A Machine Learning Framework for Concrete Workability Estimation

Project Member(s): Zhang, J., Sirivivatnanon, V., Chang, X.

Funding or Partner Organisation: Australian Research Council (ARC Linkage Projects)
Australian Research Council (ARC Linkage Projects)
Gunlake Concrete NSW Pty Ltd
Boral Limited

Start year: 2023

Summary: Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons for rejection at construction sites, resulting in significant costs, waste, and delays. Multimodal data sources will be used to provide a reliable workability estimate in real time, enabling construction teams to identify and rectify workability issues in transit while continuously monitoring the adjustments effects.

FOR Codes: Pattern recognition, Computer vision, Expanding knowledge in the information and computing sciences, Cement products and concrete materials, Workplace safety

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.