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.

The vadose zone acts as a natural buffer that prevents contaminants such as arsenic (As) from contaminating groundwater resources. Despite its capability to retain As, our previous studies revealed that a substantial amount of As could be remobilized from soil under repeated wet-dry conditions. Overlooking this might underestimate the potential risk of groundwater contamination. This study quantified the remobilization of As in the vadose zone and developed a prediction model based on soil properties. 22 unsaturated soil columns were used to simulate vadose zones with varying soil properties. Repeated wet-dry cycles were conducted upon the As-retaining soil columns. Consequently, 13.9-150.6 mg/kg of As was remobilized from the columns, which corresponds to 37.0-74.6 % of initially retained As. From the experimental results, a machine learning model using a random forest algorithm was established to predict the potential for As remobilization based on readily accessible soil properties, including organic matter (OM) content, iron (Fe) content, uniformity coefficient, D30, and bulk density. Shapley additive explanation analyses revealed the interrelated effects of multiple soil prop-erties. D30, which is inter-related with Fe content, exhibited the highest contribution to As remobilization, fol-lowed by OM content, which was partially mediated by bulk density.

The stability of soil in high-altitude regions is significantly affected by freeze-thaw cycles, which alter its mechanical and physical properties. This study investigates the impact of 12 consecutive freeze-thaw cycles on poorly graded sandy-silt soil collected from Arunachal Pradesh. To enhance soil resistance, a bio-slurry containing urea (60 g/L) and calcium chloride (111 g/L), along with vetiver and bamboo fibers (by soil weight), was introduced as a stabilizing agent. The durability of the treated soil was evaluated by measuring the weight fluctuations after each cycle and assessing unconfined compressive strength (UCS) after 5, 10, and 12 cycles. The results revealed that untreated soil experienced a 50% reduction in UCS, while bioslurry-treated soil retained 70-80% of its original strength after 12 freeze-thaw cycles. The greatest strength retention was observed in soil treated with bioslurry and bamboo fiber, which retained 80% of its strength, followed by vetiver-treated soil at 75% strength retention. Weight loss measurements indicated that untreated soil samples lost 9.5% of their initial mass, whereas bioslurry-treated samples exhibited only a 3-5% weight loss. The findings of the study highlight the potential of bioslurry and natural fibers in mitigating freeze-thaw-induced soil degradation, making them suitable for applications in geotechnical engineering in cold-climate regions.

PurposeThis paper aims to develop a probabilistic framework which combines uncoupled cofferdam stability analysis, random forest and Monte Carlo simulation for cofferdam reliability analysis.Design/methodology/approachThe finite element method and limit equilibrium method are used to calculate the seepage field and stability of cofferdam, respectively. Sufficient training and validating random samples are generated to obtain a random forest surrogate model with acceptable accuracy. The calibrated random forest model combined with MCS is used to conduct cofferdam reliability analysis. The proposed methodology is illustrated using a typical cofferdam model.FindingsThe numerical simulation results demonstrate that a larger pore water pressure leads to a lower stability of the cofferdam and vice versa. The increase in the slope angle significantly reduces the stability of cofferdam on the corresponding side, while the stability of cofferdam on the other side is mainly affected by the internal pore water pressure. The increase in the width and height of the reverse pressure platform significantly enhances the stability of cofferdam, and the changes in the angle of the reverse pressure platform affect the stability of cofferdam to some extent. The probability of failure (Pf) of cofferdam increases gradually with increasing vertical and horizontal scales of fluctuation, coefficient of variation, and cross-correlation coefficient when the degradation degree of soil properties is low. It is worth noting that the effect of vertical and horizontal scales of fluctuation, coefficient of variation, and cross-correlation coefficient on the Pf of cofferdam changes significantly when degradation coefficient decreases to a critical value.Practical implicationsA geotechnical engineer could use the proposed method to perform cofferdam reliability analysis.Originality/valueThe reliability of cofferdam can be efficiently and accurately studied using the proposed framework.

