通过高分辨率计算机断层扫描成像进行材料毛孔度估计的先进堆叠建模技术
Bubryur Kim1, Sri Preethaa K R2, Yuvaraj Natarajan3
1School of Space Engineering Sciences, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, Korea.
Scientific reports
|March 4, 2026
概括
一个新的深卷积神经网络 (DeepCNN) 框架自动化了2D CT 扫描中的混凝土孔隙度估计. 这种先进的方法提供了准确,高效的材料表征,大大改善了传统的技术.
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:
- 传统的混凝土孔径测量是手动的,耗时的,在低分辨率或噪音较高的成像中缺乏稳定性.
- 准确的孔隙性评估对于混凝土材料的表征和性能预测至关重要.
研究的目的:
- 开发一种自动化框架,用于使用深度卷积神经网络 (DeepCNN) 和2DCT扫描图像进行混凝土孔隙度估计.
- 提高孔径测量的精度和效率,特别是在具有挑战性的成像条件下.
主要方法:
- 在增强型CT图像上训练了一个DeepCNN架构,具有多级特征提取器和基于SPP的部.
- 图像处理包括自动化ROI检测,规范化和类特定过.
- 基于规则的自适应值 (RBAT) 策略用于材料分类和孔隙度估计.
主要成果:
- 该框架准确地估计了各种混凝土类型 (CM,GM,UHPC) 的孔隙性,与真空平面仪测量相比,偏差在1.3-1.5%之间.
- 在DeepCNN分类器实现精确回忆AUC的1.0.0.
- 自动化方法在低分辨率和杂的成像条件下证明了稳定性.
结论:
- 拟议的混合框架为混凝土孔隙性评估提供了一个准确,自动化和计算效率高的解决方案.
- 这种方法适用于基于CT的实用和工业材料表征工作流程.
- 该研究强调了DeepCNN在先进的非破坏性材料分析方面的潜力.
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