| 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 | 64 | 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 | 60 | 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 | 43 | 2018 |
| 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 | 21 | 2018 |
| Resilience assessment of process industry facilities using dynamic Bayesian networks Q Tong, T Gernay Process Safety and Environmental Protection 169, 547-563, 2023 | 16 | 2023 |
| 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 | 13 | 2022 |
| 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 | 12 | 2023 |
| 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 | 7 | 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 | 5 | 2022 |
| 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 |
| 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 | 1 | 2023 |
| Mapping wildfire ignition probability and predictor sensitivity with ensemble-based machine learning Q Tong, T Gernay Natural Hazards 119 (3), 1551-1582, 2023 | | 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 | | 2023 |
| DATA ANALYSIS AND MACHINE LEARNING FOR ENHANCING RESILIENCE TO FIRE, FROM IGNITION MAPPING TO STRUCTURAL AND SYSTEMS MODELING Q Tong Johns Hopkins University, 2023 | | 2023 |
| Mapping wildfire ignition probability with ensemble-based machine learning models Q Tong, T Gernay ASCE 2023 Engineering Mechanics Institute Conference, p. 463, 2023 | | 2023 |
| A dynamic Bayesian network approach to assess resilience to cascading events in industrial facilities Q Tong, T Gernay ASCE 2023 Engineering Mechanics Institute Conference, p. 77, 2023 | | 2023 |
| 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 | | 2023 |
| Leveraging Machine Learning to Predict the Structural Capacity of Slender Steel Members at Elevated Temperature Qi Tong, Carlos Couto, Thomas Gernay Engineering Mechanics Institute Conference, 2022 | | 2022 |
| COMPARING ANALYTICAL AND MACHINE-LEARNING-BASED DESIGN METHODS FOR SLENDER SECTION STEEL MEMBERS IN FIRE C Couto, Q Tong, T Gernay | | |
Thomas GernayAssistant Professor of Engineering, Johns Hopkins UniversityVerified email at jhu.edu
