Toward Intelligent Construction: AI-Driven Autonomous Monitoring of Fresh Concrete Flow Behaviour

Abstract

Accurate assessment of the flow behaviour of fresh concrete is essential for ensuring stability, mechanical performance, and durability. Flow behaviour governs key construction processes including mixing, pumping, and placement. While rheometers provide precise measurements, they are costly, complex, and impractical for field use. Consequently, alternative techniques have gained attention for real-time and on-site monitoring. These approaches evaluate flow characteristics and track mixing by capturing measurable signals such as images, sound, vibration, or power consumption. Compared to rheometers, they offer advantages in speed, automation, and minimal disruption. This review critically examines the principles, accuracy, technology readiness, applicability, and limitations of these emerging approaches, and explores future directions. It also emphasizes artificial intelligence (AI) integration to enhance data processing and automation and examines multimodal strategies such as multi-sensor fusion, multi-source data fusion, and large language models to improve analysis and decision making. These advances support the vision of Industry 4.0 through intelligent and connected concrete production systems.