Search Results (36)

This study aims to investigate the processes associated with mass movements and their relationship with the behavior of the Amazon River delta-estuary (ADE) wetlands. The methodological approach involves using water spectral indices and ground-penetrating radar (GPR) to diagnose areas of soil water saturation and characterize regions affected by mass movements in Amazonian cities. It also involves identifying areas of critical saturation content and consequent mass movements. Analysis of risk and land use data revealed that the affected areas coincide with zones of high susceptibility to mass movements induced by water. The results showed the following: the accumulated annual precipitation ranged from 70.07 ± 55.35 mm·month⁻¹ to 413.34 ± 127.51 mm·month⁻¹; the response similarity across different sensors obtained an accuracy greater than 90% for NDWI, MNDWI, and AWEI for the same targets; and a landfill layer with a thickness variation between 1 and 2 m defined the mass movement concentration in Abaetetuba city. The interaction between infiltration, water saturation, and human-induced land alteration suggests that these areas act as wetlands with unstable dynamics. The analysis methodology developed for this study aimed to address this scenario by systematically mapping areas with mass movement potential and high-water saturation. Due to the absence of geological and geotechnical data, remote sensing was employed as an alternative, and in situ ground-penetrating radar (GPR) evaluation was suggested as a means of investigating the causes of a previously observed movement. Full article

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

This study investigates the impact of nonlinear constitutive models on one-dimensional seismic site response analysis (SRA) for soft, reclaimed soil deposits in Saemangeum, South Korea. Two widely used models, MKZ and GQ/H, were applied to three representative soil profiles using the DEEPSOIL program. Ground motions were scaled to bedrock peak ground accelerations (PGAs) corresponding to annual return periods (ARPs) of 1000, 2400, and 4800 years. Seismic response metrics include the ratio of GQ/H to MKZ shear strain, effective PGA (EPGA), and short- and long-term amplification factors (F_a and F_v). The results highlight the critical role of the site-to-motion period ratio (T_g/T_m) in controlling seismic behavior. Compared to the MKZ, the GQ/H model, which features strength correction and improved stiffness retention, predicts lower shear strains and higher surface spectral accelerations, particularly under strong shaking and shallow conditions. Model differences are most pronounced at low T_g/T_m values, where MKZ tends to underestimate amplification and overestimate strain due to its limited ability to reflect site-specific shear strength. Relative to code-based amplification factors, the GQ/H model yields lower short-term estimates, reflecting the disparity between stiff inland reference sites and the soft reclaimed conditions at Saemangeum. These findings emphasize the need for strength-calibrated constitutive models to improve the accuracy of site-specific seismic hazard assessments. Full article

This paper presents a comprehensive on-site decision-making framework for assessing the structural integrity of a jacket-type offshore platform in the Gulf of Mexico, installed at a water depth of 50 m. Six critical analyses—(i) static operation and storm, (ii) dynamic storm, (iii) strength-level seismic, (iv) seismic ductility (pushover), (v) maximum wave resistance (pushover), and (vi) spectral fatigue—are performed using SACS V16 software to capture both linear and nonlinear interactions among the soil, piles, and superstructure. The environmental conditions include multi-directional wind, waves, currents, and seismic loads. In the static linear analyses (i, ii, and iii), the overall results confirm that the unity checks (UCs) for structural members, tubular joints, and piles remain below allowable thresholds (UC < 1.0), thus meeting API RP 2A-WSD, AISC, IMCA, and Pemex P.2.0130.01-2015 standards for different load demands. However, these three analyses also show hydrostatic collapse due to water pressure on submerged elements, which is mitigated by installing stiffening rings in the tubular components. The dynamic analyses (ii and iii) reveal how generalized mass and mass participation factors influence structural behavior by generating various vibration modes with different periods. They also include a load comparison under different damping values, selecting the most unfavorable scenario. The nonlinear analyses (iv and v) provide collapse factors (Cr = 8.53 and RSR = 2.68) that exceed the minimum requirements; these analyses pinpoint the onset of plasticization in specific elements, identify their collapse mechanism, and illustrate corresponding load–displacement curves. Finally, spectral fatigue assessments indicate that most tubular joints meet or exceed their design life, except for one joint (node 370). This joint’s service life extends from 9.3 years to 27.0 years by applying a burr grinding weld-profiling technique, making it compliant with the fatigue criteria. By systematically combining linear, nonlinear, and fatigue-based analyses, the proposed framework enables robust multi-hazard verification of marine platforms. It provides operators and engineers with clear strategies for reinforcing existing structures and guiding future developments to ensure safe long-term performance. Full article

