Horizon 2020 Marie Skłodowska-Curie Innovative Training Network

Matteo Vagnoli

BSc, MSc
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Matteo Vagnoli 2017-07-01T20:30:50+00:00
Early Stage Researcher
University of Nottingham (United Kingdom)

Project 9: Railway bridge condition monitoring and fault diagnostics

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Research Interests:

System reliability; Fault diagnostics; Risk assessment; Health monitoring; Nuclear safety; Sensitivity analysis; Dynamic reliability


Matteo Vagnoli gained a BSc in Energy Engineering from Politecnico di Milano in Italy. After graduating with honors in his MSc in Nuclear Engineering, he worked as a research fellow and teaching assistant in collaboration with the Laboratory of Signal and Risk Analysis (LASAR), under the supervision of Dott. Francesco Di Maio and Prof. Enrico Zio. His work was centered on developing innovative methods of risk-based post-processing and on-line clustering methods, computational methods for dynamic reliability analysis of industrial systems, as well as Mathematical and Statistical models within the Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) framework, and evaluating the impact of climate change in the Energy supply system.

Before joining TRUSS ITN in September 2015, he had been referee of scientific journals and conferences and participated in International conferences.

Research Outputs:

  • Di Maio, F., Vagnoli, M. and Zio, E. (2015), “Risk-Based Clustering for Near Misses Identification in Integrated Deterministic and Probabilistic Safety Analysis”, Science and Technology of Nuclear Installations, vol. 2015, Article ID 693891, 29 pages, 2015. DOI: http://dx.doi.org/10.1155/2015/693891
  • Sahlin, U., Di Maio, F., Vagnoli, M. and Zio E. (2015) “Evaluating the impact of climate change on the risk assessment of nuclear power plants”, Safety and Reliability of Complex Engineered Systems, Taylor & Francis Group, London, ISBN 978-1-138-02879-1.

Publications in TRUSS
 More than 35% of the European railway bridges are over 100 years old and the increasing traffic loads are pushing the railway infrastructure to its limits. Bridge condition-monitoring strategies can help the railway industry to improve safety, availability and reliability of the network. In this paper, a Bayesian Belief Network method for condition monitoring and fault detection of a truss steel railway bridge is proposed by relying on a fuzzy analytical hierarchy process of expert knowledge. The BBN method is proposed for obtaining the bridge health state and identifying the most degraded bridge elements. A Finite Element model is developed for simulating the bridge behaviour and studying a degradation mechanism. The proposed approach originally captures the interactions existing between the health state of different bridge elements and, furthermore, when the evidence about the displacement is introduced in the BBN, the health state of the bridge is updated.