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通过计算机视觉自动估计水泥度

Hossein Kabir¹, Jordan Wu², Sunav Dahal¹

¹Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Nature communications

|November 15, 2024

概括

一个新的计算机视觉模型自动监测水吸收在水泥材料,提供实时耐用性评估. 这种方法取代了传统的,劳动密集型的重量测量,以改进建筑材料分析.

科学领域:

材料科学材料科学材料科学
土木工程土木工程是指土木工程.
计算机视觉计算机视觉

背景情况:

监测水吸收对于评估水泥材料的耐用性与环境因素相比至关重要.
像ASTM C1585这样的当前方法依赖于手动,不频繁的重量测量,限制实时分析.
开发自动化,准确的水吸收监测方法对于建筑材料科学至关重要.

研究的目的:

开发和验证计算机视觉模型,用于在水泥系统中自动实时监测水的吸收.
用一种高效的,自动化的方法来取代劳动密集型的传统方法来评估耐用性.
为了能够准确地预测度值,以提高材料性能评估.

主要方法:

通过使用6234张图像 (4000张真实图像,234张合成图像) 来训练一个定制的计算机视觉模型.
该模型在15个糊混合物的1440个数据点上进行了训练 (w/c比为0.4-0.8,固化时间为1-7天).
该模型自动检测镜样本中的水位,每分钟估计水的透率.

主要成果:

计算机视觉模型准确地预测了实时的初始和二次度 (R2 > 0.9).
在对各种糊状混合物进行训练后,在预测方面取得了很高的信心.
在砂和混凝土系统上证明了成功的应用.

结论:

开发的计算机视觉模型提供了一个低成本的自动化解决方案,用于监测水吸收.
这项技术有助于实时评估建筑材料的耐用性.
为改善建筑行业的质量控制和材料开发铺平了道路.

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