In geotechnical earthquake engineering, enhancing the seismic properties of foundation soil to modify the characteristics of earthquake waves transmitted to structures presents a viable solution. This study investigates the effect of placing an isolation layer, composed of a mixture of recycled tire rubber and sand, beneath structures to mitigate seismic forces acting on buildings situated on soil layers with high amplification potential. In other words, the role of a soil layer functioning as a seismic isolator is examined. To achieve this objective, the seismic behavior of building-type structures is analyzed through numerical simulations, supplemented by laboratory experiments available in the literature. The numerical analyses are performed in the frequency domain using the finite element method within a one-dimensional (1D) framework. To validate the feasibility of the proposed isolation layer based on parametric analysis results, comparisons are made with laboratory tests available. In the literature, seismic isolation applications with thicknesses ranging from 1 to 3 m resulted in reductions of 6.8% to 16.17% in response spectral accelerations measured at the surface, while improvements in Fourier amplitude ratios ranged between 12.03% and 13.98%. This approach aims to provide an economical and efficient solution for earthquake-resistant structures while simultaneously promoting sustainability by recycling waste tires, contributing both to environmental conservation and economic benefits. Full article

Emerging contaminants (ECs), encompassing pharmaceuticals, personal care products, pesticides, and industrial chemicals, represent a growing threat to ecosystems and human health due to their persistence, bioaccumulation potential, and often-unknown toxicological profiles. Addressing these challenges necessitates advanced analytical tools capable of detecting and quantifying trace levels of ECs in complex environmental matrices. This review highlights the pivotal role of mass spectrometry (MS) in monitoring ECs, emphasizing its high sensitivity, specificity, and versatility across various techniques such as Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and High-Resolution Mass Spectrometry (HR-MS). The application of MS has facilitated the real-time detection of volatile organic compounds, the comprehensive non-targeted screening of unknown contaminants, and accurate quantification in diverse matrices including water, soil, and air. Despite its effectiveness, challenges such as matrix interferences, a lack of standardized methodologies, and limited spectral libraries persist. However, recent advancements, including hybrid MS systems and the integration of artificial intelligence (AI), are paving the way for more efficient environmental monitoring and predictive modeling of contaminant behavior. Continued innovation in MS technologies and collaborative efforts are essential to overcome existing challenges and ensure sustainable solutions for mitigating the risks associated with emerging contaminants. Full article

This study investigates the influence of the soil–structure interaction (SSI) on the seismic performance of structures, focusing on the effects of foundation size, soil type, and superstructure height. While the importance of SSI is well recognized, its impact on structural behavior under seismic loads remains uncertain, particularly in terms of whether it reduces or amplifies structural demands. A simplified dynamic model, incorporating both the mechanical behavior of the soil and structural responses, is developed and validated to analyze these effects. Using a discrete element approach and the 1940 El Centro earthquake for validation, the study quantitatively compares the response of soil-interacting structures to those with fixed bases. The numerical results show that larger foundation blocks (20 m × 20 m and 30 m × 30 m) increase the seismic response values across all soil types, causing the structure to behave more like a fixed-base system. In contrast, reducing the foundation size to 10 m × 10 m increases the flexibility of structures, particularly buildings built on soft soils, which affects the displacement and acceleration response spectra. Softer soils also increase natural vibration periods and extend the plateau region in regard to spectral acceleration. This study further finds that foundation thickness has a minimal impact on spectral displacement, but structures on soft soils show more than a 15% reduction in spectral displacement (SD) compared to those on hard soils, indicating a dampening effect. Additionally, increasing the building height from 7 to 21 m results in a more than 20% decrease in SD for superstructures with natural vibration periods exceeding 2.4 s, while taller buildings with longer natural vibration periods exhibit opposite trends. Structures built on soft soils experience larger foundation-level displacements, absorbing more seismic energy and reducing earthquake accelerations, which mitigates structural damage. These results highlight the importance of considering SSI effects in seismic design scenarios to achieve more accurate performance predictions. Full article

