Real-Time Concrete Workability Estimation in Transit via an IoT-Enabled Cyber-Physical System

Concrete quality on construction sites depends on maintaining certain workability during transportation. However, traditional slump testing is manual and cannot assess concrete in transit. This study presents a cyber-physical system (CPS) integrating IoT sensors with machine learning models to estimate concrete workability in real time during delivery. The proposed approach first correlates traditional slump test parameters with sensor measurements, allowing for automated replication of established workability evaluation. The CPS continuously captures IoT sensor data, such as drum rotation, vibration, internal pressure, and temperature, processes these data in real time, and infers workability before unloading. The developed CPS was validated in a real-world case study, achieving high workability prediction accuracy with an average error of approximately 0.9 cm and timely, automated feedback of below 200 ms. These results enable continuous in-transit monitoring of concrete workability and lay a practical foundation for data-driven operational improvements in construction logistics.