Risk-Targeted Design of Buildings against Floods

Project ID: 2531ad1574

(You will need this ID for your application)

UCL Lead department: Risk and Disaster Reduction

Project Summary:

Since 2000, there have been nine record-breaking months of rainfall in the UK. Due to a four-million increase in population by 2045, and shortage of housing, one in 10 new UK constructions will be built on high flood hazard areas. As a result, by 2050, one in three UK homes will be at risk of flooding. If buildings in high-hazard areas are inevitable, reducing vulnerability is the only viable option to invert the above trend.

To achieve it, designers must reduce losses relating to society (e.g., human displacement), environment (e.g., carbon embodied in repairs), and economic damage. Refined design usually involves trial-and-error procedures where a guessed building configuration is assessed and continuously revised until a target loss is met. This includes resource-intensive probabilistic estimations of hazard, building structural response, water ingress through the envelope, and loss modelling accounting for the contents and any property flood resilience measures (e.g., flood gates, non-return valves). Human resources and/or technical skills for this paradigm are often lacking. Thus, design neglects portions of the involved physical processes and does not allow controlling/targeting selected values of losses (i.e., it is not risk-targeted).

This project removes the need for design trial-and-error by substituting the above resource-intensive estimations with a computationally cheap machine learning approach. This allows explicitly targeting selected flood loss values. Unlike existing approaches, this methodology allows to: explicitly model water ingress and related damage processes of building envelope and contents; include property flood resilience measures; tackle the cases of new construction, retrofitting, and build back better; include flood insurance for the residual risk; model lifecycle direct and indirect losses (e.g., lifecycle cost of repairs, lifecycle recovery time); include cost-benefit analysis in both monetary and embodied carbon terms.

The ideal candidate is skilled in hydraulics, structural engineering, and coding. Skills in probabilistic risk assessment are desirable.