Bioengineering techniques are being increasingly adopted as a sustainable solution to soil slope instability. Despite their recognized benefits, however, the mechanistic contribution of vegetation to slope stability remains inadequately understood due to the intricate nature of soil–root interactions and the complexity of root architectures. Most existing research predominantly offers qualitative assessments of vegetation effectiveness. This study aims to numerically substantiate the role of vegetation as a bioengineering technique for soil slope stabilization. Various plant species used commonly for soil stabilization were identified, and undisturbed soil samples were collected to quantify the shear strength parameters of the soils from both barren and vegetated slopes, along with root tensile strengths. A comprehensive topographic survey was conducted to capture the precise topography of the study area. Utilizing these primary data, in this work we develop 3D models for five representative plants within each species category and employ the Spectral Element Method (SEM), an advanced higher-order formulation of the finite element method (FEM), within the 3D domain to evaluate the factor of safety for the soil slopes. The SEM offers superior accuracy and stability in numerical computations. The results obtained through the SEM were corroborated through the FEM modeling and were found to be consistent with other established methodologies. This innovative approach of 3D SEM aims to quantitatively assess the impact of vegetation on soil slope stability and provide a more rigorous understanding of bioengineering applications in geotechnical engineering. Full article

In planetary science, visible (Vis) and near-infrared (NIR) reflectance spectra allow deciphering the chemical/mineralogical composition of celestial bodies’ surfaces by comparison between remotely acquired data and laboratory references. This paper presents the design of an automated test rig named Exoland Simulator equipped with two reflectance spectrometers covering the 0.38–2.2 µm range. It is designed to collect data of natural/synthetic rocks and minerals prepared in the laboratory that simulate the composition of planetary surfaces. The structure of the test rig is conceived as a Cartesian robot to automatize the acquisition. The test rig is also tested by simulating some project trajectories, and results are presented in terms of its ability to reproduce the programmed trajectories. Furthermore, preliminary spectral data are shown to demonstrate how the soil analogs’ spectra could allow an accurate remote identification of materials, enabling the creation of libraries to study the effect of multiple chemical–physical component variations on individual spectral bands. Despite the primary scope of Exoland, it can be advantageously used also for tribological purposes, to correlate the wear behavior of soils and materials with their composition by also analyzing the wear scars. Full article

Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time. Full article

Seismic risk management in urban areas requires accurate prediction of the expected seismic hazard. The seismic design standards in the world provide the seismic design spectra (DS). These are crucial for estimating seismic forces on a structure, are typically derived from theoretical models in deterministic or probabilistic seismic hazard studies, especially for bedrock soils. Characterizing soil dynamic amplification frequencies or periods is necessary to establish site-specific design spectra (DS_site). Geotechnical and geophysical studies, along with environmental vibration records, determine soil stratigraphy characteristic features and their dominant frequency or period. These parameters improve our understanding of seismic wave behavior from bedrock to surface soil during earthquakes. This article details the utilization of geotechnical, geophysical studies, and environmental vibration records to estimate DS_site in accordance with Mexican seismic regulation and examines the cost–benefit aspects of using the Ds_site in optimizing the structural design of a medium-rise steel building in southeastern Mexico, characterized by soft soil and a moderate seismic hazard. The case study demonstrates an 18% cost savings in the structural elements of the building by employing the DS_site with more rational spectral ordinates for study site. Full article

