The Research on Pore Fractal Identification and Evolution of Cement Mortar Based on Real-Time CT Scanning Under Uniaxial Loading

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Abstract
Investigating the pore structure and understanding the relationship between pore characteristics and mechanical properties are crucial to research in the study of cement mortar. At present, the segmentation of large-scale concrete pores is mainly conducted using traditional algorithms or software, which are time-consuming and operate in a semi-automated manner. However, the application of these methods faces challenges when analyzing large-scale rock pores due to factors such as a lack of data, artifacts, and inconsistent contrast. In this study, six series of cement mortars were subjected to real-time CT scanning under uniaxial loading (RT-CT) to collect real-time three-dimensional data on the evolution of pore structures during loading. To address issues such as artifacts and inconsistent contrast, a new augmentation method was proposed to overcome artifacts and enhance contrast consistency. Finally, the augmented dataset was utilized for training, and the Fast R-CNN algorithm served as the framework for developing the pore recognition model. The results indicate that the improved algorithm demonstrates enhanced convergence and greater accuracy in pore segmentation. A mathematical model is developed to relate uniaxial compressive strength (UCS) to pore fractal dimension and porosity, based on pore segmentation analysis. The fractal dimensions evolution of each specimen is consistent with the progressive failure indicated by the strain-stress curve. Under uniaxial loading, specimens with a 4:1 cement–sand ratio exhibited peak strength. The incorporation of fractals improved particle contact, thereby facilitating the formation of the skeletal structure. These efforts contribute to improving the identification of the deformation of cement mortars.
Funding Information
  • National Key Research and Development Program of China (2023YFE0205800)
  • Provincial Natural Science Foundation of Shanxi, China (202403021211025, 202304021301028)
  • National Natural Science Foundation of China (U23A20285, 42002279, 42207205)

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