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Active FELLOWSHIP UKRI Gateway to Research

HiFiMacrEL - High-Fidelity Macroscale Modelling of Historical Masonry Structure under Earthquake Loading

£2.06M GBP

Funder Horizon Europe Guarantee
Recipient Organization Imperial College London
Country United Kingdom
Start Date Dec 01, 2024
End Date Nov 30, 2026
Duration 729 days
Number of Grantees 2
Roles Fellow; Principal Investigator
Data Source UKRI Gateway to Research
Grant ID EP/Z002192/1
Grant Description

Earthquake loading is a major threat to the safety of historical unreinforced masonry (URM) structures (e.g. old masonry buildings, bridges, and monuments) located in earthquake-prone regions of Europe. The damage or collapse of URM structures with historical and cultural value not only threatens the safety of people but also brings extremely adverse social and economic effects.

Therefore, reliable predictions of the dynamic response of historical URM structures under seismic loading are excessively important. As the existing microscale and mesoscale modelling strategies which model masonry units and mortar joints individually often impose prohibitive computational demands, the macroscale modelling strategy which reduces the modelling effort and computational cost receives wide applications in both research and practice. However, the existing macroscale models often ignore important

mechanical behaviour of masonry under seismic loading, limiting their applicability to specific problems. To overcome the deficiencies of existing macroscale models, this project will develop an advanced and general macroscale modelling strategy which fully replicates the important mechanical phenomena involved in URM structures under seismic loading, and propose an efficient homogenization method to generate needed macro-level information for nonlinear dynamic structural analysis.

The proposed numerical approach will be validated and applied to two case studies involving realistic historical URM structures which were damaged by earthquakes.

All Grantees

Imperial College London

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