Sandy red clay, abundant in clay minerals, exhibits a marked sensitivity to variations in water content. Several of its properties are highly prone to deterioration due to wet-dry cycling, potentially leading to slope instability. To investigate the multi-scale deterioration patterns and the underlying chain mechanism of sandy red clay subjected to wet-dry cycles, this study conducted systematic tests on remolded sandy red clay specimens through 0 to 5 wet-dry cycles, with the number of cycles (N) as the variable. The study's results indicated the following, under wet-dry cycling: (1) Regarding the expansion and shrinking properties, the absolute expansion rate (delta a) progressively increased, whereas the absolute shrinkage rate (eta a) gradually decreased. Concurrently, the relative expansion rate (delta r) and relative shrinkage rate (eta r) gradually declined. (2) At the microscale, wet-dry cycles induced significant changes in the microstructure, characterized by increased particle rounding, disrupted stacked aggregates, altered inter-particle contacts, enlarged and interconnected pores, increased number of pores, and a reduction in clay mineral content. (3) At the mesoscale, cracks initiated and propagated. The evolution of cracks undergoes stages of initiation stage, propagation stage, and stable stage, and with the crack rate increasing to 2.0% after five cycles. (4) At the macroscale, the shear strength exhibited a continuous decline. After five cycles, cohesion decreased by as much as 49.6%, whereas the internal friction angle only decreased by 4.3%. This indicates that the loss of cohesion was the primary factor contributing to the strength deterioration. (5) A 19.4% decrease in the slope factor of safety (Fv) occurred after five cycles. This reduction was primarily attributed to the decrease in material cohesion and the upward shift in the potential sliding surface. Under the influence of wet-dry cycles, slope failures typically transitioned from overall or deep sliding to localized or shallow sliding.

Computed tomography (CT) is an effective technique for characterizing the internal structure of soil. However, the voxels in CT images obtained by majority of medical scanners exhibit anisotropy, i.e., the resolution in the vertical direction is lower compared to the horizontal direction, which can have adverse effects on the characterization of soil morphological parameters and the quality of three-dimensional reconstructed images. Currently, existing interpolation methods for achieving voxel isotropy in soil CT images are unable to generate high -quality interpolation images at arbitrary positions between two slices, which leads to errors in the analysis of soil structure. Therefore, this study proposed an inter -layer interpolation method (APFlowNet) based on convolutional neural network (CNN) and bidirectional optical flow to generate high -quality images with isotropic voxels and assist in digital soil descriptions. The proposed method utilized an estimated image synthesis module to extract bidirectional optical flow between two input images and estimate optical flows from the input image to arbitrary interpolation positions, enabling the acquisition of overall continuous change. Subsequently, the intermediate image synthesis module was employed to extract the residual stream and its corresponding weights, facilitating the capture of detailed changes. Finally, the interpolation image synthesis module integrated the global and detail information to produce a high -precision interpolation image with isotropic voxels. Compared to the best -performing Linear method in traditional approaches, the APFlowNet method demonstrates superior performance with a peak signal-to-noise ratio (PSNR) of 32.637 dB and a structural similarity index (SSIM) of 0.928, representing improvements of 1.97% and 0.43%, respectively. This study showcased that the APFlowNet method not only increases the number of soil CT images but also achieves voxel isotropy, providing an intelligent technique for comprehending the internal structure of soil and multi -scale modeling.

The distribution of the permafrost in the Tibetan Plateau has dramatically changed due to climate change, expressed as increasing permafrost degradation, thawing depth deepening and disappearance of island permafrost. These changes have serious impacts on the local ecological environment and the stability of engineering infrastructures. Ground penetrating radar (GPR) is used to detect permafrost active layer depth, the upper limit of permafrost and the thawing of permafrost with the season's changes. Due to the influence of complex structure in the permafrost layer, it is difficult to effectively characterize the accurate structure within the permafrost on the radar profile. In order to get the high resolution GPR profile in the Tibetan Plateau, the reverse time migration (RTM) imaging method was applied to GPR real data. In this paper, RTM algorithm is proven to be correct through the groove's model of forward modeling data. In the Beiluhe region, the imaging result of GPR RTM profiles show that the RTM of GPR makes use of diffracted energy to properly position the reflections caused by the gravels, pebbles, cobbles and small discontinuities. It can accurately determine the depth of the active layer bottom interface in the migration section. In order to prove the accuracy of interpretation results of real data RTM section, we set up the three dielectric constant models based on the real data RTM profiles and geological information, and obtained the model data RTM profiles, which can prove the accuracy of interpretation results of three-line RTM profiles. The results of three-line RTM bears great significance for the study of complex structure and freezing and thawing process of permafrost at the Beiluhe region on the Tibetan Plateau.