Microscopic Parameter Extraction and Corresponding Strength Prediction of Cemented Paste Backfill at Different Curing Times

• Published in: Advances in civil engineering 2018-01, Vol.2018 (2018), p.1-9
• Main Authors: Wang, Mei, Wang, Pai, Liu, Lang, Qin, Xuebin, Xin, Jie
• Format: Article
• Language: English
• Subjects: Backfill; Cement; Cementing; Civil engineering; Cluster analysis; Clustering; Concrete; Consolidation; Crack propagation; Curing; Digital imaging; Drinking water; Engineering; Failure; Fractals; Grain size; Image processing; Machine learning; Mechanical analysis; Methods; Microstructure; Mining; Neural networks; Noise reduction; Parameters; Physical properties; Porosity; Predictions; Scanning electron microscopy
• ISSN: 1687-8086; 1687-8094
• DOI: 10.1155/2018/2837571

To accurately and intuitively study the influence of microscopic parameters and mechanical responses of the consolidation process of cemented paste backfill (CPB), a method is proposed for characterizing its geometric and morphological characteristics and its mechanical response. A set of microstructure parameter software is developed for analyzing the CPB consolidation process, which quantitatively analyzes the mechanical response of CPBs at a microscopic scale. Based on the fuzzy clustering method, CPB microscopic pore images are extracted via digital image processing technology. Microscopic CPB pores are extracted from images via cluster analysis, binarization, and denoising techniques. Then, images are evaluated for porosity, number of pores, average pore width, fractal dimension, weighted probability entropy, and 11 more indicators to quantitatively analyze pores. Thus, the proposed method forms nonlinear relationships between microstructure parameters and mechanical responses based on a deep learning TensorFlow framework under different curing times. Results show that the multiparameter predictive mechanical response at the microscopic scale has a good effect, and the predicted average error is 9.51%. The accuracy of the proposed method is higher than that of the traditional method. Therefore, the proposed method provides a new method to quantitatively analyze the mechanical response strength prediction at a microscale.