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Grant Award View - GA292452
A Machine Learning Framework for Concrete Workability Estimation
GA ID:
GA292452
Agency:
Australian Research Council
Approval Date:
16-Mar-2023
Publish Date:
29-Mar-2023
Category:
Humanities, Arts and Social Sciences (HASS) Research
Grant Term:
16-Mar-2024 to 15-Mar-2027
Original: 16-Mar-2023 to 31-Dec-2025
Value (AUD):
$455,969.00
(GST inclusive where applicable)
Variations:
- GA292452-V1 - Variation to Grant (17-May-2023 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 22/23 Linkage
Grant Program:
Linkage Projects
Grant Activity:
A Machine Learning Framework for Concrete Workability Estimation
Purpose:
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.
GO ID:
GO Title:
Linkage Projects for funding applied for in 2022
Internal Reference ID:
LP22 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
University of Technology Sydney
Recipient ABN:
77 257 686 961
Grant Recipient Location
Suburb:
ULTIMO
Town/City:
ULTIMO
Postcode:
2007
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2007
Country:
AUSTRALIA