为研究干湿循环条件下黄土的微观结构变化规律,以西安市黄土为例,先基于灰度共生矩阵提取黄土灰度图像纹理特征,计算土壤的微观裂纹和孔隙面积占比,然后通过深度学习时序回归预测模型建立起灰度纹理特征和微观裂纹与孔隙面积占比之间的联系,计算土壤干湿循环损伤因子判断土壤干湿循环损伤程度。研究表明:在2次干湿循环内,土壤团粒边缘结构破坏,微观裂纹和孔隙急剧增加;在5次干湿循环内,土壤结构纹理化走向逐渐明显,趋近于平行水分迁移方向;经历4次干湿循环后,土壤的干湿循环损伤破坏比例达到93.10%;经过6次干湿循环后,土壤的干湿循环损伤已不再增大,即土壤微观结构纹理化已经趋于稳定。
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.
关键词
黄土 /
干湿循环 /
微观结构 /
灰度共生矩阵 /
深度学习
Key words
loess /
dry-wet cycle /
microstructure /
gray-level co-occurrence matrix /
deep learning
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