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Aim: In complex networks, the degree distribution varies and provides an insight into the general structure of the system. For example, it may show scale‐free characteristics of the network, indicating higher vulnerability against non‐random disturbances. However, investigating its spatio‐temporal variability, degree distribution in marine food webs remains an unresolved issue. In this paper, we focus on describing the global variability of degree distribution in marine food webs. Location: Global. Methods: We studied 105 marine food webs. By Kolmogorov–Smirnov test, and kernel density estimation, we determined the degree distribution of each food web, described its spatio‐temporal pattern and quantified the correlation between relevant parameters as a function of the scale‐free property of the degree distribution. Results: Marine food webs around the globe did not strictly exhibit scale‐free characteristics in most regions, and only below 5% of the food webs entered the "strongest fit" level of the scale‐free network. We also find food webs in the polar regions indicate relatively high goodness‐of‐fit to scale‐free networks. The upwelling ecosystem related to ocean currents is prone to form a scale‐free web, which exhibits periodic scale‐free characteristics. The ecosystem types with relatively 'low fit' levels were mainly concentrated in the ecosystems heavily influenced by human activities. Main Conclusions: This research will enhance the research in terms of (a) classifying degree distribution in marine food webs; (b) revealing the variability in the spatial pattern of particular distributions, for example, the scale‐free characteristics and (c) exploring the distribution of in‐degree in space, quantifying the proportion of generalist and specialist species, as a potential indicator of adaptive potential of ecosystems. This research contributes to our understanding of the scale‐free features of marine food webs globally. It also offers a real systems‐based conservation approach to assess the spatial heterogeneity of the structural vulnerability of marine ecosystems. [ABSTRACT FROM AUTHOR]

The inherent spatial variability of rock mass strength has been explicitly considered in reliability analyses of slopes governed by the Hoek–Brown(HB) criterion. However, previous studies have primarily focused on extensive slopes where plane strain analysis is applicable, neglecting rock slopes constrained by boundaries that exhibit significant 'end effects' and are unsuitable for two-dimensional(2D) analysis. To bridge this gap, this research presents a novel three-dimensional(3D) reliability framework designed specifically for width-limited slopes in spatially variable rock masses. Considering the efficiency limitation of previously widely adopted numerical simulations, which struggle to accommodate extensive computation task, this study constructs the deterministic model using upper bound limit analysis (UBLA). A discretized mechanism is developed to determine the safety factor of spatially variable HB slopes. The integration of accelerated search strategies enables the determination of a solution to safety factor within a mere 5 min, mitigating the computational burden associated with high-dimensional stochastic problems or scenarios with a low probability of failure. Probabilistic analysis is conducted using a metamodel Sparse Polynomial Chaos Expansion (SPCE) in conjunction with Monte Carlo Simulation (MCS). Parametric analysis is employed to investigate the influence of various factors on slope reliability, including the autocorrelation length, coefficient of variation of strength, and correlation coefficient. This research presents a novel, computationally efficient deterministic model for slopes characterized by spatial variability in HB strength parameters. Furthermore, the employed principles show promising applicability in adjacent fields, such as tunneling and foundation engineering. Highlights: Develops a novel 3D reliability framework for width-limited slopes in spatially variable rock masses governed by the Hoek-Brown criterion. Develops an efficient UBLA-based deterministic model with a 3D spatial discretized technique and accelerated search strategies, reducing Fs computing time to within 5 mins. Integrates SPCE metamodel and MCS for probabilistic analysis, enabling investigation of various factors on slope reliability. The employed principles demonstrate promising applicability in adjacent fields, such as tunneling and foundation engineering. [ABSTRACT FROM AUTHOR]

Plate anchors have been recognized as an attractive option for floating offshore structures due to their low cost and high effectiveness. The prediction of anchor pullout capacity often assumes a homogeneous sand with uniform properties over the entire soil mass. However, natural soils typically exhibit significant spatial variability due to their geological history of soil formation. To account for this inherent spatial variability, this paper has proposed an analytical approach to probabilistically estimate the pullout capacity of strip plate anchors in sand. The soil friction angle was represented by a random field, and the first two moments and the probability density function of the anchor breakout factor were determined analytically using a local average theory. The proposed analytical approach was validated by the random finite-element method (RFEM) over a wide range of soil and anchor parameters. The findings suggest that the anchor embedment depth ratio has a limited effect on the estimated anchor failure probability, whereas the coefficient of variation and correlation length of the soil friction angle have a significant influence. This confirms the importance of sufficient site investigation for cost-effective and reliable anchor design. Overall, the proposed analytical framework can be regarded as a reliable and practical alternative to the computationally intensive RFEM. [ABSTRACT FROM AUTHOR]

Natural soils often exhibit significant spatial variability due to their geological history of soil formation. As an attractive anchoring solution for floating offshore structures, this paper has investigated the pullout capacity of strip plate anchors in clay considering the inherent soil spatial variability. In this study, the soil properties were represented by random fields, and an analytical framework was developed to estimate the first two moments and the probability density function of the pullout capacity factor for shallowly and deeply embedded anchors. The analytical approach was validated by the random finite element method (RFEM) over a wide range of soil and anchor parameters. The results show that the coefficient of variation and correlation length of the soil significantly affect the prediction of anchor pullout capacity, providing evidence that sufficient site investigation is of great importance for cost-effective and reliable anchor design. Probabilistic charts were also developed to aid in the probabilistic analysis of anchor pullout capacity. Overall, the developed analytical framework can be used as a good approximation to the computationally intensive RFEM. [ABSTRACT FROM AUTHOR]

This study employed the Japanese Ocean flux data sets with use of remote sensing observations version 2 (JOFURO2) to examine global-scale seasonal variations and trends in Latent heat flux (LHF) over a 19-year period. Furthermore, additional analysis has been conducted to determine the response of LHF to the El Niño Southern Oscillation (ENSO) phenomenon. To assess variability, trends, and strength of relationships with ENSO, statistical score analysis was employed using seasonal means, standard deviations, linear trends, and linear correlations, respectively. In this study, the seasons were classified as December-January-February (DJF), March-April-May (MAM), June-July-August (JJA), and September-October-November (SON). The result of the study revealed that the highest LHF values tracked the annual movement of the sun. In the Northern Hemisphere, the highest spatial trends occurred during DJF, while JJA exhibited the peak values in the Southern Hemisphere. This spatial pattern aligns with the seasonal means of LHF, where the highest and lowest standard deviations and trends coincide with the corresponding regions of high and low LHF. This finding suggests that the standard deviation patterns support the observed variability in seasonal LHF means. The strongest spatial correlations between LHF and ENSO were observed over the Indian Ocean during the SON season. In contrast, the correlations between LHF and ENSO in the Atlantic Ocean exhibited spatial heterogeneity, with a significant correlation only during the DJF season. In general, the seasonal spatio-temporal patterns suggest a dynamic link between LHF and ENSO, potentially linked to large-scale monsoon system changes, the specific locations and distributions of positive/negative trends and standard deviations in LHF reveal a spatial response that appears independent of the ENSO phenomenon. [ABSTRACT FROM AUTHOR]

