Phd in EARTH SCIENCES

Thesis title: A Framework for Multi-Hazard Assessment of Linear Infrastructure through Multi-Source Data Integration and Assimilation

The increasing frequency and intensity of natural hazards in recent decades have placed growing pressure on infrastructure systems worldwide. Linear assets such as roads, bridges, and tunnels are particularly vulnerable, as geohazard processes can compromise their structural stability and trigger cascading service disruptions with severe socio-economic consequences. Addressing these challenges requires not only a deeper understanding of how hazards interact with infrastructure networks but also the development of tools that enable timely and informed management decisions. To respond to these needs, the concept of Smart Geotechnical Asset Management (SGAM) has emerged, combining advances in monitoring technologies with data-driven decision-support systems. This integrated paradigm enables early-warning capabilities and predictive maintenance strategies, thereby enhancing both safety and economic sustainability. Building upon these foundations, this research introduces an innovative workflow for multi-hazard assessment of linear infrastructures, developed within the SGAM business service in collaboration with NHAZCA, a spin-off of Sapienza University of Rome specialising in satellite-based hazard monitoring. Supported by the European Space Agency (ESA), the project establishes a systematic methodology for data integration, hazard characterisation, and asset-focused analysis. The research first involved the creation and optimisation of a comprehensive geodatabase of relevant hazard datasets, with particular emphasis on the harmonisation of multiple landslide inventories into a single consolidated product. Subsequently, hazard assessment strategies were tailored to individual processes. Landslide susceptibility models were combined with interferometric (Persistent Scatterer) data to approximate intensity levels. Subsidence was evaluated using Persistent Scatterer analysis integrated with geological and topographic parameters. Liquefaction susceptibility was derived from geological predisposing factors and seismic inputs through a potential liquefaction index. In the absence of detailed hydraulic simulations, geomorphological indicators were employed to identify flood-prone sectors. A central challenge addressed by this research was the need to account for the specific vulnerability of linear infrastructure systems. To this end, simplified yet scalable procedures were developed to evaluate the potential impact of slope and ground-deformation processes, with slope units proving particularly effective as mapping entities at broader scales. Ultimately, the study proposes a multi-hazard assessment represented as a single integrated layer, which, given the regional scale, does not explicitly model hazard interdependencies but provides a coherent framework for analysing their cumulative impact. By linking hazard characterisation with asset-oriented data integration, this research advances the assessment of infrastructure resilience and offers a transferable framework to support proactive, risk-informed management of linear networks.