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基于机器视觉和数字图像相关技术的混凝土损伤演化研究
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Study on Damage Evolution of Concrete Specimens Based on Machine Vision and Digital Image Correlation Technology
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    摘要:

    提出一种在试件损伤过程中,以数字图像方法所得应变场为基础,采用机器视觉识别快速获得对应损伤状态下试件表面裂缝分布的裂缝自动识别检测(Automated Crack Detection and Measurement,ACDM)方法. 采用三点弯曲试验,以夹式位移计测量裂缝口张开位移(Crack Mouth Opening Displacement,CMOD)作为参考用真实值,对ACDM的精度进行分析. 结果显示,CMOD小于0.05 mm时,ACDM识别的误差小于0.01 mm,ACDM方法可用于分析微裂缝尺度的混凝土损伤情况. 通过3组不同强度混凝土立方体试件的压缩试验,采用ACDM方法所得裂缝面积直接表征损伤指标来反映试件的损伤演化情况. 结果表明,在宏观裂缝出现之前,在不同损伤阶段下由ACDM方法所得损伤指标,对应数值变化均大于10-3,与目前表征损伤应变场的不均匀性变异系数(Cv)相比更为灵敏和准确.

    Abstract:

    This paper presents an Automated Crack Detection and Measurement (ACDM) method, which is based on the strain field obtained by the Digital Image Correlation (DIC) method and uses machine vision recognition to quickly obtain the distribution of cracks on the surface of the specimen in the corresponding damage state. Through three-point bending test, the Crack Mouth Opening Displacement (CMOD) value is obtained by the clip gauge and ACDM method, respectively. The CMOD was measured by the clip gauge as a reference. By analyzing the accuracy of this method, the results show that when CMOD was less than 0.05 mm, the identification error is less than 0.01 mm, and ACDM can be used to analyze the micro-crack concrete damage. Based on the compression tests of three groups of concrete cube specimens under different damage stages, the damage indexes obtained by ACDM method have corresponding numerical changes greater than 10-3, which is more sensitive and accurate than the current quantitative Cv-value of strain field.

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WANG Qingyuan, XU Ying?,QIAN Sheng.基于机器视觉和数字图像相关技术的混凝土损伤演化研究[J].湖南大学学报:自然科学版,2023,(11):169~180

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  • 在线发布日期: 2023-12-04
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