The proximity of residential areas to traffic corridors has raised concerns among researchers regarding train-induced vibrations. Experimental studies have consistently shown variations in train-induced vibrations, which can be attributed to uncertainties and randomness in both the vibration source and propagation path. This study aims to investigate the influence factors and quantify their contributions to train-induced vibration variations through a comprehensive field measurement campaign and numerical simulations using random finite element models. During the field measurements, vibrations were recorded at the ground surface and the column base for a total of 53 passbys of the same type of metro train on parallel rail lines. Variations in train-induced vibrations caused by the source-to-receiver distance and spatially varied soil properties were analyzed and compared. Specifically, the investigation focused on the spatial variation of soil properties including elastic modulus, density and damping ratio in the topsoil and the second layer of clay, while simultaneously considering the influence of spatial variations of all three soil properties on the transmission of train-induced vibrations from the surface soil to the structural columns. Understanding these variations is crucial for the probabilistic assessment of building serviceability during train passbys. The findings of this research provide valuable insights for probabilistic prediction and evaluation of building vibration comfort. [ABSTRACT FROM AUTHOR]

The determination of the optimal random field element (RFE) size is crucial in soil slope reliability analysis as it governs the trade-off between precision in failure probability calculations and computational efficiency. Given the substantial computational burden associated with smaller RFE sizes, studies on their impact on slope failure probability are scarce. This research examines the influence of RFE size on failure probability and safety factor, employing the Karhunen–Loève expansion to generate random fields and integrating the simplified Bishop method with particle swarm optimization (PSO) to assess slope stability. Through Monte Carlo Simulation (MCS), this study investigates the effects of the ratio of slope height to RFE size (H/De) on slope reliability metrics across two illustrative cases. Results reveal a notable influence of H/De on the distribution of safety factors (Fs) and failure probability (PF), with overestimation observed at smaller H/De ratios. When H/De exceeds 10 for Example 1 and 15 for Example 2, the Fs distribution patterns in both scenarios stabilize significantly, displaying minimal variability. The PF of Example 1 and Example 2 decreases with the increase of H/De and remains basically unchanged when H/De exceeds 10 and 15, respectively. Consequently, a recommended H/De ratio of 20 is proposed based on the analyzed cases, facilitating accurate calculations while mitigating computational overhead. [ABSTRACT FROM AUTHOR]

ABSTRACT The present work proposes a new version of the Green‐FOSM (first‐order second moment) method, which eliminates the iterative calculation process of the original version and, simultaneously, solves the convergence problems related to the mechanical properties of rocks that form the geological formation. In this calculation scheme, the iterative process is eliminated by using a matrix that correlates the nodal displacement vector with the strain vector. Considering the same computational resources, this non‐iterative version of the Green‐FOSM method is up to 200 times faster than the original iterative process. In addition, it allows analyzing problems with more than 10,000 random variables, value that in the original method is less than 3000. To demonstrate its validity, the proposed method is applied to two hypothetical models subjected to different fluid extraction processes. For all the different levels of correlation and spatial variability, the statistical results obtained by the proposed method agree well with the results obtained via Monte Carlo Simulation (MCS). The relationship between CPU times demonstrates that the proposed method is at least 50 times faster than MCS. In the end, the non‐iterative Green‐FOSM method is used to obtain the displacement, strain, and stress fields of a geological section constructed from a seismic image of Brazilian pre‐salt oil region. The results found show that, depending on the levels of spatial variability, the analyzed fields can assume values up to 30.6% higher or lower than the values obtained deterministically. [ABSTRACT FROM AUTHOR]

An inverse characterization approach to identify the in-situ elastic properties of composite constituent materials is developed. The approach relies on displacement measurements available from image-based measurement techniques such as digital image correlation and template matching. An optimization problem is formulated, where the parameters of an assumed functional form describing spatially variable material properties are obtained by minimizing the discrepancies between noisy displacement measurements and the corresponding simulated values. The proposed formulation is analyzed from a statistical inference theory standpoint. It is shown that the approach exhibits estimation consistency, i.e. given noisy input data the identified material properties converge to the true material properties as the number of available measurements increases. The performance of the proposed approach is evaluated by a series of virtual characterizations that mimic physical characterization tests in which fiber centroid displacements are obtained through fiber template matching. The virtual characterizations demonstrate that the effect of measurement noise in identifying the in-situ constituent properties can be mitigated by selecting a sufficiently large measurement dataset. The numerical studies also show that, given a rich measurement dataset, the proposed approach is able to describe increasingly complex spatial variation of properties. [ABSTRACT FROM AUTHOR]

To improve the probability analysis efficiency of slope, this paper proposes an improved particle swarm optimization (IPSO) method to determine the critical sliding surface (CSS) and its corresponding minimum safety factor, which is used to calculate the failure probability of slope. Based on the random fields generated by using Karhunen-Loève (KL) expansion method, the simplified Bishop's method combined with the entry and exit method, the particle swarm optimization (PSO) method, and the IPSO method is used to determine the CSS and its corresponding minimum factor of safety. Then, Monte Carlo simulation is used to estimate the failure probability of slope. The application potential of the IPSO method is demonstrated by re-analyzing two examples. Meaningful comparisons are made to demonstrate the calculating accuracy and calculating efficiency of the IPSO method in searching for the minimum safety factor of slope. Results show that the IPSO can accurately and efficiently determine the minimum safety factor in slope probability analysis considering the spatially variable soils. The IPSO method provides a promising tool for an efficient slope probability analysis. [ABSTRACT FROM AUTHOR]

Proper consideration of variations in soil properties and their effects is necessary to enhance the seismic safety of structures. In this study, the effect of spatial variations in the cyclic resistance ratio on seismic ground behavior was investigated. Initially, dynamic centrifuge model tests were conducted on sandy ground featuring a 20% mixture of weak zones with low relative density and on homogeneous sandy ground with no mixture of weak zones. Subsequently, an effective stress analysis was performed by modeling the distribution of weak zones in the centrifuge model tests. Finally, after confirming the validity of the parameter settings, several analytical models with different weak-zone distributions were generated and numerically analyzed using random field theory. The results indicate that a local mixing of approximately 20% weak zones has only a limited effect on overall ground behavior. However, differences were observed in the rate of increase and dissipation of the excess pore water pressure ratio and in the residual horizontal displacement. [ABSTRACT FROM AUTHOR]

