Ensuring the accuracy of free-field inversion is crucial in determining seismic excitation for soil-structure interaction (SSI) systems. Due to the spherical and cylindrical diffusion properties of body waves and surface waves, the near-fault zone presents distinct free-field responses compared to the far-fault zone. Consequently, existing far-fault free-field inversion techniques are insufficient for providing accurate seismic excitation for SSI systems within the near-fault zone. To address this limitation, a tailored near-fault free-field inversion method based on a multi-objective optimization algorithm is proposed in this study. The proposed method establishes an inversion framework for both spherical body waves and cylindrical surface waves and then transforms the overdetermined problem in inversion process into an optimization problem. Within the multi-objective optimization model, objective functions are formulated by minimizing the three-component waveform differences between the observation point and the delayed reference point. Additionally, constraint conditions are determined based on the attenuation property of propagating seismic waves. The accuracy of the proposed method is then verified through near-fault wave motion characteristics and validated against real downhole recordings. Finally, the application of the proposed method is investigated, with emphasis on examining the impulsive property of underground motions and analyzing the seismic responses of SSI systems. The results show that the proposed method refines the theoretical framework of near-fault inversion and accurately restores the free-field characteristics, particularly the impulsive features of near-fault motions, thereby providing reliable excitation for seismic response assessments of SSI systems.

The effective dynamic viscosity of a soil-rock mixture (S-RM) serves as a essential parameter for simulating flowlike landslides in the context of fluid kinematics. Accurate measurement of this viscosity is significant for understanding the remote sustainability and rheological properties of landslide hazards. This study presents a method for determining dynamic viscosity, incorporating experimental measurements and numerical inversion. The experiment involves monitoring the movement of S-RMs with varying water content and rock block concentration, followed by the calculation of centroid displacements and velocities using digital image processing. The power-law model, combined with computational fluid dynamics, effectively captures the flow-like behavior of the S-RM. A grid search method is then employed to determine the optimal parameters by comparing the predicted centroid displacement with experimental results. A series of flume experiments were conducted, resulting in the observation of spatial mass distribution and centroid displacement variations over time during soil-rock movement. The dynamic viscosity model of the S-RM is derived from the experimental data. This dynamic viscosity model was then employed to simulate an additional flume experiment, with the results demonstrating excellent agreement between the simulated and experimental centroid displacements. Sensitivity analysis of the dynamic viscosity model indicates a dependence on shear rate and demonstrates a high sensitivity to water content and rock block concentration, following a parabolic trend within the measured range. This research contributes to the fields of geotechnical engineering and landslide risk assessment, offering a practical and effective method of measuring the dynamic viscosity of S-RM. Future research could explore additional factors influencing rheological behavior and extend the applicability of the proposed method to different geological environments.

Ongoing climate warming and increased human activities have led to significant permafrost degradation on the Qinghai-Tibet Plateau (QTP). Mapping the distribution of active layer thickness (ALT) can provide essential information for understanding this degradation. Over the past decade, InSAR (Interferometric synthetic aperture radar) technology has been utilized to estimate ALT based on remotely-sensed surface deformation information. However, these methods are generally limited by their ability to accurate extract seasonal deformation and model subsurface water content of active layer. In this paper, an ALT inversion method considering both seasonal deformation from InSAR and smoothly multilayer soil moisture from ERA5 is proposed. Firstly, we introduce a ground seasonal deformation extraction model combining RobustSTL and InSAR, and the deformation extraction accuracy by considering the deformation characteristics of permafrost are evaluated, proving the effectiveness of RobustSTL in extracting seasonal deformation of permafrost. Then, using ERA5 soil moisture products, a smoothed multilayer soil moisture model for ALT inversion is established. Finally, integrating the seasonal deformation and multilayer soil moisture, the ALT can be estimated. The proposed model is applied to the Yellow River source region (YRSR) with Sentinel-1A images acquired from 2017 to 2021, and the ALT retrieval accuracy is validated with measured data. Experimental results show that the vertical deformation rate of the study area generally ranges from -30 mm/year to 20 mm/year, with seasonal deformation amplitude ranging from 2 mm to 30 mm. The RobustSTL method has the highest accuracy in extracting seasonal deformation of permafrost, with an RMSE (root mean square error) of 0.69 mm, and is capable of capturing the freeze-thaw characteristics of the active layer. The estimated ALT of the YRSR ranges from 49 cm to 450 cm, with an average value of 145 cm. Compared to the measured data, the proposed method has an average error of 37.5 cm, which represents a 21 % improvement in accuracy over existing methods.

