Project description
Growing geotechnical engineering with machine learning
Geotechnical engineering faces major challenges due to climate change and the need for sustainable building practices. Engineers must work with soils that vary greatly in their properties, making construction difficult and often unpredictable. Traditional methods for ensuring safe, efficient designs are slow and consume large amounts of materials, especially concrete, which has a high carbon footprint. As infrastructure demands grow, new methods are essential to make construction more efficient and environmentally friendly. In this context, the EU-funded GRID project aims to bring machine learning into geotechnical engineering to solve these issues. By improving calculations, addressing soil variability, and optimising designs, GRID seeks to reduce material use, lower emissions and make construction safer and more resilient.
Objective
Our proposed research initiative seeks to propel machine learning into the forefront of geotechnical engineering, with a vision to address critical challenges and revolutionise the field for the betterment of society. The overarching goals of our project align with the need to confront uncertainty, combat climate change through zero carbon emission strategies, address soil parameter heterogeneity, expedite finite element (FE) calculations e.g. for reliability analyses, and enhance design efficiency to reduce material consumption, particularly in the context of concrete. By undertaking this multidimensional approach, our research aims not only to apply machine learning in geotechnical engineering but to fundamentally transform the field, ushering in a new era of efficiency, sustainability and resilience. Through collaboration and innovation, we aspire to make machine learning an integral and indispensable tool for addressing the complex challenges faced by geotechnical practitioners in the 21st century.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. This project's classification has been validated by the project's team.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. This project's classification has been validated by the project's team.
Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff ExchangesCoordinator
1180 Wien
Austria