The Global Industry Standard on Tailings Management (GISTM) promotes performance-based approaches in geotechnical assessments. Hence, characterizing the spatial variability of deposited tailings is expected to be a key input for some tailings storage facilities (TSFs); however, it has seldom been investigated. In this study, we assess the spatial variability of thickened and conventional tailings, which have been deposited into the same TSF, providing a unique opportunity to investigate two tailings technologies. A dense array of 15 cone penetration tests (CPTus) with an average offset of 1.5 m has been conducted to collect data. In addition to evaluating the spatial variability, the collected information is also used to assess the potential of machine learning (ML) for detrending when deriving random fields. Using a new proposed stationarity score, we find that an ML-based detrending outperforms traditional procedures for most scenarios. In terms of correlation lengths, we find similar ranges for thickened and conventional tailings (vertical: δwv=0.2–0.6 m , horizontal δwh=1.5–4.5 m) and similar distributions, likely influenced by the depositional processes. In contrast, the variance in the conventional tailings is higher, which we attribute to its segregating nature. Finally, by inspecting previous studies on natural soils, we find that the variability of mine tailings (δwh/δwv=2–21) resembles that observed in alluvial deposits, which we attribute to the parallels in the depositional processes. [ABSTRACT FROM AUTHOR]

Recent studies in earthquake engineering have outlined the difficulty of ground response analyses (GRAs) to replicate the observed ground motion and related variability at borehole array sites. Improvement of the seismic site response estimation requires accounting for and propagating the uncertainties in local soil conditions into surface ground motion. Uncertainties in site conditions arise from a number of factors, which include the uncertainties in the shear-wave velocity (VS) that are mainly caused by the natural spatial variability of soils and rocks. In this paper, a novel VS randomization approach is proposed to propagate the small-scale spatial VS heterogeneities into samples of VS profiles within a nonstationary probabilistic framework, to be further used in one-dimensional (1D) GRAs. The nonstationary approach is based on partitioning a borehole base-case VS profile into several locally stationary layers. The proposed approach was applied at three European sites exhibiting different subsurface soil conditions. Compared with both the classical stationary and an approach from the literature for VS randomization, the proposed approach provides a set of VS profiles fully consistent with the pseudoexperimental site signatures in terms of surface-wave dispersion curves, fundamental and higher-mode resonance frequencies, and site amplification. This paper also outlines the importance of the method used to measure VS profile in both the estimation of depth-dependent variability of VS at a given site and the prediction of site response variability. [ABSTRACT FROM AUTHOR]

Cacao can be cultivated either as a monoculture or within diverse agroforestry systems, which differ, among others, in the choice of shade tree species, tree density, and whether conventional or organic management is applied. Agroforestry can improve ecosystem services in comparison to cacao monocultures, but the effect of different systems on soil quality, a main driver of the whole ecosystem´s health, needs further investigation. We analysed soil samples from a long-term trial in Bolivia that compares conventional and organic monocultures, conventional and organic agroforestry, successional agroforestry, and fallow plots. We measured chemical parameters (pH, organic carbon, available phosphorous), microbial parameters (microbial biomass carbon and phosphorous, microbial activity), and enzymatic activity (phosphatase, β-glucosidase, urease and protease activities). Plant inputs to soil were also quantified in the different systems. Soil organic matter and enzymatic activities were higher in fallow plots than in monocultures. Agroforestry showed intermediate values, not significantly higher than monocultures. Management type (organic versus conventional) had minimal impact on most parameters. Plant matter input quantity did not affect soil properties, suggesting that quality and diversity of plant inputs might have stronger effects than the quantity. Moderate to strong spatial variability was observed for all studied parameters. For microbial and biochemical properties, sampling season also caused strong variation. Our study contributes to highlighting that the characteristics of specific plants, such as those that grow in the fallow plots, could have a higher impact on soil quality than the sheer quantity of fresh plant material incorporated into the soil. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a framework for assessing the reliability of fiber reinforced polymer (FRP)–strengthened reinforced concrete (RC) beams in flexure using stochastic nonlinear finite-element (SNFE) analysis and k-w-means clustering, based on active-learning kriging Monte Carlo simulation (AK-MCS), in which spatial variations in the concrete and bond material properties are considered. A computer algorithm was developed to augment commercially available nonlinear finite-element (FE) analysis software and automate the process for conducting the SNFE clustering-based AK-MCS analysis. The k-w-means clustering was based on the U learning function to provide multipoint enrichment to improve convergence of the stopping criteria by allowing parallel computation of the SNFE models. Parametric analysis indicated the accuracy of the reliability prediction of the examined member and proved the efficiency of the proposed analysis in reducing the number of calls to SNFE models compared with data in the existing literature, when using probability-based stopping criteria. Practical Applications: The quality of the FRP-to-concrete bond is affected by the integrity of the concrete at the interface, which varies across the dimensions of the strengthened member, causing added uncertainty in predicting the structural response, and hence the reliability of the FRP-strengthened member. This study proposes a computationally efficient approach to assess the reliability of FRP-strengthened concrete members by considering the spatial variation in the concrete properties (compressive strength, tensile strength, bulk modulus) and the quality of the FRP-to-concrete bond (shear and normal bond strength) by using an adaptive machine-learning technique. The proposed framework may be utilized by engineers to design FRP-strengthening systems for concrete members experiencing variation in the concrete properties due to poor quality control or active deterioration. [ABSTRACT FROM AUTHOR]

A new standardized metric, the standardized spatial variability index (SSVI), was developed using apportionment entropy. The proposed SSVI was used to study the spatial variability of precipitation extremes and volumes in the Krishna River basin (KRB), India, using gridded data from 1901 to 2020. The analysis was performed at three spatial and two temporal scales. Results show that the proposed SSVI can better capture spatial variability than traditional methods. KRB experienced a significant spatial variability in precipitation volume and extremes. More importantly, there is a concentration of extreme precipitation events in the western part compared to the eastern part. The variability of extreme events showed a significant correlation with the mean elevation of the catchments. Trend analysis reveals significant increasing trends observed in SSVI of extreme precipitation events in the northwestern KRB over the study period, indicating that extreme precipitation events have been evenly spread across the catchment in recent decades. [ABSTRACT FROM AUTHOR]