Key messageIntegrating airborne laser scanning and satellite time series data across the forest rotation enhances decision-making in precision forestry. This review supports forest managers by illustrating practical applications of these remote sensing technologies at different stages of intensive forest plantation management-such as site assessment, monitoring, and silviculture-helping improve productivity, sustainability, and operational efficiency.ContextIntensively managed forest plantations depend on high-resolution, timely data to guide silviculture and promote sustainability.AimsThis review explores how airborne laser scanning (ALS) and satellite time series data support precision forestry across key stages, including site assessment, establishment, monitoring, inventory updates, growth tracking, silvicultural interventions, and harvest planning.ResultsThe review highlights several key applications. ALS-derived digital elevation models and canopy metrics improve site productivity estimation by capturing micro-topographic variables and soil formation factors. Combining ALS with multispectral data enhances monitoring of seedling survival and health, although distinguishing seedlings from non-living components remains a challenge. ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. These models also help mitigate drought and wind damage by guiding density and canopy structure management. ALS terrain data further support harvest planning by optimizing machinery routes and reducing environmental impacts.ConclusionDespite progresses, challenges remain in refining predictive models, expanding remote sensing applications, and developing tools that translate complex data into field operations. A major barrier is the technical expertise needed to interpret spatial data and integrate remote sensing into workflows. Continued research is needed to improve accessibility and operational relevance. High-resolution data still offer strong potential for adaptive management and sustainability.

Root-knot nematode (RKN) causes severe yield loss in cucumber. Understanding the interactions of biocontrol agent-soil microbiomes and RKNs is essential for enhancing the efficacy of biocontrol agents and nematicides to curb RKN damage to cucumber. The field experiment in this work was conducted to determine the ability of Bacillus velezensis GHt-q6 to colonize cucumber plants, investigate its effect on the control of RKNs, and assess its influence on soil microbiology in the inter-root zone of cucumber plants. After 10 days post-treatment (DPT), GHt-q6-Rif could stably colonize the roots (4.55 x 10(4) cfu center dot g(-1)), stems (3.60 x 10(3) cfu center dot g(-1)), and leaves (3.60 x 10(2) cfu center dot g(-1)) of cucumber. The high-throughput sequencing results suggested that the bacterial community diversity increased at the late development phase (p > 0.05). The strain GHt-q6 increased the relative abundance of beneficial bacteria (Gemmatimonadaceae, Sphingomonadaceae, Pseudomonadaceae). Throughout the complete cucumber growth period, strain GHt-q6 significantly increased soil urease, sucrase, accessible potassium, and phosphorus (p < 0.05). However, strain GHt-q6 had a minimal effect on catalase activity. At the pulling stage, strain GHt-q6 exhibited 43.35% control effect on cucumber RKNs, which was 7.54% higher than that of Bacillus subtilis. The results highlighted the significant potential of the strain GHt-q6 to manage cucumber RKNs and improve soil microecology. Hence, the applications of B. velezensis GHt-q6 can enhance the nematicidal action to curb RKN infecting cucumber.

Root rot disease is a significant constraint to sweet cherry production in the highlands of southwest China, causing substantial yield losses. While the disease is prevalent, the complex interplay of climate, topography, soil, and management practices on its development remains poorly understood. To address this knowledge gap, a field survey encompassing 95 field sites was conducted to assess disease incidence (DI) and canopy damage index (CDI). Our results showed that the average DI and CDI were 27.04 and 20.52%, respectively. DI and CDI were influenced by management practices: they both increased with the number of planting years and were lower with Cerasus szechuanica rootstock and composted animal manures compared with Da-qingye rootstock and uncomposted animal manures. Climatic and topographic factors also played an important role in observing higher DI at higher altitudes and shady slopes (P < 0.05). Moreover, both DI and CDI demonstrated positive correlations with the aridity index and sunshine duration and negative correlations with mean annual temperature and mean annual precipitation (P < 0.05). Soil properties, including moisture content, bulk density, pH, and sand content, were positively associated with DI and CDI, while clay content and available potassium exhibited negative correlation. The present study emphasizes the combined impact of multiple factors on root rot disease in sweet cherry, with management practices and soil properties having a more decisive effect than climate and topography. These findings provide crucial insights for developing effective disease management strategies.

