The current investigation examines the fluctuating behaviour of stiff pavement built on a two-parameter base and is influenced by aircraft loading impacts. This investigation is driven by the necessity for an accurate evaluation of pavement behaviour under elevated stress scenarios caused by aircraft, which can guide pavement design and upkeep. A stochastic numerical model, the vehicle-pavement interaction model (VPI), was created using a comprehensive 3D dynamic model of an aircraft vehicle and stationary runway roughness profiles. The rigid pavement is simulated using a computationally efficient 1D finite element mathematical model incorporating six DOF. The Pasternak model represents the soil medium, incorporating shear interaction between the spring elements. The pavement's irregularities are considered and replicated using a power spectral density (PSD) function. This assembled model was used to investigate the dynamical reaction of concrete pavement vibrations caused by the passing of an aircraft vehicle using MATLAB code. The dynamic governing differential equations of the aircraft's motion are developed and coupled with the pavement system equations. The coupled system is then solved in the time domain using the direct computational integration approach with the Newmark-Beta integration scheme, explicitly utilizing the linear average acceleration method. This approach is employed to resolve the equations that govern and assess the performance of the connected system. The current findings are being compared to existing analytical outcomes to verify the precision of the current coding. The research examined the impact of various pavement and aircraft vehicle behaviors and factors on the dynamic response of pavement, including the speed, main and auxiliary suspension components, mass and the load position of the aircraft, also the damping, random roughness, thickness, span length and elastic constant of the pavement, even, the modulus of subgrade of the foundation, the rigidity modulus of the shear layer. The findings demonstrate notable influences of aircraft speed and pavement surface roughness on various response parameters. Specifically, the results reveal that a higher subgrade modulus leads to decreased deflection, rotation, and bending moments. Conversely, longer span lengths tend to elevate response parameters while simultaneously reducing shear force. In conclusion, the results highlight the significance of critical factors, including velocity and subgrade modulus, in forecasting the performance of pavement subjected to aircraft loads. The present research is confined to the investigation of the dynamic's performance of the VPI simulation of airfield rigid pavement. The findings from this study can be expanded on by paving engineers to improve the structural effectiveness and reliability of the pavement, serving as a basis for subsequent fatigue analysis in response to diverse dynamic loads such as earthquake, temperature and vehicle load.

An appropriate interface constitutive model is crucial to the simulation of soil-structure interface behavior. Currently, most models are only capable of describing the mechanical properties of rough interface. However, they are unable to simultaneously account for the effects of surface roughness and particle breakage. This study proposes an elastoplastic interface constitutive model considering the effects of normal stress, relative density, particle breakage, and surface roughness. It describes the variations of critical void ratio and critical stress ratio with normalized surface roughness by exponential functions. Change in critical void ratio caused by particle breakage is denoted by input work. An expression of the critical state line and a modified dilatancy function are derived based on the state-dependent dilatancy theory, uniformly describing the influences of relative density, particle breakage, normal stress, and surface roughness. The yield and hardening functions are introduced by including the plastic shear displacement as the hardening parameter based on the Mohr-Coulomb criterion. Finally, experimental data from the literature are utilized to validate the accuracy of the proposed model for various materials under different conditions.

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt's Central Eastern Desert (CED), where a severe flash flood occurred on 26-27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 x 106 m3, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements-Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 x 106 m3 and 1 x 106 m3) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga-Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management.

The roughness of the fracture surface directly affects the strength, deformation, and permeability of the surrounding rock in deep underground engineering. Understanding the effect of high temperature and thermal cycle on the fracture surface roughness plays an important role in estimating the damage degree and stability of deep rock mass. In this paper, the variations of fracture surface roughness of granite after different heating and thermal cycles were investigated using the joint roughness coefficient method (JRC), three-dimensional (3D) roughness parameters, and fractal dimension (D), and the mechanism of damage and deterioration of granite were revealed. The experimental results show an increase in the roughness of the granite fracture surface as temperature and cycle number were incremented. The variations of JRC, height parameter, inclination parameter and area parameter with the temperature conformed to the Boltzmann's functional distribution, while the D decreased linearly as the temperature increased. Besides, the anisotropy index (I-p) of the granite fracture surface increased as the temperature increased, and the larger parameter values of roughness characterization at different temperatures were attained mainly in directions of 20 degrees-40 degrees, 60 degrees-100 degrees and 140 degrees-160 degrees. The fracture aperture of granite after fracture followed the Gauss distribution and the average aperture increased with increasing temperature, which increased from 0.665 mm at 25 degrees C to 1.058 mm at 800 degrees C. High temperature caused an uneven thermal expansion, water evaporation, and oxidation of minerals within the granite, which promoted the growth and expansion of microfractures, and reduced interparticle bonding strength. In particular, the damage was exacerbated by the expansion and cracking of the quartz phase transition after T > 500 degrees C. Thermal cycles contributed to the accumulation of this damage and further weakened the interparticle bonding forces, resulting in a significant increase in the roughness, anisotropy, and aperture of the fracture surface after five cycles. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published 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/).