Landslide is a disastrous geological hazard in the Three Gorges Reservoir (TGR) area of China. In this study, random limit equilibrium analysis is conducted to assess a layered slope stability influenced by multi-factors in spatially variable soils, where the effects of earthquake, reservoir water level drawdown and rainfall are investigated, and possible failure modes and failure paths are identified. Furthermore, some sensitive analysis is performed to investigate the effects of the spatial variability of soil properties, water level drawdown velocity, rainfall intensity and pattern. Results show that the layered slope under complex working conditions exhibits the shallow failure modes, and the sliding surfaces are mainly located at the interface between soil layers. Meanwhile, the shear strength parameters in the shallow depth plays a crucial role in the layered slope failure probability. The earthquake has a prominent effect on slope stability, followed by reservoir water level drawdown and rainfall infiltration. The probability of failure under the combined effects of earthquake and other factors (e.g., 23.5%, 31.4% and 44.6%) is significantly higher than under the combined effects of rainfall and water level drawdown (e.g., 15.9%). Moreover, it can be found that the spatial variability of soil properties, water level drawdown velocity, rainfall intensity and pattern have essential impacts on the slope failure probability. [ABSTRACT FROM AUTHOR]

Geological parameters of soil exhibit spatial variability. Inverse analysis allows the acquisition of accurate spatial distributions of key geological parameters, which is crucial for structural safety assessment. In this study, an ensemble Kalman filter (EnKF) is employed in the context of data assimilation. Random fields are used as the initial input ensembles for the algorithm. The present study effectively integrates the ensemble Kalman filter with the numerical simulation software ABAQUS, enabling the inversion of parameter fields under various operating conditions. An in-house Python code script is developed to control ABAQUS for finite element computations and to obtain observations at target points. During the stepwise computation process, the algorithm can utilize newly acquired observations to accelerate the convergence of the parameter field to the true field. The effectiveness of the algorithm is validated, and the method is applied to a case study of double-tunnel excavation and a stepwise excavation analysis of a three-layered slope. The impact of the number of ensemble members and the ratio of the horizontal correlation scale to the vertical correlation scale of random fields on the effectiveness of updating the parameter field have also been investigated. [ABSTRACT FROM AUTHOR]

To examine structural safety and reliability, an accurate prediction of the variability of structural resistance must first be determined. This may be achieved through extensive physical testing or, more commonly in modern research, synthetic data generation, such as stochastic finite element analyses (SFEAs). Due to the prevalence and versatility of such techniques, and the need for a high level of confidence when performing a safety assessment, an understanding of the accuracy of data derived from SFEAs is essential. In this paper, SFEA models have been developed to predict the responses of 16 unreinforced masonry walls tested in a laboratory under cyclic in-plane lateral loading. Each SFEA has been developed to reflect the as-built conditions of these experimental specimens and focus on predicting the shear capacity of the failure mechanisms that unreinforced masonry shear walls are susceptible to. From the results of these SFEAs, the accuracy (quantified as model error) of this modelling strategy has been estimated by comparing the peak in-plane shear resistances of the laboratory and numerical models. [ABSTRACT FROM AUTHOR]

In this study, the effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure (CRS) were investigated. The numerical model of the CRS embankment was established and validated with the field data. An application programming interface (API) was developed to deal with the data exchanging issue between the numerical model and the spatial variability characterization model. Based on the verified numerical model and the API, the probabilistic analysis with 500 Monte Carlo simulations was automatically computed. Three influencing factors of the retained soil (the mean of the friction angle, the variation of the friction angle and the vertical correlation length of the random field) are analysed by parametric analysis. The results show that the vertical correlation length of the random field is most important in the earth pressure calculation process, while the mean of the friction angle is the factor with least impact. On the whole, the spatial variability of soil properties has minimal impact on the distribution and magnitude of earth pressure behind the retaining structure. [ABSTRACT FROM AUTHOR]

Understanding the spatial variability and driving mechanisms of humus horizon thickness (HHT) degradation is crucial for effective soil degradation prevention in black soil regions. The study compared ordinary kriging interpolation (OK), inverse distance weighted interpolation (IDW), and regression kriging interpolation (RK) using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and relative RMSE to select the most accurate model. Environmental variables were then integrated to predict HHT characteristics. Results indicate that: (1) RK was superior to OK and IDW in characterizing HHT with the smallest ME (11.45), RMSE (14.98), MAE (11.45), and RRMSE (0.44). (2) The average annual temperature (0.29), precipitation (0.27), and digital elevation model (DEM) (0.21) were the primary factors influencing the spatial variability of HHT. (3) The HHT exhibited notable variability, with an increasing trend from the southeast towards the central and northern directions, being the thinnest in the southeast. It was thicker in the northeast and southwest regions, thicker but less dense along the southern Bohai coast, thicker yet sporadically distributed in the northwest (especially Chaoyang and Fuxin), and thick with aggregated distribution over a smaller area in the northeastern direction (e.g., Tieling). These findings provide a scientific basis for accurate soil management in Liaoning Province. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the impact of spatial variability in the subgrade layer on the critical response of pavements and the effectiveness of geogrid reinforcement, employing the random field finite difference analysis (RFFDA). A comprehensive parametric study was conducted to examine the influence of two crucial factors: the coefficient of variation ( COV E ) and scale of fluctuation (SOF) of the subgrade modulus. Further investigation was conducted to uncover the statistical and mechanical mechanisms underlying the impact of subgrade spatial variability with emphasis on the critical strain distributions and their correlation with both the overall modulus and the local spatial variability of the key influence zone. Furthermore, this study explored the influence of subgrade spatial variability on the effectiveness of geogrid in reducing critical strains, considering various placement positions and geogrid moduli. The following main conclusions are drawn: (a) subgrade spatial variability has a substantial amplifying effect on critical pavement strains due to low modulus dominating effect, (b) there exists a worst value of SOF that results in the most unfavorable statistics of critical subgrade strain, (c) the effect of subgrade spatial variability on critical subgrade strain is more pronounced compared to its effect on critical asphalt strain, (d) the mean value of critical subgrade strain in RFFDA can be significantly underestimated when assuming fixed location for the strain, and (e) the effectiveness of geogrid in reducing critical strains is impacted by subgrade spatial variability, with the impact varying with the type of critical strain and geogrid location. Specifically, when placed at the base course–subgrade interface, the ability of geogrid to reduce critical subgrade strain is significantly compromised due to the subgrade spatial variability. [ABSTRACT FROM AUTHOR]

In the reliability analysis of the landslide-stabilizing piles system (LSP), the most commonly used uncertainty models of geotechnical parameters mainly include the homogeneity model (HM) and unconditional random field (UCRF). However, neither of these models considers the existence of borehole data, which may lead to overestimation of the degree of site uncertainty. Based on borehole data in situ, the conditional random field (CRF) of the shear strength of the sliding surface was established through random field theory and the Kriging estimation method. By combining the limit equilibrium method (LEM) and Monte Carlo simulation (MCS), a novel procedure was introduced that can calculate the stability coefficient and design thrust of pile stabilization at the same time. By considering multiple failure modes comprehensively, a calculation flowchart for the failure probability of the LSP was proposed. The spatial layout parameters of pile stabilization, including the pile location, pile spacing, and pile length, significantly impact the calculation results. Compared with using HM and UCRF, the failure probability of the LSP may be lower when using CRF. Based on the allowable failure probability, feasible design solutions were provided. Further comparisons were made on the number of feasible solutions under these three uncertainty models. The research results in this paper might provide some assistance for the optimization of pile stabilization. [ABSTRACT FROM AUTHOR]

