Real-time prediction of early concrete compressive strength using AI and hydration monitoring.

The continuous evolution of construction technologies, particularly in reinforced concrete production, demands advanced, reliable, and efficient methodologies for real-time monitoring and prediction of concrete compressive strength. Traditional laboratory methods for assessing compressive strength are time-intensive and can introduce delays in construction workflows. This study introduces a comprehensive framework for a system designed to predict early-age compressive strength of concrete through continuous monitoring of the cement hydration process using a custom artificial intelligence (AI) model. The system integrates a network of temperature sensors, communication modules, and a centralized database server to collect, transmit, and analyze real-time data during the concrete curing process. The AI model, a deep neural network leverages this data to generate accurate strength predictions. The system architecture emphasizes scalability, robustness, and integration with existing construction management systems. Empirical results indicate that the proposed system achieves high predictive accuracy, with an R