A Machine Learning Framework for Concrete Workability Estimation

用于混凝土和易性评估的机器学习框架

• 批准号: LP220100390
• 负责人: Prof Jian Zhang
• 金额: $ 31.94万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-03-16 至 2027-03-15
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP220100390
• 关键词: Machine; Learning; Framework; Concrete; Workability

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.

混凝土是澳大利亚使用最多的建筑材料。该项目旨在开发一种系统，利用先进的机器视觉和机器学习来测量搅拌车运输中混凝土的工作性，并为目前由认证测试员目测混凝土工作性的做法提供可靠的替代方案。混凝土不符合工作性要求是建筑工地最常见的拒收原因之一，导致大量成本、浪费和延误。将使用多模式数据源实时提供可靠的工作性估计，使施工队能够识别和纠正运输中的工作性问题，同时持续监测调整效果。