基于G-S-G的混凝土结构裂缝识别及监测方法

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摘要为解决复杂约束条件下现代混凝土结构裂缝的精准监测问题，提出基于G-S-G混凝土结构裂缝智能识别及监测方法。将灰度共生矩阵理论（GLCM）和自组织特征映射神经网络(SOM)模型结合，并通过数字图像处理技术（DIP）及数字特征筛选法(DFS)辅助分析，研究提高混凝土结构裂缝识别及监测精度的智能方法；并基于工程实例（柱的偏心受压试验），验证方法的可行性及准确性。结果表明，在有限的样本空间下，基于GLCM-SOM的裂缝识别模型，通过构建的标准特征样本集（角二阶矩（ASM）、熵（ENT）等）排除环境因素及孔洞、凹陷等缺陷的干扰，获得较高的识别精度；基于SOM-GLCM的裂缝监测数据显示，筛选出的相关（COR）和聚类阴影（CLS）损伤特性指标与裂缝的发展情况具有良好的线性关系，可作为裂缝延展趋势的敏感特性指标。提出的G-S-G裂缝检测方法，充分结合GLCM与SOM各自的独特优势，建立起精准识别裂缝损伤的网络模型，并对裂缝的发展趋势进行有效监测。研究有助于实现现代混凝土结构裂缝损伤的高精度智能化健康监测。

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	李可心1
	王钧1
	戚大伟2

关键词 ：结构损伤检测, 数字图像处理, 自组织特征映射神经网络, 灰度共生矩阵, 损伤指标, 裂缝监测

Abstract：In order to solve the problem of accurate monitoring of cracks in modern concrete structures under complex constraints, an intelligent monitoring method for cracks in concrete structures based on G-S-G is proposed. Combining the gray level co-occurrence matrix theory (GLCM) and the self-organizing feature mapping neural network (SOM) model, and through the digital image processing technology (DIP) and digital feature screening (DFS) analysis to study the intelligent methods that can improve the crack identification and monitoring accuracy of concrete structures. The feasibility and accuracy of the method are verified by the eccentric compression test of the column. The results show that under the limited sample space, the crack identification model based on GLCM-SOM can eliminate the interference of environmental factors and defects such as holes and depressions through the construction of six standard feature samples (P1-P6 in Tab.1).Therefore, obtaining higher recognition accuracy. The crack monitoring data based on SOM-GLCM shows that the selected damage characteristics index has a good linear relationship with the development of cracks (as shown in Fig.12). Therefore, COR and CLS can be used as sensitive indicators of crack propagation trends. The proposed G-S-G method fully combines the unique advantages of GLCM and SOM to establish a network model for accurately identifying crack damage and effectively monitor the development trend of cracks. This research process can help to achieve high-precision and intelligent health monitoring of crack damage in modern concrete structures.

Key words： structural damage detection digital image processing self-organization map gray level co-occurrence matrix damage index crack monitoring

收稿日期: 2019-07-29 出版日期: 2020-05-28

引用本文:

李可心1,王钧1,戚大伟2. 基于G-S-G的混凝土结构裂缝识别及监测方法[J]. 振动与冲击, 2020, 39(11): 101-108.
LI Kexin1, WANG Jun1, QI Dawei2. Research on crack identification and monitoring method of concrete structure based on G-S-G. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(11): 101-108.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I11/101

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