Smart monitoring, testing and predicting
The construction sector is highly inefficient. This is mostly caused by overdesign to reduce the amount of quality control required for the capital intensive and high-risk projects that the sector deals with. Hence, there is a need for easy-to-use quality control solutions able to provide reliable affordable data for better assessment of concrete quality and prediction of their properties.
The C3S research group is on a mission to develop cheap and easy-to-use wireless sensors able to generate meaningful data and promote informed decision-making during construction projects and management of existing buildings and infrastructures. We develop our own sensors in collaboration with different cross-sectoral partners and provide customized services related to sensor testing, validation and product design to external companies.
The gradual widespread use of data-generation technologies during the life-cycle of concrete demands innovative data analytics to digest this increased data inflow and promote reliable data-driven decisions in real time. The C3S research group combines concrete and data analytics knowledge to develop and deploy customized AI and machine learning solutions for a wide variety of applications within the construction sector (optimize concrete production, testing, quality control, construction operations, etc.).
Machine learning algorithms have been getting increasing attention in civil engineering applications during the last decade. The mathematical framework of this technology usually outperforms the prediction power of traditional regression approaches when it comes to understand input-output relationship in complex systems. Moreover, the full data-driven approach provides a different perspective to researchers and practitioners which often highlights unseen patterns that remained unnoticed as these might confront the current established understanding of the phenomenon.