To ensure the sustainable development of the surrounding environment and the sustainable operation of landfills, detecting landfill leakage is of great significance. In landfills lacking a leakage monitoring system, the inability to detect and locate damaged High-Density Polyethylene (HDPE) membranes can lead to the contamination of soil and groundwater by landfill leachate. To address this issue, this study proposes a resistivity tomography inversion model based on the external-electrode power supply mode. Utilizing the resistivity difference between the leakage zone and the surrounding soil, electrodes are arranged symmetrically for both power supply and measurement. Upon applying direct current (DC) excitation, potential data are collected, with the finite volume method employed for inversion and the Gauss-Newton method integrated with an adaptive particle swarm optimization algorithm for parameter fitting. Experimental results show that the combined algorithm provides better clarity in edge recognition of low-resistance models compared with single algorithms. The maximum deviation between inferred leakage coordinates and the actual location is 10.1 cm, while the minimum deviation is 6.4 cm, satisfying engineering requirements. This method can effectively locate point sources and line sources, providing an accurate solution for subsequent leakage point filling and improving repair efficiency.

Verticillium wilt (VW) is one of the most common and devastating diseases in cotton production, and early diagnosis is very important to alleviate the damage of VW. Recent studies have shown that early diagnosis and prevention of soil-borne diseases can be achieved by detecting spectral changes related to chlorophyll fluorescence and transpiration. However, there are no systematic studies to report the heterogeneity of photosynthetic characteristics and their spectral responses of plant leaves at the early stage of VW. In this study, the spatial heterogeneity characteristics in chlorophyll fluorescence of cotton leaves during the incubation period of VW were discussed, and the pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information, aiming to realize the early diagnosis of VW of cotton. The results showed that the chlorophyll fluorescence parameters Y(NPQ) (quantum yield of regulated energy dissipation) and NPQ/4 (non-photochemical quenching/4) values of cotton increased and the Y(II) (effective quantum yield of photosystem II) decreased significantly during the asymptomatic period of VW, indicating heterogeneity in photosynthetic capacity of leaves in the early stage of VW, i.e., VW developed from leaf margins to leaf center, and leaf margin was the area where chlorophyll fluorescence changed firstly. Furthermore, the multi-task learning model constructed with vegetation index and wavelet features accurately inversed the pixellevel heterogeneous characteristics of leaf Y(NPQ) and Y(II). The spectral information had the best inversion performance for the local heterogeneous regions of Y(II), with a classification accuracy of 85.6 %, a Kappa coefficient of 0.71, an r2 (coefficient of determination) of 0.66, and a RMSE (root mean square error) of 0.06. According to the inversion results of the local heterogeneous region of Y(II), the accurate diagnosis of early-stage VW was realized, with an accuracy of 87.4 % and a Kappa coefficient of 0.75. This study will provide a new method for the early prevention and control of VW.

The hardening soil model with small-strain stiffness (HSS model) is widely applied in deep foundation pit engineering in coastal soft-soil areas, yet it is characterized by a multitude of parameters that are relatively cumbersome to acquire. In this study, we incorporate a genetic algorithm and a back-propagation neural network (BPNN) model into an inversion analysis for HSS model parameters, with the objective of facilitating a more streamlined and accurate determination of these parameters in practical engineering. Utilizing horizontal displacement monitoring data from retaining structures, combined with local engineering, both a BPNN model and a BPNN optimized by a genetic algorithm (GA-BPNN) model were established to invert the stiffness modulus parameters of the HSS model for typical strata. Subsequently, numerical simulations were conducted based on the inverted parameters to analyze the deformation characteristics of the retaining structures. The performances of the BPNN and GA-BPNN models were evaluated using statistical metrics, including R2, MAE, MSE, WI, VAF, RAE, RRSE, and MAPE. The results demonstrate that the GA-BPNN model achieves significantly lower prediction errors, higher fitting accuracy, and predictive performance compared to the BPNN model. Based on the parameters inverted by the GA-BPNN model, the average compression modulus Es1-2, the reference tangent stiffness modulus Eoedref, the reference secant stiffness modulus E50ref, and the reference unloading-reloading stiffness modulus Eurref for gravelly cohesive soil were determined as Eoedref=0.83Es1-2 and Eurref=8.14E50ref; for fully weathered granite, Eoedref=1.54Es1-2 and Eurref=5.51E50ref. Numerical simulations conducted with these stiffness modulus parameters show excellent agreement with monitoring data, effectively describing the deformation characteristics of the retaining structures. In situations where relevant mechanical tests are unavailable, the application of the GA-BPNN model for the inversion analysis of HSS model parameters is both rational and effective, offering a reference for similar engineering projects.

The accurate calibration of snow parameters is necessary to establish an accurate simulation model of snow, which is generally used to study tire-snow interaction. In this paper, an innovative parameter inversion method based on in situ test results is proposed to calibrate the snow parameters, which avoids the damage to the mechanical properties of snow when making test samples using traditional test methods. A coupled Eulerian-Lagrangian (CEL) model of plate loading in snow was established; the sensitivity of snow parameters to the macroscopic load-sinkage relationship was studied; a plate-loading experiment was carried out; and the parameters of snow at the experimental site were inverted. The parameter inversion results from the snow model were verified by the experimental test results of different snow depths and different plate sizes. The results show the following: (1) The material cohesive, angle of friction, and hardening law of snow have great influence on the load-sinkage relationship of snow, the elastic modulus has a great influence on the unloading/reloading stiffness of snow, and the influence of density and Poisson's ratio on the load-sinkage relationship can be ignored. (2) The correlation coefficient between the inversion result and the matching test data is 0.979, which is 0.304 higher than that of the initial inversion curve. (3) The load-sinkage relationship of snow with different snow depths and plate diameters was simulated by using the model parameter of inversion, and the results were compared with the experimental results. The minimum correlation coefficient was 0.87, indicating that the snow parameter inversion method in this paper can calibrate the snow parameters of the test site accurately.

