基于CT图像和K-Means聚类算法研究混凝土细观损伤

混凝土CT图像细观损伤区没有明显的灰度特征，基于阈值及边缘检测的图像分割方法难以提取细观损伤信息。论文首次提出应用K-Means聚类算法深度挖掘混凝土CT图像内部蕴含的细观损伤信息。首先，对圆柱体混凝土试件进行了单轴静力压缩CT试验；然后，依据轮廓系数确定最优聚类簇数，利用K-Means聚类算法在非监督状态下寻找混凝土CT图像的最优划分，获得了包含细观损伤信息的分区图；最后，统计了细观损伤区域像素点总数，计算了混凝土损伤度。结果表明：从破坏区和细观损伤区图上能直观地观察到各应力阶段混凝土内部细观损伤的演化规律。细观损伤度随应力的变化具有规律性，峰值荷载前细观损伤发展经历了相对稳定期和稳定发展期，峰值荷载后细观损伤度减小，损伤聚集在裂缝周围，细观损伤经历不稳定发展期。K-Means聚类算法在分析混凝土损伤演化方面具有明显优势。

Analysis on meso-scale damage of concrete using CT images and K-Means clustering algorithm

No obvious grayscale feature can be detected in the mesoscopic damage area of concrete CT images, and it is difficult to extract mesoscopic damage information with an image segmentation method that is based on thresholding or edge detection. This paper describes a new K-Means clustering algorithm for deeply excavating such information from the CT images of concrete. First, we conduct uniaxial static compression tests on concrete cylinder specimens. Then, we determine the number of optimal clusters according to the outline coefficient, use this algorithm to search for the optimal partition of a CT image in non-supervised state, and obtain a partition map that carries mesoscopic damage information. Finally, the degree of damage is calculated by counting the total number of pixels over the damage area. The results show that at each compression stage, the evolution of mesoscopic damage in concrete can be observed intuitively on the map of failure zones and mesoscopic damage zones. It reveals the trend of mesoscopic damage degree varying with stress ? a relatively stable initial period and a stable growing period before the load peak, and after that an unstable decaying period. Thus, this study demonstrates the significant advantage of the K-Means clustering algorithm in analysis of the damage evolution of concrete.