Most of the robust artificial intelligence (AI)-based constitutive models are developed with synthetic datasets generated from traditional constitutive models. Therefore, they fundamentally rely on the traditional constitutive models rather than laboratory test results. Also, their potential use within geotechnical engineering communities is limited due to the unavailability of datasets along with the model code files. In this study, the data-driven constitutive models are developed using only laboratory test databases and deep learning (DL) techniques. The laboratory database was prepared by conducting cyclic direct simple shear (CDSS) tests on reconstituted sand, that is, PDX sand. The stacked long short-term memory (LSTM) network and its variants are considered for developing the predictive models of the shear strain (gamma [%]) and excess pore pressure ratio (ru) time histories. The suitable input parameters (IPs) are selected based on the physics behind the generation of ru and gamma (%) of the liquefiable sands. The predicted responses of gamma (%) and ru agree well in most cases and are used to predict the dynamic soil properties of the PDX sand. The same modeling framework is extended for other sand and compared with existing AI-based constitutive models to verify its practical applicability. In summary, it is observed that though the trained models predicted the time histories of ru and gamma reasonably well; however, they struggled to predict the hysteresis loops at higher cycles. Therefore, more research is needed to verify and enhance the predictability of existing AI-based models in the future before using them in practice for simulating cyclic response.

Soil moisture generally refers to the amount of water stored between soil particles in the spaces (pores). The moisture content of soil influences its mechanical properties thus resulting in different soil behaviour. Applying a load of a wheel to the surface of soil cause a reduction in soil pore volume (soil deformation) and that depends on the physical composition of the soil, dampness (water content), density, and the initial compression state. Thus, the wheel sinks into the soil to a certain depth until the soil produces a resistance force (load-bearing capacity)equal to that of the wheel load. The amount of load-bearing capacity depends on the moisture content of the soil. In this article, interest will be given to studying the connection between the sandy loam soil moisture content and the load-bearing capacity. At the laboratory of the Hungarian University of Agriculture and Life Sciences (Szent Istvan Campus), the measurements were performed to determine the connection of load-bearing capacity of the sandy loam soil with different moisture content. Was used a Bevameter technique to measure the force, displacement, and moisture analyser to determine the moisture content level ofsoil. The obtained result shows when the moisture content level increases, the sinkage also increases which means the load-bearing capacity of the soil decreases.

In semi-arid areas, light buildings, highways, and pavements are frequently damaged by the subsurface swelling or shrinkage of expansive soils during both wetting and drying cycles. The goal of this research is to explore the X-ray diffraction of natural clay with bentonite additives in order to determine the amount of expanding minerals in the clay based on changes in the diffractometer profile and diffraction intensity. Mineralogical studies are crucial for determining the geotechnical behavior of these soils. Five semi-arid areas were chosen to explore the key minerals that influence geotechnical behavior. The various geological backgrounds were reflected in differing expansivities, and X-ray diffraction revealed considerable mineralogy differences between the five zones under consideration. Non-sharp peaks rose above background intensities in zones containing smectite clay minerals. Significant expanding minerals produced distinct peaks in the clays. Adding 10, 20, 30, and 40% commercial bentonite changed the peak size and area beneath the peak. Overlapping intensities in clay minerals can affect the intensity of peaks in lower 2 theta ranges. This was discovered to influence the method of quantification and can be improved by the usage of heating or glycolation processes. The diffraction profile for each examined area is supplied, along with an identification of expansion minerals. The methodology is provided for estimating clay minerals in areas with similar geological origins. Qatif clays were discovered to be the most expansive with estimated expanded mineral concentrations ranging from 23.9 to 34.7%. The remaining four clays had mineral concentrations ranging from 4.4 to 20%. Two proposed semi-quantitative methods are investigated. The peak intensity method produced better results than the area under the peak method.