Iron pipes connected by bell-spigot joints are utilized in buried pipeline systems for urban water and gas supply networks. The joints are the weak points of buried iron pipelines, which are particularly vulnerable to damage from excessive axial opening during seismic motion. The axial joint opening, resulting from the relative soil displacement surrounding the pipeline, is an important indicator for the seismic response of buried iron pipelines. The spatial variability of soil properties has a significant influence on the seismic response of the site soil, which subsequently affects the seismic response of the buried iron pipeline. In this study, two-dimensional finite element models of a generic site with explicit consideration of random soil properties and random mechanical properties of pipeline joints were established to investigate the seismic response of the site soil and the buried pipeline, respectively. The numerical results show that with consideration of the spatial variability of soil properties, the maximum axial opening of pipeline joints increases by at least 61.7 %, compared to the deterministic case. Moreover, in the case considering the variability of pipeline-soil interactions and joint resistance, the spatial variability of soil properties remains the dominant factor influencing the seismic response of buried iron pipelines.

Antislide piles are currently applied widely in slope reinforcement engineering, but investigation of the stability of slopes stabilized with this measure under the action of mainshock-aftershock (Ms-As) sequences is very limited. In this study, the probability density evolution method (PDEM) and the Newmark method is adopted to evaluate the reliability of slope reinforced by antislide piles subjected to Ms-As sequences considering the spatial variability of material parameters. Firstly, stochastic Ms-As sequences are generated by combining a physical function model, the Copula function, and the narrowband harmonic group superposition method. In addition, the spectral representation method is taken to generate the random field and the parameters are assigned to the numerical model. Then, the Newmark method is applied to batch-calculate the permanent displacement (Disp) of the slope caused by the Ms-As sequences. The effects of pile position, pile length, and coefficient of variation of cohesion and friction angle (COVC and COVF) on the average value of Disp are discussed. Finally, based on the PDEM, the seismic reliability of the slope strengthened by antislide piles subjected to the Ms-As sequences are obtained. The research results indicate that with the COV increases, the average value of Disp of the slope shows a gradual tendency to increase, and the average value is more sensitive to COVC. Compared with the unreinforced slope, the Disp of the slope strengthened by antislide piles is reduced. The cumulative damage caused by the aftershock and the risk of failure can be significantly reduced by setting a reasonable antislide pile.

The bearing capacity of offshore single pile composite foundations can be significantly affected by the spatially variable soil properties and the different soil layers installing the pile. The previous research mainly focuses on effects of isotropy or transverse anisotropy spatial variable soil on the bearing capacity and failure mechanism of piles embedded in a single soil layer. The practical sites generally contain multiple soil layers and the soil properties may exhibit strong rotated anisotropy characteristics due to the complex geological movements. However, how the rotated anisotropy spatial variability of soil property affects the bearing capacity of the offshore single pile composite foundation embedded into multiple soil layers remains unclear. This study aims to systematically investigate the effects of rotated anisotropy three-dimensional spatial variability of soil properties on the vertical bearing capacity of the offshore single pile composite foundation embedded into two soil layers. The three-dimensional random finite element is used to simulate the pile-soil response of the offshore single pile composite foundations under vertical static loads. The influence of the scale of fluctuation delta, rotated angle of anisotropy, and coefficient of variation of different soil parameters including elastic modulus E, cohesion c, and internal friction angle phi are investigated. The results show that the COV of E and c have a larger influence than that of phi. The rotated anisotropy of the upper-layer soil generally has a prominent effect on the bearing capacity of the pile compared with the lower-layer soil especially when the horizontal scale of fluctuation is large. These findings underscore the importance of accounting for rotated anisotropy spatial variability in the design of offshore single pile composite foundations.

The Arctic experiences rapid climate change, but our ability to predict how this will influence plant communities is hampered by a lack of data on the extent to which different species are associated with particular environmental conditions, how these conditions are interlinked, and how they will change in coming years. Increasing temperatures may negatively affect plants associated with cold areas due to increased competition with warm-adapted species, but less so if local temperature variability is larger than the expected increase. Here we studied the potential drivers of vegetation composition and species richness along coast to inland and altitudinal gradients by the Nuuk fjord in western Greenland using hierarchical modelling of species communities (HMSC) and linear mixed models. Community composition was more strongly associated with random variability at intermediate spatial scales (among plot groups 500 m apart) than with large-scale variability in summer temperature, altitude or soil moisture, and the variation in community composition along the fjord was small. Species richness was related to plant cover, altitude and slope steepness, which explained 42% of the variation, but not to summer temperature. Jointly, this suggests that the direct effect of climate change will be weak, and that many species are associated with microhabitat variability. However, species richness peaked at intermediate cover, suggesting that an increase in plant cover under warming climatic conditions may lead to decreasing plant diversity.

