一种利用三维扫描和监督学习表征和预测混凝土骨料形态特征的方法

IF 8 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Yong Ding , Dewen Liu , Yu Jiang , Hongying Cai , Zixiang Zhou , Jianxiong Zhang , Yongbing Sun , Danna Ma
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引用次数: 0

摘要

为实现对混凝土粗集料内在几何特性的精细化分析,本研究开发了一种快速、准确的检测方法。利用先进的三维扫描技术,对混凝土CA的几何形态特征进行了深入的研究。引入了一个创新指标“等效粒径(D)”来表征骨料的三维属性。利用MATLAB进行全面二次开发,分析几何特征,计算分形维数(即渐变、表面积、粗糙度)。级配的分形维数反映了粒度分布的复杂性;表面面积的分形维数量化了纹理的不规则性;粗糙度的分形维数描述了轮廓的变化。建立了“三维扫描、二次开发和监督学习”相结合的预测框架,对D进行准确估计。结果表明,对总质量(M)和D的预测精度均较高,D的平均预测误差(APE)显著降低至1.78 %。二维(2D)指标主导M预测,三维指标更适合预测d。提出的预测模型不仅为混凝土骨料筛选提供了高效、精确的解决方案,也为今后混凝土骨料优化设计奠定了理论和技术基础。这项工作突出了将3D扫描与机器学习相结合的潜力和优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A method for characterizing and predicting the morphological features of concrete aggregates using 3D scanning and supervised learning
To achieve refined analysis of intrinsic geometric characteristics of concrete coarse aggregates (CA), this study develops a rapid, accurate testing method. Leveraging advanced three-dimensional (3D) scanning technology, an in-depth investigation into geometric morphological features of concrete CA was conducted. An innovative indicator, "equivalent particle size (D)" was introduced to characterize the 3D attributes of aggregates. MATLAB was utilized for comprehensive secondary development to analyze geometric features and compute fractal dimensions (i.e. gradation, surface area, and roughness). The fractal dimension of gradation reflects particle size distribution complexity; the fractal dimension of surface area quantifies texture irregularity; the fractal dimension of roughness describes contour variation. A predictive framework integrating "3D scanning, secondary development and supervised learning" was established for accurate estimation of D. Results show superior precision in predicting both total mass (M) and D, and the average prediction error (APE) of D was significantly reduced to 1.78 %. Two-dimensional (2D) indicators dominate M prediction, while 3D indicators are more suitable for predicting D. The proposed prediction model not only provides an efficient and precise solution for concrete aggregate screening but also establishes a theoretical and technical foundation for future optimization design of concrete aggregates. This work highlights the potential and advantages of combining 3D scanning with machine learning.
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来源期刊
Construction and Building Materials
Construction and Building Materials 工程技术-材料科学:综合
CiteScore
13.80
自引率
21.60%
发文量
3632
审稿时长
82 days
期刊介绍: Construction and Building Materials offers an international platform for sharing innovative and original research and development in the realm of construction and building materials, along with their practical applications in new projects and repair practices. The journal publishes a diverse array of pioneering research and application papers, detailing laboratory investigations and, to a limited extent, numerical analyses or reports on full-scale projects. Multi-part papers are discouraged. Additionally, Construction and Building Materials features comprehensive case studies and insightful review articles that contribute to new insights in the field. Our focus is on papers related to construction materials, excluding those on structural engineering, geotechnics, and unbound highway layers. Covered materials and technologies encompass cement, concrete reinforcement, bricks and mortars, additives, corrosion technology, ceramics, timber, steel, polymers, glass fibers, recycled materials, bamboo, rammed earth, non-conventional building materials, bituminous materials, and applications in railway materials.
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