With the increasing utilization of underground space, engineering muck has become a potential urban risk. This study employed a waste-to-waste strategy to promote its low-carbon recycling by using rice husk ash (RHA) as a stabilizer, with a focus on elucidating the stabilization mechanisms through multi-scale analysis. The results showed that RHA synergized with cement, enhancing unconfined compressive strength and water stability, while reducing the specific surface area and swelling potential of the engineering muck. The optimal RHA dosage was found to be between 4 % and 6 %, with cement content ranging from 3 % to 9 %. The multi-scale analysis demonstrated that the stabilization mechanisms of RHA-cement stabilized soil were governed by two main factors: structural enhancement and surface modification, both of which were driven by the promotion of novel hydration products through the incorporation of RHA. Specifically, the needle-like and columnar minerals effectively filled soil pores, forming a dense, robust skeletal structure that enhanced the mechanical properties of the stabilized soil. Meanwhile, the honeycomb-like C-S-H gel adhered to soil particle surfaces, repairing cracks and reinforcing interparticle bonding, thus improving the overall structural integrity. AFM analysis further revealed that the honeycomb-like C-S-H gel consisted of rod-like nanoparticles that were regularly arranged on the soil surface. This feature increased surface roughness, reduced fractal dimensions, and created a multi-scale structure of micro-papillae and nano-hairs with a lotus leaf effect, significantly enhancing the hydrophobic properties of the soil.

The variability in particle morphology significantly impacts the mechanical properties of rockfill materials. To enhance the understanding of this influence, this study collected basalt rockfill particles from 6 different site sources, with their morphology captured by 3D scanning technology, and then the morphological characteristics categorized through cluster analysis. True triaxial tests for these 6 particle groups were simulated using discrete element method (DEM), and the effects of elongation, flatness, convexity, and intermediate principal stress coefficient on the stress-strain relationship and peak strength were qualitatively assessed through principal component analysis (PCA). Further, by controlling the elongation, flatness, and convexity, 3D reconstructed particle models were created by spherical harmonics (SH) analysis, and the true triaxial tests on these models were simulated to quantitatively clarify the influence of morphological parameters on the macroscopic stress- strain relationship, peak strength, microscopic contact, anisotropic evolution, and other characteristics. Considering the size effect in rockfill materials, multi-scale models incorporating particle morphology were further evaluated across four sample scales. The results indicate that, on the macro scale, the three morphological parameters and the middle principal stress coefficient each have substantial effects on peak strength independently, while the interaction among these parameters does not have a notable influence on the strength. With increasing convexity, the peak strength of samples gradually decreases, while an increase in elongation and flatness leads to a trend of initially increasing and then decreasing strength. On the micro scale, the increase in both elongation and flatness results in a more uniform fabric in the main and lateral directions, while the coordination number shows a trend of initially increasing and then decreasing before stabilizing gradually. The influence of elongation on the main direction fabric is slightly smaller than that of flatness, while convexity has minimal effect on these microscopic features. Additionally, the morphological parameters not only impact the deformation capacity of samples but also demonstrate heightened sensitivity to the strength-size relationship of the sample due to interlocking and boundary constraints between particles. This underscores the pivotal role of morphological parameters in governing the mechanical motion of particles during the sample size scaling process, consequently influencing the strength of the material.

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.

Soil-rock mixture is a typical two-phase composite material. The physicomechanical properties and relative quantities of each component greatly influence the macroscopic mechanical properties of the soil-rock mixture. This study takes the macroscopic and mesoscopic coupling perspective and uses the discrete element direct shear test as the main method to comprehensively investigate the effects of rock content and rock particle size on the mechanical properties of the soil-rock mixture. The results show that the macroscopic shear strength of soil-rock mixture specimens increases with the increase of rock content. The initial packing state and compactness of specimens affect their shear dilation performance to some extent. The contact force chain distributions within specimens under different soil-rock combinations differ significantly. However, a very obvious force chain band is observed in the specimens at the end of the shearing (ranging from the upper left of the shearing box to its lower right). The contact force branch vectors within the force chain band are thick and dense, with obvious directionality (all from top left to bottom right). Under the same vertical load, the average coordination number of particles on the shear surface decreases with the increase of rock content. Within the same specimen, the average coordination number of particles on the shear surface increases with the increase of vertical load.