Determining the burial depth for offshore pipelines to resist impact load is challenging owing to the spatial variability of soil strengths, which proves to significantly affect failure behaviours of soils and pipelines. To facilitate the design, accurate and fast evaluation on pipeline damage is required. Here, an integrated surrogate model was developed to forecast impact damage of pipelines buried in spatially varied soils. Through coupling the random field and numerical simulation, a stochastic finite element analysis framework was derived and verified to yield the datasets; Based on the scheme of feature extraction - integration from convolution neural network, the surrogate model was established, which mapped the three-dimensional soil spatial field to the structural response. Prediction mechanism of the developed model was explored, where correlations among soil spatial distribution patterns, failure mechanisms and feature recognitions were discussed. The models enabled to capture the key features representing the failure mechanisms under random soil conditions, including the local failure mode of soil and pipe-soil interactions, which theoretically explained its feasibility in damage estimation. Further, model performance was comprehensively evaluated with regard to prediction accuracy, uncertainty quantification, and transfer learning, and the corresponding causes were investigated. Satisfactory performance and high computation efficiency were demonstrated.

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

Investigations of seismic response of underground structures often assume homogeneous or layered homogeneous sites. However, significant spatial variability in soil parameters may lead to vastly different underground structure performance from that obtained for homogeneous sites. Based on random field theory, this study models the spatial variability of the soil elastic modulus, cohesion, and friction angle using the Karhunen-Loe`ve (K-L) expansion method. Target acceleration response spectra are generated according to standards, and the trigonometric series method is employed to create artificial seismic waves of four different intensities. Nonlinear dynamic analyses of underground structures under deterministic and random field conditions are conducted using ABAQUS software. The study comprehensively analyzes the structural damage state, internal forces, interstory displacement, and drift ratio to evaluate the station structure's performance under different seismic intensities. Results show that the spatial variability of soil parameters significantly impacts the dynamic response of underground structures, especially for stronger earthquakes. The variability of soil stiffness and strength parameters leads to greater fluctuations and uncertainties in displacement and internal force responses, exacerbating structural damage. It is recommended that when the peak ground acceleration (PGA) reaches or exceeds 0.5 g, the spatial variability of soil parameters should be incorporated into the analysis to ensure a reliable assessment of the structural seismic performance.

For offshore platforms installed in seismically active regions, maintaining the safety of operations is an important concern. Therefore, the reliability of these structures, under earthquake ground motions, should be evaluated accurately. In this study, reliability methods are applied to determine the probability of failure of jacket platforms against extreme level earthquake (ELE), considering uncertainties in ground motions and the properties of the structure and soil. They are verified by two variance reduction Monte Carlo sampling methods to find the most efficient method in terms of both accuracy and calculation time. During the ELE event, also called strength level earthquake, structural members and foundation components are permitted to sustain localised and limited nonlinear behaviour, so a force-based criterion is utilized for the limit-state function. The results indicate that all reliability methods, except for FOSM, provide a good approximation of the probability of failure. Also, Point-fitting SORM is the most efficient method.

The aim of the present study is to assess the impact of rotational anisotropy on the undrained bearing capacity of a surface strip footing over an unlined circular tunnel on spatially variable clayey soil. The finite-difference method (FDM) is utilised to perform both deterministic and stochastic analyses. The Monte Carlo simulation approach is used to estimate the mean stochastic bearing capacity factor (mu Npro) and probability of failure (pf) of the entire system. The responses are evaluated for different geometric and spatially variable parameters and the strata rotation angle (beta). The failure patterns and the required factor of safety (FSr) corresponding to a specific probability of failure (e.g. pft = 0.01%) are determined for various parameters. The results obtained for the rotational anisotropy (beta$\ne \;$not equal 0) are observed to be significantly different from those for horizontal anisotropic structure (beta = 0), and considering only the horizontal anisotropic structure may lead to the overestimation or underestimation of the response of the structure.

Soil-water characteristics, which vary with hydrological events such as rainfall, significantly influence soil strength properties. These properties are crucial determinants of the bearing capacity of foundations. Moreover, shear strength characteristics of soils are inherently spatially variable, and considering them as homogeneous parameters can result in unreliable design. This paper presents a probabilistic study of the two-dimensional bearing capacity of a strip footing on spatially random, unsaturated fine-grained soil using Monte Carlo simulation. The study employs the hydro-mechanical random finite difference method through MATLAB programming along with FLAC2D software. The undrained shear strength under saturated conditions is modelled as random fields using a log-normal distribution. The generated random values are then made depth-dependent by correlating them with matric suction. Initially, matric suction is assumed to be under a hydrostatic condition and decreases linearly with depth to zero at the groundwater level. Afterward, unsaturated soil is subjected to rainfall with different durations, resulting in the non-linear distribution of matric suction and, consequently, the mean value of undrained shear strength in depth. The results showed that rainfall infiltration impacts the strength characteristics of near-surface heterogeneous strata, leading to significant effects on the bearing capacity and failure mechanism of footing.