Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning

This study proposes a novel approach for real-time strength monitoring and risk assessment of building structures by leveraging machine learning for concrete compressive strength prediction. The assessment of the prevailing Reinforced Concrete (RC) buildings for a seismic damage is a hard structural engineering trouble and a key problem for disaster mitigation and resilience. The seismic damage evaluation of those structures aids in figuring out whether or not the buildings can be used effectively after the earthquake by knowing the chance of damage degrees. We developed a machine learning model capable of analyzing various concrete mix parameters, including the amount of cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and the age of the concrete. The model predicts the compressive strength, a crucial indicator of a RCC column's structural integrity. In structural engineering, assessing the seismic susceptibility of the existing reinforced concrete (RC) buildings is a critical task that is essential to resilience and catastrophe preparedness. Seismic Risk Assessments (SRA) of these structures help determine whether a building is safe for post-earthquake use by tracking the likelihood of damage. approach allows for continuous assessment of a building's structural health, enabling proactive identification of potential risks. The inclusion of this technology into current monitoring systems offers building managers and engineers actionable insights which help them make informed decisions about maintenance and repair requirements. This research establishes new methods for proactive and effective building structure risk assessment which improves safety and extends the life span of constructed environments.