineering & System Safety 238, 109445, 2023 | 43 | 2023 |
| Resilience assessment of process industry facilities using dynamic Bayesian networks Q Tong, T Gernay Process Safety and Environmental Protection 169, 547-563, 2023 | 26 | 2023 |
| Application of the EnKF method for real-time forecasting of smoke movement during tunnel fires J Ji, Q Tong, LL Wang, CC Lin, C Zhang, Z Gao, J Fang Advances in Engineering Software 115, 398-412, 2018 | 24 | 2018 |
| Machine learning models for predicting the resistance of axially loaded slender steel columns at elevated temperatures Q Tong, C Couto, T Gernay Engineering Structures 266, 114620, 2022 | 22 | 2022 |
| Predicting the capacity of thin-walled beams at elevated temperature with machine learning C Couto, Q Tong, T Gernay Fire Safety Journal 130, 103596, 2022 | 14 | 2022 |
| A hierarchical Bayesian model for predicting fire ignitions after an earthquake with application to California Q Tong, T Gernay Natural Hazards 111 (2), 1637-1660, 2022 | 11 | 2022 |
| An explainable machine learning based flashover prediction model using dimension-wise class activation map L Fan, WC Tam, Q Tong, EY Fu, T Liang Fire Safety Journal 140, 103849, 2023 | 8 | 2023 |
| Numerical analysis of the effects of fire with cooling phase on reinforced concrete members T Gernay, J Pei, Q Tong, P Bamonte Engineering Structures 293, 116618, 2023 | 6 | 2023 |
| Predicting the Capacity of Slender Steel Columns at Elevated Temperature with Finite Element Method and Machine Learning Q Tong, C Couto, T Gernay Applications of Structural Fire Engineering, 2021 | 3 | 2021 |
| Applying machine learning to evaluate the performance of thin-walled steel members in fire Q Tong, C Couto, T Gernay Intelligent Building Fire Safety and Smart Firefighting, 2023 | 2 | 2023 |
| Resistance models for thin-walled steel beams under non-uniform temperature using machine learning C Couto, Q Tong, T Gernay 13th International Conference on Structures in Fire, 2024 | 1 | 2024 |
| Mapping wildfire ignition probability and predictor sensitivity with ensemble-based machine learning Q Tong, T Gernay Natural Hazards 119 (3), 1551-1582, 2023 | 1 | 2023 |
| Comparing analytical and machine-learning-based design methods for slender section steel members in fire C Couto, Q Tong, T Gernay 12th International Conference on Structures in Fire, 2022 | 1 | 2022 |
| An Interpretability Analysis Framework to Enhance Deep Learning Model Transparency: With a Study Case on Flashover Prediction Using Time-Series Sensor Data L Fan, Q Tong, H Fang, W Zhong, WC Tam, T Liang Fire Technology, 1-26, 2025 | | 2025 |
| Promoting AI Trustworthiness to Predict the Impacts of Internal Fires on Buildings Q Tong, H Fang, Y Bao, WC Tam Qi Tong, Hongqiang Fang, Yihai Bao, Wai Cheong Tam, 2024 | | 2024 |
| Examining the Effects of Deep Learning Model Structure on Model Interpretability for Time-Series Classifications in Fire Research WC Tam, L Fan, Q Tong, H Fang Journal of Physics: Conference Series 2885 (1), 012097, 2024 | | 2024 |
| Detecting Firefighter’s Thermal Risks in a Commercial Building Structure Using Machine Learning Q Tong, DW Stroup, WC Tam, H Fang Journal of Physics: Conference Series 2885 (1), 012082, 2024 | | 2024 |
Thomas GernayAssistant Professor of Engineering, Johns Hopkins UniversityPatvirtintas el. paštas jhu.edu