基于神经网络和集群的裂检测和混凝土结构的分析
Young Choi1, Hee Won Park2, Yirong Mi2
1Earth Turbine, 36, Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Republic of Korea.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一个自主卷积神经网络系统用于混凝土裂检测. 先进的系统准确地识别裂并量化损坏,改善结构完整性的评估.
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
- 计算机视觉 计算机视觉
背景情况:
- 混凝土结构对于基础设施至关重要,但容易发生裂,危及安全,增加维护成本.
- 手动裂检查耗时,易出错,对于复杂的现代建筑而言是不够的.
- 早期发现裂对于成本有效的维修和防止灾难性的结构故障至关重要.
研究的目的:
- 设计和研究使用卷积神经网络 (CNN) 来检测混凝土裂的自主系统.
- 为了自动识别混凝土裂并计算它们在结构中的比例.
- 为了能够客观,高效地评估随着时间的推移,具体的损害水平.
主要方法:
- 开发一个卷积神经网络 (CNN) 模型,用于自动化混凝土裂识别.
- 图像聚类技术的应用,以从检测到的图像中量化裂区域.
- 对CNN模型在混凝土裂数据集上的性能进行实验验证.
主要成果:
- 经过训练的CNN模型在分类混凝土裂方面达到99.9%的高准确率.
- 图像集群有效地确定了检查的混凝土样本中裂纹存在的百分比.
- 该系统在识别和量化具体损害方面表现出可靠的性能.
结论:
- 基于CNN的自主系统为混凝土裂检测提供了高度准确和客观的解决方案.
- 量化裂纹比例可以准确评估损坏程度和积极的结构维护.
- 这项技术可以显著降低维修成本,提高混凝土基础设施的安全性和寿命.
相关概念视频
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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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01:28Types of Non-structural Cracks in Concrete
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Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
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01:22Creep in Concrete
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01:12Non-destructive Tests for Concrete Strength
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01:25Effects of Creep
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