Damage Analysis of Loess under Dry-Wet Cycles Based on Deep Learning

SONG Jia; BAI Yang; WANG Xiao-lin

doi:10.11988/ckyyb.20210908

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Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (2) : 87-94. DOI: 10.11988/ckyyb.20210908

ROCK SOIL ENGINEERING

Damage Analysis of Loess under Dry-Wet Cycles Based on Deep Learning

SONG Jia, BAI Yang, WANG Xiao-lin

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Abstract

The aim of this study is to investigate the changes in the microstructure of loess under dry-wet cycles.The gray-scale image texture features of Xi’an loess were extracted based on the gray-level co-occurrence matrix,and the area of cracks and pores in the loess was calculated based on the percentage of the area of cracks and pores.The relationship between the gray-scale texture characteristics and the proportion of cracks and pore areas was established through the deep learning time-series regression prediction model,and the damage degree of loess under dry-wet cycles was determined by calculating the damage factor.Our study revealed that within two dry-wet cycles,the edge structure of soil aggregates was destroyed,and cracks and pores increased sharply;within five dry-wet cycles,the texturing trend of soil structure became more obvious,approaching the direction of parallel water migration;after four times of dry-wet cycle,the damage ratio of loess under dry-wet cycle reached 93.10%;after six dry-wet cycles,the damage no longer increased,which means that the microstructure texturing of the loess stabilized.

Key words

loess / dry-wet cycle / microstructure / gray-level co-occurrence matrix / deep learning

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SONG Jia, BAI Yang, WANG Xiao-lin. Damage Analysis of Loess under Dry-Wet Cycles Based on Deep Learning[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(2): 87-94 https://doi.org/10.11988/ckyyb.20210908

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