Multi-hazard Vulnerability Assessment of Structures for Resilience Enhancement (MultiVERSE)
Lead Research Organisation:
University College London
Department Name: Civil Environmental and Geomatic Eng
Abstract
MultiVERSE aims to develop an innovative, robust and harmonised multi-hazard design and assessment methodology for civil infrastructures with full consideration of relevant structural, technological, economic, environmental, regulatory, and sociocultural (STEERS) factors. To achieve this aim, MultiVERSE proposes a novel performance-based multi-hazard engineering (PBME) framework (earthquake sequences and earthquake-tsunami sequences) for multi-hazard vulnerability assessment of structures in coastal regions close to tectonically active zones in Europe (e.g., Greece, Turkey, Italy). By adopting a cross-disciplinary decision-making methodology, the PBME framework addresses issues ranging from hazard interactions and their cumulative damaging effect on structural performance to the expected losses for efficient resilience-enhancing design and assessment of civil infrastructures. Although the framework is applicable to all construction types, this project focuses on reinforced concrete (RC) buildings because they represent a significant proportion of buildings in most multi-hazard-vulnerable European countries. The framework will be applied to individual buildings and a large-scale building portfolio in Italy, where the Supervisor has strong links with local stakeholders, thus ensuring data availability, knowledge transfer and actual impact of the research on local communities. MultiVERSE
builds on the probabilistic catastrophe risk modelling expertise of the Supervisor and Host Institution and the RC seismic design and assessment expertise of the Fellow. The PBME framework will promote multi-hazard risk reduction by providing better guidance to practising engineers and various stakeholders for designing new structures and assessing existing structures in multi-hazard prone regions, and practice-oriented tools for optimal decision-making in pre- and post-disaster settings (e.g., post-event building tagging and optimal repair or retrofit technique selection and design).
builds on the probabilistic catastrophe risk modelling expertise of the Supervisor and Host Institution and the RC seismic design and assessment expertise of the Fellow. The PBME framework will promote multi-hazard risk reduction by providing better guidance to practising engineers and various stakeholders for designing new structures and assessing existing structures in multi-hazard prone regions, and practice-oriented tools for optimal decision-making in pre- and post-disaster settings (e.g., post-event building tagging and optimal repair or retrofit technique selection and design).
Publications
Aljawhari K
(2022)
A fragility-oriented approach for seismic retrofit design
in Earthquake Spectra
Cremen G
(2022)
Modelling and quantifying tomorrow's risks from natural hazards
in Science of The Total Environment
Cremen G
(2022)
Investigating the potential effectiveness of earthquake early warning across Europe
in Nature Communications
Cremen G
(2022)
A Simulation-Based Framework for Earthquake Risk-Informed and People-Centered Decision Making on Future Urban Planning
in Earth's Future
Francis T
(2023)
Seismic fragility of reinforced concrete buildings with hollow-core flooring systems
in Bulletin of the New Zealand Society for Earthquake Engineering
Galasso C
(2023)
Assessing the potential implementation of earthquake early warning for schools in the Patras region, Greece
in International Journal of Disaster Risk Reduction
Galasso C
(2024)
The 2023 Kahramanmaras Earthquake Sequence: finding a path to a more resilient, sustainable, and equitable society
in Communications Engineering
Galasso C
(2022)
Resilient infrastructure
in Communications Engineering
Handa Y
(2024)
A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan
in Sustainable and Resilient Infrastructure
McCloskey J
(2023)
Reducing disaster risk for the poor in tomorrow's cities with computational science
in Nature Computational Science