Abstract
In order to explore the correlation between the compactness of sand core samples and its surface image features and to provide the basis for rapid identification and recognition of core samples in engineering investigation, a typical image data set of sand core samples disturbed by drilling construction in practical engineering has been established, using Python language to compile algorithm to calculate one-dimensional entropy and two-dimensional entropy of 60 groups of sand core samples with different densities. The influence of different sand core compactness on surface entropy characteristics was discussed, and the following conclusions were obtained: (1) Affected by drilling construction and disturbance, the looser the sand core surface particles are, the worse the sorting is and the more irregular the shape characteristics are. There is a close relationship between grain texture and compactness. (2) The calculation results of sand image entropy of one-dimensional entropy and two-dimensional entropy showed that the entropy value of loose, slightly dense, and medium dense sand images is positively correlated with the compactness of sand. (3) The maximum variance of two-dimensional entropy of loose, slightly dense, and medium dense sand image in the same borehole is less than 0.09, and the data variance amplification effect of two-dimensional entropy of image is mainly between different boreholes. (4) The dense feature of core sample structure forms an ordered structure with a gray change boundary, which increases the roughness of the image and leads to the increase of entropy. The two-dimensional entropy reveals the internal correlation mechanism of the influence of the engineering state on the surface structure of sand more clearly than the one-dimensional entropy and more effectively characterizes the dense degree of sand particles. (5) Using two-dimensional entropy to judge the compactness of sand image in the same borehole, the data fluctuation is small, and the algorithm is stable and reliable. The research results have reference values for the detection and analysis of sand sample density in geotechnical engineering investigation.
Highlights
Core samples of geotechnical engineering investigation are obtained by drilling
Visual recognition technology is developed with the development of image recognition technology in recent years
With the improvement of computer computing power and the rapid development of color recognition technology combined with artificial neural network, many computing models have been generated; for example, Wang and Wang [12] studied neural network algorithm and TensorFlow and the convolution neural network model of rock slope image set analysis is established
Summary
Core samples of geotechnical engineering investigation are obtained by drilling. Core identification is the core step of engineering investigation. e accuracy and efficiency of identification directly affect the progress of the project. The standard rock and soil sample slice database is generally used, and the database is established by using the disturbed rock and soil core image of engineering investigation site for texture feature analysis, which is rarely reported. Rough the collection of sand core photos, classification, calibration, and segmentation are carried out according to the general image recognition standard, the core image database is established, the calculation model based on texture entropy is established, and the influence law of entropy and compactness is obtained through statistical analysis. Erefore, the author established a database based on the classification and calibration of sand with different compactness collected from the engineering site, analyzed the influence of compactness on it by using image entropy as the main feature, and quantitatively characterized the visual features of the core sample surface with the help of entropy. It is necessary to explore the visual characteristics of sand core samples with different compactness
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