基于G-S-G的混凝土结构健康监测研究
Jiaqi Wang1, Hongbi Kang1, Kexin Li2
1Department of Landscape Architecture, Beihua University, Jilin, China.
Scientific reports
|January 9, 2025
概括
这项研究介绍了一种先进的方法,用于使用优化的灰级共发生矩阵 (GLCM) 和自我组织地图 (SOM) 神经网络进行混凝土结构健康监测. G-S-G方法准确地检测裂,增强结构完整性的评估.
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
- 人工智能的人工智能
背景情况:
- 混凝土结构是必不可少的基础设施,需要强有力的健康监测.
- 现有的方法可能难以准确检测裂并与其他损伤类型区分.
- 实时,高精度的监控对于防止结构性故障至关重要.
研究的目的:
- 为准确和实时的混凝土结构健康监测提出一个改进的GSG方法.
- 通过减轻其他缺陷的干扰来增强裂纹损伤的识别.
- 开发可靠的指标来评估混凝土结构的健康状况.
主要方法:
- 图像采集和优化,以清晰可视化裂.
- 集成优化的灰级共发生矩阵 (GLCM) 与自组织地图 (SOM) 神经网络.
- 提取概率范围指数 (PRI) 和 (ENT) 作为敏感损害指标.
主要成果:
- 在裂识别过程中有效消除蜂和坑洞损伤的干扰.
- 成功提取PRI和作为结构健康监测的可靠指标.
- 展示G-S-G方法对混凝土裂损坏的高精度智能监控的能力.
结论:
- 拟议的G-S-G方法结合了GLCM和SOM,在混凝土结构健康监测方面取得了重大进展.
- (ENT) 和PRI被确定为损害特征的敏感和可靠指标.
- 这项研究促进了对现代混凝土结构中裂的智能和高精度监测.
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