Junsen Zeng

The WMI (weak muddy intercalation) is a typical weak structural surface in the red-bedded rock mass; ensuring slope stability by increasing the strength of the WMIs helps reduce project costs and carbon emissions. With the advantages of energy saving, high efficiency, and green, microwave technology has attracted scholars’ attention to geotechnical material property improvement. However, the mineral composition of the WMIs is complex and variable, and the applicability of microwave technology needs further evaluation. In this paper, the effects of microwave temperature and clay mineral types and content on the physical and mechanical properties of the WMIs were evaluated. The results show that microwave heating can substantially improve the uniaxial compressive strength of the WMIs, regardless of the types and content of clay minerals. Dehydration, dehydroxylation of clay minerals, and local melting of albite occurring in the specimens under microwave heating enhanced the strength of the soil particles and the interparticle joints. The strength increase ratios of the WMI specimens increased with the temperature increase. With the increase in clay mineral content, the strength increase ratio of kaolinite WMIs and illite WMIs decreased, while the strength increase ratios of montmorillonite WMIs increased. The present multiple regression analysis methods are used to establish the strength prediction models of the WMI microwave-reinforced specimens, which can guide the engineering application.

The mechanical behavior of mudstones is significantly affected by their mesostructure. This study investigates the damage evolution of red mudstone mesostructures under uniaxial compression through U-Net image segmentation, mesoscopic representative elementary area (mREA), meso-element equivalent method, and linearly superimposed model. Results show that the mREAs can be simplified as a binary structure comprising nonclayey minerals and a porous matrix. The inclusion-matrix interfaces constituted by the dominant inclusions, such as large inclusions and a series of inclusions with specific arrangements, are the weakest regions at the postpeak stage. The shear failure of these interfaces leads to general shear failure with a penetrating principal fracture or local shear with several nonpenetrating local fractures in the mREAs. The damage evolution and failure mode are sensitive to the inclusion geometric features, e.g., inclination, grain size, and spacing. However, the uniaxial compressive strength (UCS) is mainly determined by the included angle between the dominant inclusion inclination and the loading direction. With consistent composition, an increase in the included angle from 0 to 90° results in an increase in the UCS of the muddy facies and a decrease in the sandy facies, with the UCS of the sandy facies being more sensitive to changes in the included angle. Our findings indicate that the structural failure of red mudstones from mesoscopic to macroscopic is driven by the accumulation and expansion of shear failure at multiple scales due to the presence of hard–soft interfaces. This study enhances our understanding of the structural failure mechanism of mudstones and serves as a practical reference for the mesomechanical testing of clay rocks.

Characterizing the surface deformation during the inter-survey period could assist in understanding rock mass progressive failure processes. Moreover, 3D reconstruction of rock mass surface is a crucial step in surface deformation detection. This study presents a method to reconstruct the rock mass surface at close range in a fast way using the improved structure from motion—multi view stereo (SfM) algorithm for surface deformation detection. To adapt the unique feature of rock mass surface, the AKAZE algorithm with the best performance in rock mass feature detection is introduced to improve SfM. The surface reconstructing procedure mainly consists of image acquisition, feature point detection, sparse reconstruction, and dense reconstruction. Hereafter, the proposed method was verified by three experiments. Experiment 1 showed that this method effectively reconstructed the rock mass model. Experiment 2 proved the advanced accuracy of the improved SfM compared with the traditional one in reconstructing the rock mass surface. Eventually, in Experiment 3, the surface deformation of rock mass was quantified through reconstructing images before and after the disturbance. All results have shown that the proposed method could provide reliable information in rock mass surface reconstruction and deformation detection.

This paper studies the limitations of binocular vision technology in monitoring accuracy. The factors affecting the surface displacement monitoring of the slope are analyzed mainly from system structure parameters and environment parameters. Based on the error analysis theory, the functional relationship between the structure parameters and the monitoring error is studied. The error distribution curve is obtained through laboratory testing and sensitivity analysis, and parameter selection criteria are proposed. Corresponding image optimization methods are designed according to the error distribution curve of the environment parameters, and a large number of tests proved that the methods effectively improved the measurement accuracy of slope deformation monitoring. Finally, the reliability and accuracy of the proposed system and method are verified by displacement measurement of a slope on site.