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

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 microstruc...

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Bibliographic Details
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
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Description
Summary: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.
ISSN:1687-8086
1687-8094
DOI:10.1155/2018/2837571