Promotion of CO2 sequestration:
The overall research goal is to promote CO2 sequestration in cement-based materials and reduce the carbon footprint of the cement industry. This can be achieved by investigating the potential of incorporating alternative materials and developing novel processes and techniques that can enhance the carbonation of cement-based materials. The purpose is to reduce global CO2 emissions and fight against climate change. This research opens new perspectives to develop new sustainable multifunctional materials, which will move towards better and more environmentally friendly construction.
Cost-effective sustainable materials:
The overall research goal is to develop cost-effective sustainable materials that can meet the growing demand for environmentally friendly and socially responsible building materials. The research aims to explore the use of alternative materials and manufacturing processes that can reduce the environmental impact of construction activities, including reducing energy consumption, greenhouse gas emissions, and waste generation. However, alternative and innovative materials are usually more expensive than traditional materials. The research also aims to assess the economic viability of sustainable materials and identify strategies to promote their adoption in the construction industry. The proposed research can contribute to the development of sustainable and affordable building materials, improve the resilience of the built environment, and promote the transition towards a more sustainable and circular economy.
Self-informed system to predict future behavior:
The overall research goal is to develop a self-informed system that can predict the future behavior of concrete structures by leveraging machine learning and artificial intelligence techniques. The research aims to explore the potential of developing a system that can learn from past behavior and use this knowledge to make accurate predictions about future behavior of concrete structures. The primary focus is to develop a system that can adapt and learn from feedback, in order to continuously improve the accuracy of its predictions. The research also aims to assess the potential of the self-informed system to enhance decision-making and improve the performance of concrete structures across a wide range of applications, such as structural health monitoring, risk assessment, and predictive maintenance. The proposed research can contribute to the development of more intelligent and efficient systems for managing and maintaining concrete structures, leading to improved safety, durability, and sustainability.