Soil moisture detection research, which influences crop growth, land use, and soil erosion, is receiving significant attention. This study proposes a nondestructive, integrated ultrawideband (UWB)-based framework for soil moisture measurement and prediction. The method utilizes a UWB-loaded unmanned aerial vehicle (UAV) to gather radar echo data, circumventing soil damage issues inherent in current research and equipment. We first employ time-frequency analysis methods to convert the echo signals into 2-D spectrograms, constructing datasets labeled with soil moisture. Then, a trained neural network is used to predict the soil moisture at single point. Additionally, a novel interpolation method is proposed to enhance prediction accuracy (ACC) for the ridge-furrow structure of farmland. The experimental results demonstrate that the proposed algorithm achieves a soil moisture measurement ACC of 98% in both vegetated and nonvegetated conditions, indicating strong robustness. In terms of moisture distribution prediction, the mean squared error (mse) of soil moisture spatial distribution prediction is reduced by 42% compared to traditional methods. Therefore, this system provides technical support for efficient, large-scale, and nondestructive soil information collection.

Ancient landslides tend to reactivate along pre-existing slip zones that have reached a residual state. On the eastern margin of the Tibetan Plateau, previous research has indicated that the slip zone of ancient landslides is primarily composed of clayey soil with gravel, known as gravelly slip zone soil. However, the relationship between the macromechanical behavior of gravelly slip zones and the mesostructure of the shear surfaces affected by gravel is still unclear. Herein, ring shear tests and reversal direct shear tests were performed on gravelly slip zone soil, and the 3D morphology and shear surface roughness were quantitatively characterized by using 3D laser scanning technology and the power spectral density method. The results showed a significant correlation between the friction coefficient of the shear surface and its roughness. Gravel played a crucial role in influencing the macromechanical behavior of slip zones by altering the mesomorphology of the shear surfaces. By analyzing the mechanical properties of the contact unit on the shear surface, the residual strength of the gravelly slip zone was found to be jointly controlled by the basic strength of the fine-grained soil and the undulations caused by the gravel. Finally, a residual strength model was developed for the gravelly slip zone considering both the strength of the fine-grained soil and the shear surface roughness caused by the gravel. The reactivation of ancient landslides has caused serious casualties and economic losses. Field investigations have revealed that the slip zones of ancient landslides commonly contain gravel. However, we still have limited knowledge regarding the effects of gravel on the behavior of slip zones. We carried out shear tests on gravelly slip zone soils and quantitatively characterized the shear surface morphology. Our results showed a strong correlation between the friction coefficient of the shear surface and its roughness. We found that the presence of gravel significantly influenced the macromechanical behavior of the slip zone by altering the mesostructure of the shear surface. Based on our findings, we developed a residual strength model for the gravelly slip zone that considers both the strength of the fine-grained soil and the roughness of the shear surface caused by the gravel. Our study provides valuable insights into the behavior of ancient landslides along pre-existing slip zones and improves our understanding of the role of gravel in influencing their macromechanical behavior. The friction coefficient of the slip zone is positively correlated with the shear surface roughness The gravel controls the macromechanical behavior of the slip zone by altering the morphology of the shear surface A residual strength model for the gravelly slip zone soil considering the shear surface roughness caused by gravel is proposed

Soil surface roughness (SSR) is an important indicator that characterizes the microtopography feature of farmland after tillage. It has a high practical value for sowing and seedling raising, farmland management, and drainage irrigation in agricultural production. The traditional method often is prone to damage the surface microstructure and results in low efficiency and accuracy. In this study, a new method was proposed to address the limitations of traditional measurement methods of SSR. The proposed measurement and evaluation method of farmland microtopography feature information based on 3D lidar and inertial measurement unit (IMU) could be used to quickly obtain the global point cloud map containing the height data of the test field. Taking three different tillage methods of farmland as the research object, the surface root mean square height (RMSH), correlation length (CL), and their ratio were selected as roughness parameters to explore the anisotropy of microtopography features in different directions. The measurement method was then used to study the effects of sampling processing methods (number, interval, and length) on the measurement accuracy in both OX and OY directions. The results indicate that under the same accuracy requirements, for the 2 x 2 m area, the farmland with different microtopography features needs to be processed with different sample numbers, sample intervals, and sample lengths. The optimal combination of sample parameters for Test field I is sample number of 50, sample interval of 120 mm, and sample interval of 1600 mm, and that in Test field II is sample number of 50, sample interval of 160 mm, and sample interval of 1800 mm. For Test field III, the optimal combination is sample number of 100, sample interval of 40 mm, and sample length of 1200 mm. The experimental results compared with the traditional method illustrate the high accuracy and good feasibility of the proposed method for measuring and evaluating the microtopography feature information of the farmland. The results of the study help to understand the microtopography features and its parameterization of the farmland after tillage, which could further reveal the role and significance of SSR parameters in objectively evaluating farmland tillage quality and optimizing farmland management.

Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m x 25 m) and analyzes the characteristics of SSR under different tillage practices (plowing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The plowed and harrowed plot corresponds to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times. 2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded. 3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the true value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m x 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.