研究目的
To analyze the texture features of the micro-structure of cement paste backfill (CPB) using image analysis technology and investigate the correlation between these texture features and the unconfined compressive strength (UCS) of CPB.
研究成果
The study demonstrates that texture features from SEM images, specifically Tamura texture (contrast, linearity, regularity) and GLCM features (angular second moment, correlation, entropy, variance), have significant correlations with UCS of CPB. High magnification images (10000×) provide better correlation than low magnification (2000×). This method is effective for quantitative analysis of micro-morphology and strength prediction in CPB.
研究不足
The study is limited to specific solid concentrations (72%, 76%, 78%) and may not generalize to other concentrations. The sensitivity of some texture parameters (e.g., coarseness and directionality) is low, and the method relies on SEM image quality and magnification. Further optimization is needed for broader applications.
1:Experimental Design and Method Selection:
The study used Tamura texture and gray level co-occurrence matrix (GLCM) features to characterize texture from SEM images. MATLAB software was employed for texture feature extraction.
2:Sample Selection and Data Sources:
CPB samples were prepared from tailings, binder (ordinary Portland cement), and water at solid concentrations of 72%, 76%, and 78%. SEM images were obtained from fractured samples after UCS tests.
3:8%. SEM images were obtained from fractured samples after UCS tests. List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Equipment included a planetary mortar mixer (Model No. JJ-5), standard mold (
4:7 mm×7 mm×7 mm), conservation box (Model No. JBY-60B), scanning electron microscope (SEM), and MATLAB software. Materials included tailings, cement, and water. Experimental Procedures and Operational Workflow:
CPB mixtures were prepared, cast into molds, cured, and tested for UCS. SEM images were captured, processed to 512×512 pixels with 256 gray scales, and texture features were calculated.
5:Data Analysis Methods:
Correlation analysis was performed between texture parameters and solid concentration/UCS using statistical methods in MATLAB.
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