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.

混凝土是澳大利亚最常用的建筑材料。该项目旨在开发一个系统，以使用高级机器视觉和机器学习来衡量搅拌机中混凝土在搅拌器中的运输中的可行性，并为当前的视觉测试测试仪使用认证的测试仪提供可靠的替代方法。无法满足可加工性要求的混凝土是建筑工地拒绝的最常见原因之一，导致了大量成本，浪费和延误。多模式数据源将用于实时提供可靠的可加工性估计，从而使施工团队能够识别和纠正运输中的可加工性问题，同时不断监视调整效果。