PhD Students 

Understanding and evaluating the behaviour of masonry structures against extreme hazards is of global importance to ensure the safety of those structures and their inhabitants. The project aims to developing a novel methodology based on artificial intelligence (AI) approach to predict the behaviour of masonry elements to a wide range of loading scenarios, including those from extreme hydraulic loads and impact of waterborne debris. The objective of this project is to conceptualise a novel AI approach to the modelling of masonry elements by building a database from new numerical simulations and data from literature suitable to train, validate and test the specific AI approach.

I am a PhD student working between the University of Nottingham and the British Geological Survey. My research focusses on modelling the retreat of cliff-beach-platform systems under the effects of climate change. I hope to improve existing models of this type of coast, and better quantify uncertainty in model predictions of coastal recession by the year 2100. The two main coasts studied will be Happisburgh, Norfolk, UK, and Montauk, NY, US. The project involves visiting Stony Brook University, NY for 6 months, close to Montauk, to better understand the difference in coastal recession in differing climates. 

After his Master’s degree in Hydraulic Engineering at the University of Pisa, Tommaso joined the EFMG research group in 2019 as a PhD student. His research focuses on the numerical-experimental modelling of waves impacting on rigid and flexible plates. Waves impacting on coastal structures, referred to as wave-structure interaction (WSI), pose a serious hazard for a range of coastal applications such as oil and gas rigs, offshore wind turbine platforms, breakwaters, flood protection systems and wave energy converters. These structures may deform under the wave loading, making the WSI effects even more relevant. An accurate understanding of WSI is still a major challenge for the engineering community.  

The effect of the water body geometry is crucial for reliable hazard assessment as it can alter the landslide-tsunami heights by more than an order of magnitude. The PhD student will investigate landslide-tsunamis in idealised converging and real water bodies with computer simulations with the open source code SWASH, calibrated and validated with available unique high quality physical model data from the PI. The findings will enhance our physical understanding of landslide-tsunamis and improve the reliability of landslide-tsunami hazard assessment.

Residential masonry buildings are characterised by significantly low tensile strength and brittle behaviour. Such characteristics make them vulnerable to natural hazards, such as earthquakes and extreme hydrodynamic events (e.g. floods or tsunamis) (Kelman, 2002; Shoji et al., 2014; Korswagen et al., 2022). A large number of studies on the performance and resilience of masonry structures against earthquakes during the last decades, but less attention has been given to extreme hydrodynamic events. With the increased frequency and intensity of extreme hydrodynamic events under climate change conditions, there is a critical need to understand masonry structures' response to such events (Thompson et al., 2017; Otto et al., 2018).