In an effort to improve the efficiency of soil classification, traditional methods are being combined with analytical and computational techniques. This integration has strengthened the connection between conventional classification and the application of machine-learning (ML) models to interpret soil spectral reflectance data. Due to the time and computational cost, several studies are limited to testing the classification performance of a few algorithms and do not always explore the best parameters for model optimization. The study aims to assess the efficiency of combining soil spectral reflectance with prevalent ML models for classifying pedogenetic horizons and soil suborders, enhancing traditional classification methods. We collected seven soil monoliths, previously classified according to the Brazilian Soil Classification System (SiBCS) and soil taxonomy. Using the ASD Fieldspec spectroradiometer, we obtained spectral reflectance samples along each monolith (n = 800 per monolith) to classify horizons and n = 5600 for suborder classification. Spectral fingerprints were obtained and explored by Principal Component Analysis (PCA). The spectral data were subdivided into training (70%) and test (30%) sets and submitted to the Logistic Regression (LR), Artificial Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models for the classification of horizons and suborders, considering the model’s adjustment parameters. Accuracy and F-Score were used to verify the performance of the models. There was a significant influence of particle size and soil organic carbon on the spectral fingerprint of the soils. The PCA indicated that topsoil horizons clustered in most of the monoliths analyzed, while most of the subsoil horizons showed data overlap. The NN model showed the highest accuracy in the classification of horizons (97%), while the SVM showed the lowest performance (52% accuracy). The classification of soil suborders presented accuracies between 95% and 98%. Therefore, our study concludes that spectral data combined with ML models can enhance the discrimination and classification of soil horizons and suborders, improving upon traditional methods. Full article

Tropical climate conditions favor landscape evolution and the formation of highly weathered soils under different pedogenic processes due to certain differential properties. Traditional analysis coupled with VNIR-SWIR reflectance spectroscopy and X-ray diffractometry (XRD) analyses can reveal such characteristics. Several researchers cited throughout this study already discussed the possible applications of analyses in this field. All agree that integrated knowledge (holistic) can drive the future of the soil sciences. However, few refer to the potential of soil spectroscopy in deriving pedogenetic information. Thus, this paper aimed to assess pedomorphogeological relationships in a representative toposequence of the Brazilian Midwest using traditional analyses and geotechnologies. We performed landscape observations and soil sampling in the field. The laboratory’s physical, chemical, spectral, and mineralogical determinations supported the soil classification according to the World Reference Basis (WRB/FAO) system. Based on the analysis results, we divided five profiles into two soil groups (highly and slightly weathered soils) using Pearson’s correlation and hierarchical clustering analysis (HCA). Traditional analyses determined the diagnostic attributes. Spectroscopic readings from 0.35 to 2.5 µm wavelengths and XRD supported identifying soil attributes and properties. Finally, all soil classes were correlated according to correspondent reflectance spectra and primary pedological attributes. There was a strong correlation between spectral oxide features and X-ray diffraction peaks. The HCA based on oxide content and mineral composition validated the previous soil grouping. Thus, we could assess the pedomorphogeological relationships through VNIR-SWIR spectroscopy, XRD, and traditional analyses concerning pedogenic processes through their correlation with soil properties resulting from these processes. However, periodic measurements are required, making orbital sensing a continuous data source for soil monitoring. Full article

Granulometric aggregate fractions (20 µm–2 mm) of chernozem soils with different agriculture-use histories (native steppe, permanent bare fallow, arable land, and shelterbelt) were investigated in mid-IR and far IR regions (4000–100 cm⁻¹) by heating in the air from 25 to 215 °C, using ATR FTIR and linear discriminant analysis to differentiate the land-use samples without chemical perturbation. The temperature dependences of the band maxima significantly differed for bands of silicate matrix and bands with the contribution of soil organic matter and were more stable to experimental conditions compared to the absolute positions. The thermal behavior of the integral intensities of the IR bands at 790–750 cm⁻¹ and 440–420 cm⁻¹ that was different compared to pure quartz, may be attributed to –C–H bending of alkanes and (poly)aromatic structures and skeletal bending, and could be used to distinguish intact soils from agriculturally used samples. The different temperature behaviors of the bands for fractions of soils with different land use are shown, with the maximum difference in fractions below 20–50 µm and medium fractions (50–200 µm). Changes in the band-maximum frequencies and the integral intensities of the bands were reversible for a heating–cooling cycle. The linear discriminant analysis of the spectra obtained for granulometric fractions of chernozem soils made it possible to separate the samples of native steppe, arable land, bare fallow, and shelterbelt with a high probability based on the type of vegetation and agrogenic load, mainly on the basis of the spectral ranges associated with biogenic forms of quartz and phytoliths. Full article