Due to time, budget, and/or technical constraints, geotechnical site investigation data from a specific site are often limited and sparse, leading to a long-lasting challenge in characterization of spatially varying geotechnical properties. During preliminary stages of site characterization, geotechnical data from neighboring sites or sites with similar geological conditions are often collected and used as valuable prior knowledge in geotechnical engineering practice. Nevertheless, existing methods for spatial variability characterization often rely solely on site-specific data and cannot effectively incorporate prior knowledge or existing databases. To address this issue, this study proposes a novel machine learning method that systematically combines sparsely measured data at a specific site with existing data from neighboring sites or sites with similar geological settings for characterization of property spatial variability in a data-driven manner. The proposed method starts with the construction of a dictionary that draws the dominant spatially varying patterns from a property measured at sites with similar geology under a dictionary learning framework. Leveraging the developed dictionary, the spatial variability of a property is interpreted from sparse site-specific measurements using Bayesian learning. The effectiveness of the proposed method is demonstrated using real data, and improved performance over existing methods is observed. [ABSTRACT FROM AUTHOR]

This paper aims to study the effects of the non-stationary soil property on the horizontal bearing capacity of three-dimensional monopile in spatially variable soils. The soil undrained shear strength is assumed to obey lognormal distribution and is simulated as non-stationary random fields. The mean value of the undrained shear strength linearly increases with depth, while the standard deviation keeps constant. The random finite-element method is applied to analyze the reliability of the bearing capacity. The influence of the correlations and non-stationary property on the mean and coefficient of variation of the bearing capacity are discussed. It is found that the correlation distance has no obvious effect on the bearing capacity and the bearing capacity increases with the increase of non-stationary coefficient. The results can guide the reliability-based design of horizontally loaded piles embedded in spatially variable soil. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Humans have profoundly altered phosphorus (P) cycling across scales. Agriculturally driven changes (e.g., excessive P‐fertilization and manure addition), in particular, have resulted in pronounced P accumulations in soils, often known as "soil legacy P." These legacy P reserves serve as persistent and long‐term nonpoint sources, inducing downstream eutrophication and ecosystem services degradation. While there is considerable scientific and policy interest in legacy P, its fine‐scale spatial heterogeneity, underlying drivers, and scales of variance remain unclear. Here we present an extensive field sampling (150‐m interval grid) and analysis of 1438 surface soils (0–15 cm) in 2020 for two typical subtropical grassland types managed for livestock production: Intensively managed (IM) and Semi‐natural (SN) pastures. We ask the following questions: (1) What is the spatial variability, and are there hotspots of soil legacy P? (2) Does soil legacy P vary primarily within pastures, among pastures, or between pasture types? (3) How does soil legacy P relate to pasture management intensity, soil and geographic characteristics? and (4) What is the relationship between soil legacy P and aboveground plant tissue P concentration? Our results showed that three measurements of soil legacy P (total P, Mehlich‐1, and Mehlich‐3 extractable P representing labile P pools) varied substantially across the landscape. Spatial autoregressive models revealed that soil organic matter, pH, available Fe and Al, elevation, and pasture management intensity were crucial predictors for spatial patterns of soil P, although models were more reliable for predicting total P (68.9%) than labile P. Our analysis further demonstrated that total variance in soil legacy P was greater in IM than SN pastures, and intensified pasture management rescaled spatial patterns of soil legacy P. In particular, after controlling for sample size, soil P was extremely variable at small scales, with variance diminished as spatial scale increased. Our results suggest that broad pasture‐ or farm‐level best management practices may be limited and less efficient, especially for more IM pastures. Rather, management to curtail soil legacy P and mitigate P loading and losses should be implemented at fine scales designed to target spatially distinct P hotspots across the landscape. [ABSTRACT FROM AUTHOR]

Accurate assessment of pile group's performance in spatially variable unsaturated expansive soil has long been a challenge in geotechnical engineering. This paper presents a methodology to perform reliability analysis for vertically loaded pile group, where the modified load transfer method (LTM) is utilised to investigate the load–displacement response considering the pile–pile interaction and the non-linear relationship of the pile–soil interface under the influence of matric suction reduction and the swelling of expansive soil; the Karhunen–Loève (KL) expansion method is adopted to simulate the spatial variability of soil parameters; the first–order reliability method (FORM) is utilised to perform reliability analysis of each pile in the pile group; and the reliability analysis of pile group is then performed using the sequential compounding method (SCM) by considering the pile group as a parallel system. By applying the proposed methodology to a 3 × 3 pile group under different vertical loads and infiltration times, the relative magnitudes of reliability indices for different piles in the pile group and the pile group system under two failure modes of uplifting and sinking are identified. The effects of soil's spatial variability and pile spacing on the reliability of pile group are also analysed. [ABSTRACT FROM AUTHOR]

Quantification of soil water-physical properties and their spatial variation is important to better predict soil structure and functioning, as well as associated spatial patterns in the vegetation. The provision of site-specific soil data further facilitates the implementation of enhanced land use and management practices. Using geostatistical methods, this study quantified the spatial distribution of soil bulk density (SBD), soil moisture (SM), capillary water-holding capacity (CWHC), capillary porosity (CP), non-capillary porosity (NCP), and total porosity (TP) in southern subtropical forests located at the Tropical Forest Research Center in Pingxiang City, China. A topographic map (scale = 1:10,000) was used to create a grid of l km squares across the study area. At the intersections of the grid squares, the described soil water-physical properties were measured. By calculating the coefficient of variation for each soil water-physical property, all measured soil water-physical properties were found to show moderate spatial heterogeneity. Exponential, gaussian, spherical, and linear models were used to fit the semivariograms of the measured soil water-physical properties. Across all soil water-physical properties, the range A0 variable (i.e., the separation distance between the semivariance and the sill value) measured between 3419 m and 14,156 m. The nugget-to-sill ratio ranged from 9 to 41%, indicating variations in the level of spatial autocorrelation among the soil water-physical properties. Many of the soil water-physical properties were strongly correlated (as assessed using Pearson correlation coefficients). Spatial distribution maps of the soil water-physical properties created via ordinary kriging (OK) showed that most water-physical properties had clumped (aggregated) distributions. SBD showed the opposite spatial pattern to SM and CWHC. Meanwhile, CP and TP showed similar distributions. [ABSTRACT FROM AUTHOR]

