| Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges M Vagnoli, R Remenyte-Prescott, J Andrews Structural Health Monitoring 17 (4), 971-1007, 2018 | 137 | 2018 |
| Risk-based clustering for near misses identification in integrated deterministic and probabilistic safety analysis F Di Maio, M Vagnoli, E Zio Science and Technology of Nuclear Installations 2015, 2015 | 23 | 2015 |
| Transient identification by clustering based on Integrated Deterministic and Probabilistic Safety Analysis outcomes F Di Maio, M Vagnoli, E Zio Annals of Nuclear Energy 87, 217-227, 2016 | 20 | 2016 |
| A Bayesian ensemble of sensitivity measures for severe accident modeling SM Hoseyni, F Di Maio, M Vagnoli, E Zio, M Pourgol-Mohammad Nuclear Engineering and Design 295, 182-191, 2015 | 17 | 2015 |
| Determination of prime implicants by differential evolution for the dynamic reliability analysis of non-coherent nuclear systems F Di Maio, S Baronchelli, M Vagnoli, E Zio Annals of Nuclear Energy 102, 91-105, 2017 | 11 | 2017 |
| A fuzzy-based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection M Vagnoli, R Remenyte-Prescott, J Andrews Proc. European Safety and Reliability ESREL2017, 18-22, 2017 | 9 | 2017 |
| Evaluating the impact of climate change on the risk assessment of Nuclear Power Plants U Sahlin, F Di Maio, M Vagnoli, E Zio Safety and Reliability of Complex Engineered Systems-Proceedings of the 25th …, 2015 | 9 | 2015 |
| Ensembles of climate change models for risk assessment of nuclear power plants M Vagnoli, F Di Maio, E Zio Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2018 | 8 | 2018 |
| An automatic bridge damage diagnostics method using empirical mode decomposition based health indicators and neuro‐fuzzy classification M Vagnoli, R Remenyte‐Prescott Structural Control and Health Monitoring 29 (10), e3027, 2022 | 7* | 2022 |
| A Bayesian Belief Network method for bridge deterioration detection M Vagnoli, R Remenyte-Prescott, J Andrews Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2021 | 7 | 2021 |
| An ensemble-based change-point detection method for identifying unexpected behaviour of railway tunnel infrastructures M Vagnoli, R Remenyte-Prescott Tunnelling and underground space technology 81, 68-82, 2018 | 6 | 2018 |
| A machine learning classifier for condition monitoring and damage detection of bridge infrastructure M Vagnoli, J Andrews Train. Reducing Uncertain. Struct. Saf 1, 53, 2018 | 6 | 2018 |
| Updating conditional probabilities of Bayesian belief networks by merging expert knowledge and system monitoring data M Vagnoli, R Remenyte-Prescott Automation in Construction 140, 104366, 2022 | 5 | 2022 |
| Structural Health Monitoring Developments in TRUSS Marie Sklodowska-Curie Innovative Training Network A González, F Huseynov, B Heitner, D Martinez, S Chen, EJ O'Brien, ... | 4 | 2017 |
| Railway bridge condition monitoring and fault diagnostics M Vagnoli University of Nottingham, 2019 | 3 | 2019 |
| Structural health monitoring of bridges: A Bayesian network approach M Vagnoli, R Remenyte-Prescott, J Andrews Life Cycle Analysis and Assessment in Civil Engineering: Towards an …, 2018 | 2 | 2018 |
| Towards a real-time Structural Health Monitoring of railway bridges M Vagnoli, R Remenyte-Prescott, J Andrews The 52nd ESReDA Seminar On Critical Infrastructures: Enhancing Preparedness …, 2017 | 2 | 2017 |
| Railway bridge fault detection using Bayesian belief network M Vagnoli, R Remenyte-Prescott, J Andrews | 2 | 2017 |
| TRUSS-ITN methods for detecting bridge damage from response to traffic A González, D Martínez, EJ O’Brien, M Casero, JJ Moughty, JR Casas, ... Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges …, 2018 | 1 | 2018 |
| A Data Mining Tool for Detecting and Predicting Abnormal Behavior of Railway Tunnels M Vagnoli, R Remenyte-Prescott, D Thompson, J Andrews, P Clarke, ... Structural Health Monitoring 2017, 2017 | | 2017 |
