Climate Change and Extreme Weather Increase the Risk of Building Damage. To address this risk, the project aims to develop user-friendly, data and machine learning-driven solutions that analyze building damage and propose tailored restoration measures.
The solutions will also assess the risk of future climate-related building damage for individual buildings, suggest preventive measures, and be accessible to both professionals and building owners without technical expertise.
Innovative solutions that build on new knowledge and integrate existing data sources – such as climate data, damage statistics, and best practice solutions – can provide precise, customized, and cost-effective damage mitigation measures. By leveraging advanced data collection, machine learning, and climate models, the solution will predict geographically precise climate-related risks and offer concrete recommendations on how buildings can be improved and protected against future weather challenges, ensuring sustainable preventive and restorative measures.
The project focuses on three key innovations: precise damage assessment, climate risk analysis, and learning from damage data. To achieve this, research and development are required in all three areas.
Through activities in four work packages, the project will develop and analyze applicable climate scenarios for climate-adapted risk assessment of building solutions, create a standardized platform for collecting and analyzing building damage data, develop machine learning models for damage identification, and design recommendation algorithms for preventive measures. A key success factor for the project is collaboration among project partners to integrate innovations from the four work packages.
Additionally, a fifth work package aim to promote collaboration and knowledge-sharing with the broader industry through publications and conference presentations, contributing to reducing society’s vulnerability to climate-related building damage.