Historically, landslides have been the primary type of geological disaster worldwide. Generally, the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations. Moreover, the stability of reservoir banks changes with the long-term dynamics of external disaster-causing factors. Thus, assessing the time-varying reliability of reservoir landslides remains a challenge. In this paper, a machine learning (ML) based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction. This study systematically investigated the prediction performances of three ML algorithms, i.e. multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Additionally, the effects of the data quantity and data ratio on the predictive power of deep learning models are considered. The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides. The CNN model outperforms both the MLP and LSTM models in predicting the failure probability. Furthermore, selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Manufacturers of agricultural machines, when designing, pay a little attention to its impact on soil, thus producing models with high compression loads on the soil or with a small contact area between the tyres/tracks and the soil surface. Therefore, the aim of this study is to evaluate the negative impact of both wheeled and tracked agricultural tractors on the soil, in terms of soil compaction, and its causes (i. e. design features of tractor tyres/tracks), during the last six decades (i. e. from 1961 to 2021). Soil compaction is caused by the pressure applied by agricultural machines on the soil through the contact area of their tyres/tracks with the soil surface. So, the main indicator of the negative impact on the soil by the tractors manufactured during the last 60 years, i. e. the average pressure applied by the tyres or tracks of tractors manufactured in EU and in the post-Soviet cuntries from 1961 to 2021 to the soil, was computed. A general decrease of the average pressure of the tyres/tracks on the soil can be observed in 1980s and 1990s, followed by its general increase since 2000, above all for the tractors having power higher than 140 kW. Thus, there is an urgent need to assess spatial and temporal changes in soil vulnerability to compaction, that depends on weather conditions and soil properties, as well as agricultural management practices, and can only be fully assessed by means of a combination of traditional techniques (i. e. use of soil cone penetrometer followed by 2D mapping using GIS or 3D mapping through geostatistics) and mechanical approaches (i. e. computation of agricultural machine parameters – soil contact area). The results show that tractor manufacturers did not take care of reducing soil compaction during the considered period.

The objective of this paper is to investigate the effects of spatial variability in coastal silty soil properties on the stability and failure probability of deep excavation. A super-large irregularly shaped deep excavation in the coastal area in Wenzhou, China, is considered. A numerical model is established based on the random field theory, finite difference method and Monte-Carlo simulation method. Focusing on the friction angle and cohesion, the effects of horizontal and vertical correlation distances and variation coefficients on excavation stability are systematically studied. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of the correlation distance on the surface settlement and horizontal displacement of pile. The results show that the greater correlation distance leads to a larger dispersion degree of the ground settlement curve and the displacement curve of the diaphragm wall around. The influence of the horizontal correlation distance on land settlement is more significant. The horizontal and vertical displacements of the pile top are less affected by the correlation distance. Compared with the deterministic analysis, the random analysis results considering the soil spatial variability produce larger displacement.

Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters, making the slope reliability assessment closer to the engineering reality. However, multi-source information (including test data, monitored data, field observation and slope survival records) is rarely used in current probabilistic back-analysis. Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved. In this paper, a framework by integrating a modified Bayesian Updating with Subset simulation (mBUS) method with adaptive Conditional Sampling (aCS) algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction. Within this framework, the high-dimensional probabilistic back-analysis problem can be easily tackled, and the multi-source information (e.g. monitored pressure heads and slope survival records) can be fully used in the back-analysis. A real Taoyuan landslide case in Taiwan, China is investigated to illustrate the effectiveness and performance of the established framework. The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations. Furthermore, the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12, 2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City, Taiwan, China.

Abstract Reconstructing signals which are embedding spatial patterns such as Electrical resistivity tomography, is a process that should require to reconstruct first the spatial correlation of the damaged signals. This paper proposes an approach that implements an Unstationary Kriging (UNK) to reconstruct the experimental variogram of a damaged synthetic pseudo section within a set of pseudo sections coming from the same survey. We used and compared 02 other simple methods which are Linear Regression (LR) and Ordinary Kriging (OK), to test the hypothesis we formulate to link the experimental variograms coming from the same ERT survey. We implemented the UNK using Discrete Fourier Transforms (DFT) for trend modeling. After an implementation of the hybrid process (UNK) on 02 sets of data which are synthetics, we observed that the LR and the UNK methods present an interest. They both reconstruct signals with a +90% rate of accuracy, but when there is no structure or spatial correlation within the data, the LR is unstable. DFT was also tested alone for reconstruction but was mainly used in this study to help in computing the trends for each set of variographic signals. In the end, we conclude on an evidence that is: the proposed hybrid process is a promising way to reconstruct variographic signals, since we can improve it after more time invested to dig deep into the modeling of each of his components.

This study employed the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations version 2 (J-OFURO2) to examine global-scale seasonal variations and trends in Latent Heat Flux (LHF) over a 19-year period. Furthermore, additional analysis has been conducted to determine the response of LHF to the El Niño Southern Oscillation (ENSO) phenomenon. To assess variability, trends, and strength of relationships with ENSO, statistical score analysis was employed using seasonal means, standard deviations, linear trends, and linear correlations, respectively. In this study, the seasons were classified as December-January-February (DJF), March-April-May (MAM), June-July-August (JJA), and September-October-November (SON). The result of the study revealed that the highest LHF values tracked the annual movement of the sun. In the Northern Hemisphere, the highest spatial trends occurred during DJF, while JJA exhibited the peak values in the Southern Hemisphere. This spatial pattern aligns with the seasonal means of LHF, where the highest and lowest standard deviations and trends coincide with the corresponding regions of high and low LHF. This finding suggests that the standard deviation patterns support the observed variability in seasonal LHF means. The strongest spatial correlations between LHF and ENSO were observed over the Indian Ocean during the SON season. In contrast, the correlations between LHF and ENSO in the Atlantic Ocean exhibited spatial heterogeneity, with a significant correlation only during the DJF season. In general, the seasonal spatio-temporal patterns suggest a dynamic link between LHF and ENSO, potentially linked to large-scale monsoon system changes, the specific locations and distributions of positive/negative trends and standard deviations in LHF reveal a spatial response that appears independent of the ENSO phenomenon.