Agriculture is one of the prime economical sources of India and most of the people directly or indirectly depend on farming. The researchers are focusing on plant ailment detection and managing the imbalanced nutrition in plants. Automation is introduced in agricultural fields and most of these automation strategies use the Internet of Things (IoT) for enhance productivity and automate processes. With the help of several deep and machine learning approaches the endless decision-making performance is performed. Here, the endless decision performance shows appropriate outcomes which helps to solve the unstructured problems in smart farming. It is monitored that the traditional analysis doesn't have enough decision-making ability in the selection of fertilizer quantity that is to be used in farming. This inability leads to crop ailments and that affects the lifestyle of humans too. So, the prior detection of ailments in crops is essential. Enforcing Smart Agriculture is a hot topic in research nowadays to overcome crop damage in the future. Therefore, a new IoT-based smart farming model using deep learning is proposed to increase crop yield. By detecting disease, pests, smart irrigation, and yield, the smart farming model can reduce the amount of water and chemicals used in agriculture. This smart farming model consists of four phases a) disease prediction, b) pest detection c) smart irrigation, and d) yield prediction. In the first phase, the crop images are gathered from online datasets. The diseases in crops are predicted using Multiscale Adaptive CNN with LSTM layer (MA-CNN-LSTM), where the parameters in MA-CNN-LSTM are optimized using Advanced Mountaineering Team-Based Optimization Algorithm (AMTBO). In the second phase, the input images are given to MA-CNN-LSTM to detect crop pests. Here, the AMTBO is utilized for tuning parameters. In the third phase, the soil quality and environment data are fed into the Multi-scale Adaptive 1DCNN with LSTM layer (MA-1D CNN-LSTM) to predict the smart irrigation, where the parameter optimization is done using the AMTBO. Smart irrigation enhances the growth of crops and minimizes water usage. In the final phase, the input data such as crop quality, soil quality, and environment data are given to the MA-1D CNN-LSTM to check the overall yield prediction in an agricultural region. Here, the parameters in MA-1D CNN-LSTM are optimized via the AMTBO. The simulation results are compared with other algorithms and classification techniques to check the performance of the developed model.

In the high-level radioactive waste (HLW) deep geological repository, bentonite is compacted uniaxially, and then arranged vertically in engineered barriers. The assembly scheme induces the initial anisotropy, and with hydration, it develops more evidently under chemical conditions. To investigate the anisotropic swelling of compacted Gaomiaozi (GMZ) bentonite and the further response to saline effects, a series of constant-volume swelling pressure tests were performed. Results showed that dry density enhanced the bentonite swelling and raised the final anisotropy, whereas saline inhibited the bentonite swelling but still promoted the final anisotropy. The final anisotropy coefficient (ratio of radial to axial pressure) obeyed the Boltzmann sigmoid attenuation function, decreasing with concentration and dry density, converging to a minimum value of 0.76. The staged evolution of anisotropy coefficient was discovered, that saline inhibited the rise of the anisotropy coefficient (Delta delta) in the isotropic process greater than the valley (delta(1)) in the anisotropic process, leading to the final anisotropy increasing. The isotropic stage amplified the impact of soil structure rearrangement on the macro-swelling pressure values. Thus, a new method for predicting swelling pressures of compacted bentonite was proposed, by expanding the equations of Gouy-Chapman theory with a dissipative wedge term. An evolutionary function was constructed, revealing the correlation between the occurrence time and the pressure value due to the structure rearrangement and the former crystalline swelling. Accordingly, a design reference for dry density was given, based on the chemical conditions around the pre-site in Beishan, China. The anisotropy promoted by saline would cause a greater drop of radial pressure, making the previous threshold on axial swelling fail. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Deep geological disposal is the preferred solution for radioactive waste management in many countries, including Belgium, where the Boom Clay is one of the potential candidate host formations. Over the long term, corrosion mechanisms are expected to release large amounts of gas that will rise in pressure and activate different gas transport processes in the system and the surrounding geological formation. Assessing which transfer mode prevails under which range of pressure conditions in the sound rock layers remains a major issue. This paper presents a multi-scale Hydro-Mechanical (HM) model capturing the influence of the microstructure features on the macroscopic gas flow, and especially the emergence of preferential gas-filled pathways. A detailed constitutive model for partially saturated clay materials is developed from experimental data to perform the modelling of a Representative Element Volume (REV), and integrated into a multi-scale scheme using homogenisation and localisation techniques for the transitions to the macroscopic scale. Using this tool, numerical modelling of a gas injection test in the Boom Clay is performed with the aim of improving the mechanistic understanding of gas transport processes in natural clay barriers.

