Nisrine Makhoul was awarded one of the Seal of Excellence 2020 and will spend 2 years at DABC collaborating with the research group of prof. Maria Pina Limongelli.
The project ‘Cyber-Physical System Modelling for Resilience Management of Bridges’ will be carried out in collaboration with prof. Michael Havbro Faber of Aalborg University (DK).
The Project
On 14 August 2018, 43 people died in the collapse of the so-called Morandi bridge in Genova; beyond the human tragedy, this event reminded us all of the decadent state of critical transport infrastructure in the EU. Bridges built in the last century are aging all over the world and need continuous maintenance. This will be a growing challenge in the next decades due to population growth, projected effects of climatic changes and not least to the scarcity of resources available for maintenance. Research efforts on bridge integrity management in the last decades have been devoted to risk-based approaches to that do not account for the indirect consequences associated with long recovery times. More recent research, focused on the concept of resilience, implicitly consider the bridge as a ‘standalone’ object exposed to a well-defined disruption. This does not reflect the complexity of the real world, which is formed by increasingly interconnected networks and calls for a systemic approach enabling to define resilience within and across these domains, highlighting their connections.
My vision is that information plays a pivotal role for resilient management of bridge systems, connecting and supporting decisions at the different hierarchical levels and across the several domains involved in the decision process. However, the increasing interconnectedness and interdependence that information enables, also leads to the emergence of safety and reliability problems related to the quality of the information. In this project emphasis will be on information from Structural Health Monitoring.
Resilience metrics and models for information quality will be developed and integrated into a Bayesian Probabilistic Network that will be the base of a decision support tool (DST) for the optimization of monitoring systems for resilience management of bridges.