Abstract Background The mammalian gut microbiome includes a community of eukaryotes with significant taxonomic and functional diversity termed the eukaryome. The molecular analysis of eukaryotic diversity in microbiomes of wild mammals is still in its early stages due to the recent emergence of interest in this field. This study aimed to fill this knowledge gap by collecting data on eukaryotic species found in the intestines of wild rodents. Because little is known about the influence of climate on the gut eukaryome, we compared the composition of the gut eukaryotes in two rodent species, Mus musculus domesticus and Acomys cahirinus, which inhabit a transect crossing a temperate and tropical zone on the Jordanian side of the Great Rift Valley (GRV). Methods We used high-throughput amplicon sequencing targeting the 18S rRNA gene in fecal samples from rodents to identify eukaryotic organisms, their relative abundance, and their potential for pathogenicity. Results Nematodes and protozoa were the most prevalent species in the eukaryome communities, whereas fungi made up 6.5% of the total. Sixty percent of the eukaryotic ASVs belonged to taxa that included known pathogens. Eighty percent of the rodents were infected with pinworms, specifically Syphacia obvelata. Eukaryotic species diversity differed significantly between bioclimatic zones (p = 0.001). Nippostrongylus brasiliensis and Aspiculuris tetraptera were found to be present exclusively in the Sudanian zone rodents. This area has not reported any cases of Trichuris infections. Yet, Capillaria infestations were unique to the Mediterranean region, while Trichuris vulpis infestations were also prevalent in the Mediterranean and Irano-Turanian regions. Conclusions This study highlights the importance of considering host species diversity and environmental factors when studying eukaryome composition in wild mammals. These data will be valuable as a reference to eukaryome study. Graphical Abstract

This paper summarizes the spatial-temporal analysis of aerosol dust loading and aerosol dust extinction over the Indian subcontinent for identifying trends and patterns for a period of 40 years from 1980 to 2019. These analyses are based on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data using Modern-Era Retrospective Analysis for Research and Applications, Version 2. The high-resolution data of CALIPSO indicate a high concentration of dust column mass density over the northwestern part of India. It increases from central India to Rajasthan and Jammu & Kashmir regions. Mean dust aerosol optical depth also suggests an increase in the dust loading over northwestern part of the country.

Background: The mammalian gut microbiome includes a community of eukaryotes with significant taxonomic and functional diversity termed the eukaryome. The molecular analysis of eukaryotic diversity in microbiomes of wild mammals is still in its early stages due to the recent emergence of interest in this field. This study aimed to fill this knowledge gap by collecting data on eukaryotic species found in the intestines of wild rodents. Because little is known about the influence of climate on the gut eukaryome, we compared the composition of the gut eukaryotes in two rodent species, Mus musculus domesticus and Acomys cahirinus, which inhabit a transect crossing a temperate and tropical zone on the Jordanian side of the Great Rift Valley (GRV). Methods: We used high-throughput amplicon sequencing targeting the 18S rRNA gene in fecal samples from rodents to identify eukaryotic organisms, their relative abundance, and their potential for pathogenicity. Results: Nematodes and protozoa were the most prevalent species in the eukaryome communities, whereas fungi made up 6.5% of the total. Sixty percent of the eukaryotic ASVs belonged to taxa that included known pathogens. Eighty percent of the rodents were infected with pinworms, specifically Syphacia obvelata. Eukaryotic species diversity differed significantly between bioclimatic zones (p = 0.001). Nippostrongylus brasiliensis and Aspiculuris tetraptera were found to be present exclusively in the Sudanian zone rodents. This area has not reported any cases of Trichuris infections. Yet, Capillaria infestations were unique to the Mediterranean region, while Trichuris vulpis infestations were also prevalent in the Mediterranean and Irano-Turanian regions. Conclusions: This study highlights the importance of considering host species diversity and environmental factors when studying eukaryome composition in wild mammals. These data will be valuable as a reference to eukaryome study. [ABSTRACT FROM AUTHOR]

Rapid urbanization has caused numerous construction solid waste landfills. Few studies have explored the impact of multi-source waste soils with remarkable spatial variability on the reliability of landfills. This study aims to characterize the site-specific spatial variability of stockpiled waste soils and perform a probabilistic stability assessment of a real-world landfill slope. An integrated probabilistic landfill analysis framework is developed, consisting of spatial variability characterization, random field modeling, and probabilistic slope stability analysis. Seventy-four groups of clayey waste soil parameters were obtained from a landfill in Hangzhou, China. The artificial mixing behavior makes the shear strengths of waste soils have high variability (coefficients of variation close to 0.5), a high positive cross-correlation (about 0.9), and bimodal marginal probability distributions. The spatial variabilities of the cohesion and friction angle of clayey waste soils are similar, with vertical and horizontal scales of fluctuation being 3.23 m and 34.32 m, respectively. The conditional random field modeling of clayey waste soils provides a more realistic slope stability assessment of the landfill and its impact on the failure probability highly relies on the strength and location of boreholes. More slope reinforcement measures should be adopted for safety management of the next stage of landfilling. [ABSTRACT FROM AUTHOR]

Soil being a naturally formed material, the prediction and analysis of anchor uplift capacity incorporating the inherent variabilities in the soil properties is essential. This paper presents a probabilistic analysis of horizontal strip anchors in spatially varying soil using the multivariate adaptive regression splines (MARS) as a prediction model. The use of MARS in spatially varying soils is proposed, and its advantage over the conventional expensive Monte Carlo simulations is reported in this paper. The soil cohesion and friction angle have been modeled as lognormal random fields, and the influence of the spatially distributed soil properties on the anchor uplift capacity is assessed in the current study. Furthermore, a comparison between the deterministic and reliability-based design methodologies is presented, and their implications on the design of foundation are discussed. The results indicate that the code-specified factor of safety (FOS) value of 1.5 does not fulfill the target probability of failure ( P f ) values recommended in probabilistic design. A minimum FOS = 2 is required to satisfy the P f ≤ 0.001 criteria, which is usually recommended for the design of foundations. [ABSTRACT FROM AUTHOR]

The stability analysis of tunnel faces in multilayered soils presents challenges due to the inherent variability in natural soils. Although the random field finite-element methods offer a reliable approach to address such variability, their heavy computational demands have been a significant drawback. To overcome this limitation, this study presents a novel deep learning–based method for efficient tunnel face stability analysis in layered soils with spatial variability. By combining the merits of convolutional neural networks (CNNs) and U-Net, the proposed method trains surrogate models using a small but sufficient number of random field images to effectively learn high-level features that encompass spatial variabilities, which significantly enhances computational efficiency. In particular, U-Net generates precise displacement field images based on random field images, enabling the discrimination of tunnel face collapse failure modes. To validate the effectiveness of this proposal, a comprehensive case study involving layered soils with spatial variabilities is conducted. The remarkable agreement between the outputs of CNNs and U-Net and the predictions of finite-element simulations underscores the promising potential of using deep-learning models as a surrogate for analyzing the stability of tunnel faces in spatially variable layered soils. Last but not least, the key innovation of this work lies in the pioneering application of U-Net for geotechnical reliability analysis. [ABSTRACT FROM AUTHOR]

Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.Highlights: Overall, the Random Forest algorithm was the most accurate in mapping soil properties in Extremadura. Chemical properties and key nutrients exhibit more variability than soil physical properties. The number of soil samples determines the performance of the methods used for soil property mapping. Vegetation indices and topographic attributes emerge as the most relevant variables for mapping soil physical properties. Climatic variables are more important in mapping chemical properties and key soil nutrients. [ABSTRACT FROM AUTHOR]

Rainfall infiltration analysis has a great significance to the mitigation and risk assessment of rainfall-induced landslides. The original Green–Ampt (GA) model ignored the fact that a transitional layer exists in infiltration regions of soils under the rainfall permeation; moreover, it cannot effectively analyze the rainfall-infiltrated heterogeneous slope considering the spatial variability of saturated hydraulic conductivity (ks). In this study, an improved GA model is proposed for the rainfall-infiltration analysis of heterogeneous slopes. Four common slope cases are investigated to validate the effectiveness of the proposed model. An infinite slope model is taken as an illustrative example to investigate the distributions of volumetric water content and slope stability under the rainfall infiltration. The results show that the distributions of volumetric water content and factor of safety (Fs) obtained from the proposed model are in very good agreement with the numerical results of the Richards' equation. In contrast, the modified GA model obtains biased distributions of volumetric water content and smaller Fs for the same cases. The results show that the proposed GA model can accurately identify the location of critical slip surface of the slope, and as such it provides an efficient method for risk analysis and control of slopes susceptible to landslide. [ABSTRACT FROM AUTHOR]

Slope stability is a major safety concern of rockfill embankments. Since rockfills are incohesive materials, only friction angle is considered as a shear strength parameter in the slope stability analysis of rockfill embankments. Recently, it was found that confining pressure can significantly affect the mean value and variance of the friction angle of rockfills. Since the confining pressure spatially varies within a rockfill embankment, the effect of stress-dependent spatial variability in the friction angle of rockfills should be investigated for slope stability evaluation of rockfill embankments. In the framework of the Limit Equilibrium Method (LEM), an approach is proposed for the slope stability analysis of rockfill embankments considering the stress-dependent spatial variability in the friction angle. The safety factors of slope stability are computed with variable values of the friction angle at the bases of slices which are determined by the stress-dependent mean value and variance of the friction angle of rockfills. The slope stability of a homogeneous rockfill embankment is analyzed to illustrate the proposed approach, and a parametric analysis is carried out to explore the effect of variation in the parameters of the variance function of friction angle on slope stability. The illustrative example demonstrates that the stress-dependent spatial variability of friction angle along the slip surface is obvious and is affected by the location of the slip surface and the loading condition. The effects of the stress-dependent spatial variability of the friction angle on the slope stability of high rockfill embankments should be considered. [ABSTRACT FROM AUTHOR]

Accurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China. [ABSTRACT FROM AUTHOR]

Mobile monitoring is used as an additional tool to collect air quality data at a high spatial resolution and to complement data from fixed air quality stations. Citizens are interested in contributing to air quality monitoring, and while the availability of low-cost air quality sensors can create opportunities to measure the air quality at a high spatial resolution, the data are often of lower quality, and sensors that measure combustion-related aerosols (like black carbon) are not commonly available. Mobile monitoring using a mid-range instrument can fill this gap. We present the results of a mobile BC (black carbon) monitoring campaign performed by citizens in Mechelen as part of a local citizen observatory (CO), Meet Mee Mechelen, initiated as part of the European H2020 project, Ground Truth 2.0. The goal of the study was two-fold: (1) to propose and evaluate a mobile monitoring method (data collection and data processing) to construct pollution maps of BC concentrations and (2) to demonstrate how to organize community-based air quality monitoring to measure both the spatial and temporal variations in air pollution levels. Measurements were taken during peak hours in four campaigns characterized by different meteorological conditions: October–November 2017, February–March 2018, June–July 2018 and September 2018. The results show large spatial and temporal variabilities. Spatial variability is influenced by traffic volume, stop-and-go traffic and also the building environment and the distance of biking paths from road traffic. The four different campaigns show similar spatial patterns, but due to background and meteorological influences, the absolute concentrations differ between seasons. A rescaling method using data from fixed stations in the air quality monitoring network (AQMN) was presented to construct maps representative of longer periods. This paper shows that mobile measurements can be used by CO to assess the spatial variability of air quality in a city. The data can be used to evaluate mobility plans, carry out hot spot detection, evaluate the exposure of cyclists as a function of cycling infrastructure and perform model validation. However, it is important to use high-quality instruments and apply the correct measurement methodology (number of repetitions, season) to obtain meaningful data. [ABSTRACT FROM AUTHOR]

This study involved an embankment settlement analysis of a case study of the railway loess-filled embankment section of the Baozhong railway in Guyuan, China. The two-dimensional spatial variability of the soil parameters in the embankment fill layer was simulated using random field theory and the finite difference method. The covariance matrix decomposition method was used to discretize the soil parameters within different geological cells. Settlement predictions were evaluated through Monte Carlo simulations with varying horizontal scale of fluctuations (SOFs), and the embankment surface settlement values at the centerline were compared to the deterministic calculation model (DCM), random variable model (RVM), and random field model (RFM). The results indicated that the horizontal SOFs ranging from 40–60 m in the loess-filled embankment can effectively capture the spatial distribution pattern of the soil parameters. Spatial variability exerts a substantial influence on embankment settlements. Neglecting the spatial variability of soil properties underestimates the risk of surface settlement in embankments compared to the DCM. Therefore, RFM analysis that considers the spatial variability of soil parameters must be incorporated to ensure safe operation and design in subgrade engineering. [ABSTRACT FROM AUTHOR]

Risk assessment of seismically loaded slopes is often a prerequisite for guiding decision-making and mitigation, which could be more difficult to quantify when taking the spatial variability of material properties into account. In this study, a random finite-element limit analysis (RFELA) is developed to assess the risk of a seismically loaded slope where the undrained shear strength of soils is spatially variable with depth. The nonstationary random field is first introduced to model the linearly increasing undrained shear strength. Then, finite-element limit analysis (FELA) is employed to evaluate the seismic stability and consequence, in which the seismic loading is characterized by the pseudostatic approach with a range of horizontal seismic coefficients. Finally, the quantitative risk assessment is conducted based on Monte Carlo simulations. The results show that using the nonstationary random field to model the soil spatial variability could significantly reduce the risk compared with the stationary random field, which matches better with the site-specific data. The risk of the slope failure increases with the increase in the seismic coefficient. In addition, the effects of the correlation structure of the undrained shear strength, which includes the coefficient of variation (COV) and autocorrelation distance, on the risk assessment are studied by parametric analyses. [ABSTRACT FROM AUTHOR]