用于开发深度学习模型的数据集,以评估裂宽度和在混凝土中自我修复的进展.
Jacek Jakubowski1, Kamil Tomczak2
1Department of Civil & Geotechnical Engineering and Geomechanics, AGH University of Krakow, al.Mickiewicza 30, 30-059, Krakow, Poland. jakubjac@agh.edu.pl.
Scientific data
|January 28, 2025
概括
本研究介绍了使用深度学习进行具体裂评估的数据集. 它有助于开发先进的方法来评估来自图像数据的混凝土自我修复和裂纹宽度.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 自主自我愈合对于混凝土的耐用性至关重要.
- 准确的裂宽度评估对于结构健康监测至关重要.
- 开发用于裂分析的自动化方法是一个持续的挑战.
研究的目的:
- 提供一个全面的数据集,用于开发裂宽度评估的深度学习模型.
- 具体来说,促进对自身自我愈合的评估.
- 支持创建用于混凝土裂分析的先进分析算法.
主要方法:
- 混凝土标本的实验准备,成熟,破裂和自我愈合.
- 裂表面的高分辨率扫描和规模不变的图像处理.
- 从19098个记录中提取亮度配置文件和手动参考测量.
主要成果:
- 一个包含亮度配置文件,手动测量和深度学习和分析模型的基准结果的数据集.
- 包含有标记网格线的源图像用于进一步分析.
- 该数据集适合训练基于图像的深度卷积神经网络 (CNN) 模型.
结论:
- 开发的数据集是推动混凝土自动化裂宽度评估的宝贵资源.
- 这些发现支持深度学习元传感器的开发,用于自我愈合评估.
- 进一步的研究可以利用这一数据集来改进具体的结构性健康监测技术.
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