In evaluating the safety of rock slopes engineering, it is imperative to account for rheological effects. These effects can lead to significant deformations that may adversely impact the overall structural integrity. Consequently, accurate determination of the rheological mechanical parameters of slope rocks is essential. However, the application of rheological parameters obtained from laboratory tests encounters limitations due to the rock's inherent heterogeneity, scale effects, and inevitable sample dispersion. By contrast, on-site monitoring data serve as critical assets for real-time calibration and risk assessment in the evaluation of rheological parameters and prediction of slope deformation. To integrate on-site monitoring data with rheological mechanical mechanisms, this study introduces a probabilistic inverse model for evaluating rock slope rheological parameters, grounded in Bayesian theory, and incorporating a No-U-Turn Sampler (NUTS) based on Markov Chain Monte Carlo (MCMC) sampling algorithm. In terms of methodological efficiency, we compared the NUTS method with the traditional Metropolis-Hastings (M-H) approach, demonstrating the superior efficiency of the former. Additionally, sensitivity analysis of rheological parameters was conducted using the Burgers constitutive model. By combining the NUTS-based MCMC method with this model, the uncertainty of creep parameters was successfully evaluated. Utilizing these updated posterior parameters, up to 3-year deformation forecast for the slope was executed, the findings demonstrate that the deformation on the left bank slope is slight, indicating a state of safety. This study integrates monitoring data with rheological mechanics to establish a physical-data-driven rheological safety assessment mechanism. It offers a scientifically robust and effective approach for the uncertainty evaluation of rheological parameters and deformation prediction, providing significant support for the safety assessment of the left bank slope of the Baihetan hydropower station, China.

Agricultural waste presents a significant environmental challenge due to improper disposal and management practices, contributing to soil degradation, biodiversity loss, and pollution of water and air resources. To address these issues, there is a growing emphasis on the valorization of agricultural waste. Cellulose, a major component of agricultural waste, offers promising opportunities for resource utilization due to its unique properties, including biodegradability, biocompatibility, and renewability. Thus, this review explored various types of agricultural waste, their chemical composition, and pretreatment methods for cellulose extraction. It also highlights the significance of rice straw, sugarcane bagasse, and other agricultural residues as cellulose-rich resources. Among the various membrane fabrication techniques, phase inversion is highly effective for creating porous membranes with controlled thickness and uniformity, while electrospinning produces nanofibrous membranes with high surface area and exceptional mechanical properties. The review further explores the separation of pollutants including using cellulose membranes, demonstrating their potential in environmental remediation. Hence, by valorizing agricultural residues into functional materials, this approach addresses the challenge of agricultural waste management and contributes to the development of innovative solutions for pollution control and water treatment.

The Indo-Gangetic Plain (IGP) is characterized by thick sediments, predominantly comprising alluvial deposits, which can amplify seismic waves generated by earthquakes in the Himalayan region located to the north of the plain. The presence of loose sediments can indeed pose significant seismic hazards, mainly due to phenomena like soil liquefaction. These sediments pose a threat to densely populated Delhi and NCR regions, which are 200 km away from the plate boundary of India and the Eurasian plate. Scientists are concerned about people's safety in mitigating damage caused by high-rise buildings and loose sediments in the IGP region. Reliable knowledge of the sedimentary layer's thickness and velocity structure is crucial for investigating buried active faults, understanding significant destruction, and risk assessment. Sedimentary basins are also crucial for geo-resources such as hydrocarbon and geothermal energy. This research estimated the structure of the sedimentary layer beneath four stations in the Chandigarh-Ambala region in IGP using the high-frequency receiver function (PRF) technique. The study found that the sedimentary layer thickness varies significantly, with values from 2.0 to 3.0 km beneath the IGP and increasing northward. Shallow shear velocity (S-v) in the column of sediments below the Siwalik Himalaya ranges from 2.8 to 2.9 km/s, which can be utilized for assessing earthquake ground-motion sites. The study provides new perceptions of the geodynamic processes and seismotectonic structure of the Himalayan region, allowing for better identification of the earthquake hypocenter and assessment of seismic hazards. The shear wave velocity models estimated from this research can also be beneficial for assessing seismic hazards and earthquake-resistant construction. Estimates of the crustal thickness values from waveform inversion of the PRF at individual stations reveal that the Moho depth varies between 44 and 50 km in the Indo-Gangetic Plain. From Siwalik Himalaya to the higher Himalaya, it ranges from 44 to 65 km. The depth of Moho increases from the Indo-Gangetic plain towards the lesser Himalaya.