A three-scale constitutive model for unsaturated granular materials based on thermodynamic theory is presented. The three-scale yield locus, derived from the explicit yield criterion for solid matrix, is developed from a series of discrete interparticle contact planes. The three-scale yield locus is sensitive to porosity changes; therefore, it is reinterpreted as a corresponding constitutive model without phenomenological parameters. Furthermore, a water retention curve is proposed based on special pore morphology and experimental observations. The features of the partially saturated granular materials are well captured by the model. Under wetting and isotropic compression, volumetric compaction occurs, and the degree of saturation increases. Moreover, the higher the matric suction, the greater the strength, and the smaller the volumetric compaction. Compared with the phenomenological Barcelona basic model, the proposed three-scale constitutive model has fewer parameters; virtually all parameters have clear physical meanings. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.

Underground excavation is usually accompanied by complex soil-structure interaction problems in practical engineering. This paper develops a novel multi-scale approach for investigating the soil arching effect through trapdoor tests. This approach adopts the finite element method (FEM) and smoothed particle hydrodynamics (SPH) method to handle the particle-rigid body interaction in the trapdoor tests, incorporating a micromechanical 3D-H model to derive the nonlinear material response required by the SPH method. The variation of the earth pressure on the trapdoor in simulations exhibits good agreement with those of the experiments. Extensive parametric analyzes are performed to assess the effects of soil height and inter-particle friction angle on the evolution of load transfer and soil deformation. Three deformation patterns are observed under different buried conditions, including the trapezoid, the triangle, and the equal settlement pattern. Results indicate that the planes of equal settlement develop progressively with the trapdoor movement and then enter the range of experimentally observed values. Additionally, three failure mechanisms are identified that correspond to the three deformation patterns. Due to the advantages of the micromechanical model, mesoscale behavior is captured. The anisotropy of stress distribution in the plastic region is found during the arching process.

Geological discontinuity (GD) plays a pivotal role in determining the catastrophic mechanical failure of jointed rock masses. Accurate and efficient acquisition of GD networks is essential for characterizing and understanding the progressive damage mechanisms of slopes based on monitoring image data. Inspired by recent advances in computer vision, deep learning (DL) models have been widely utilized for image-based fracture identification. The multi-scale characteristics, image resolution and annotation quality of images will cause a scale-space effect (SSE) that makes features indistinguishable from noise, directly affecting the accuracy. However, this effect has not received adequate attention. Herein, we try to address this gap by collecting slope images at various proportional scales and constructing multi-scale datasets using image processing techniques. Next, we quantify the intensity of feature signals using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Combining these metrics with the scale-space theory, we investigate the influence of the SSE on the differentiation of multi-scale features and the accuracy of recognition. It is found that augmenting the image 's detail capacity does not always yield benefits for vision-based recognition models. In light of these observations, we propose a scale hybridization approach based on the diffusion mechanism of scale-space representation. The results show that scale hybridization strengthens the tolerance of multi-scale feature recognition under complex environmental noise interference and significantly enhances the recognition accuracy of GD. It also facilitates the objective understanding, description and analysis of the rock behavior and stability of slopes from the perspective of image data. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).