Vahid Shafaie
57222634801
Publications - 24
Evaluation of early warning signals for soil erosion using remote sensing indices in northeastern Iran
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Soil erosion represents a major challenge to natural resource conservation, causing land degradation, biodiversity loss, and diminished soil quality. This study explored the use of satellite imagery to evaluate the spatiotemporal risk of soil erosion in northeastern Iran. The ICONA model was applied to identify areas at severe erosion risk, while remote sensing indices (NDVI, NDSI, and TGSI) were employed to analyze erosion trends. NDVI is used to monitor vegetation health, NDSI detects soil salinity levels, and TGSI assesses topsoil grain size distribution, collectively providing critical insights into soil erosion risk in the study area. These indices, derived from the Google Earth Engine with a 30-meter spatial resolution and monthly temporal intervals (2003–2022), were assessed at 100 points, equally divided between eroded and non-eroded regions. Field data, including vegetation plots and soil profiles, were used to validate the remote sensing outputs. Early warning signals were analyzed through three statistical indices—autocorrelation coefficient, skewness, and standard deviation—using Kendall’s tau. Results revealed that 39.7% of the area falls under low erosion risk, 58.4% under medium risk, and 1.9% under severe risk. Significant breakpoints in NDSI and NDVI were identified in 2013, while TGSI showed no detectable change. Major shifts occurred near the Alagol, Almagol, and Ajigol wetlands and northern drylands. This study underscores the importance of integrating satellite data with field validation to improve soil management, protect biodiversity, and guide sustainable erosion mitigation strategies.
Open Access: Yes
Slant shear tests and fuzzy logic integration for evaluating shear bond strength in SCC and FRSCC repair applications
Publication Name: Case Studies in Construction Materials
Publication Date: 2025-07-01
Volume: 22
Issue: Unknown
Page Range: Unknown
Description:
This study examines the interfacial bond characteristics of twenty mix proportions, comprising ten self-compacting concrete (SCC) and ten fiber-reinforced self-compacting concrete (FRSCC) formulations, the latter enhanced with 0.1 % polypropylene fibers for repair applications. Initially, experiments such as slump flow, 28-day compressive strength, and tensile strength tests were conducted to evaluate the mechanical properties of the repair layers intended for use in slant shear tests. The primary focus of the research then shifted to determining shear bond strength (SBS) and calculating interfacial cohesion and friction angles using slant shear tests across various inclination angles on these mix proportions applied over a normal vibrated concrete (NVC) substrate. Notably, FRSCC mixtures with 10 % microsilica exhibited notable enhancements, showing increased cohesion of 8.28 MPa and a tensile strength increase of 24.50 % compared to their SCC counterparts. Additionally, a general trend was observed where FRSCC mixtures demonstrated higher cohesion values compared to SCC, underscoring the effectiveness of fiber reinforcement. Furthermore, the research introduces a novel predictive model employing a fuzzy system with a generalized Mamdani's interference engine and Hamacher family of t-norms to accurately predict the SBS, achieving a predictive accuracy with an R2 value up to 0.94. Employing the fuzzy model, characterized by its high predictive accuracy, can significantly reduce the frequency of experimental tests required in the field, thereby lowering construction testing costs and enhancing repair efficiency. These findings not only advance our understanding of SCC and FRSCC behaviors in repair scenarios but also contribute significantly to the development of more reliable and sustainable construction practices by improving the precision of SBS predictions in theoretical modeling and empirical testing.
Open Access: Yes
Dem-driven investigation and AutoML-Enhanced prediction of Macroscopic behavior in cementitious composites with Variable frictional parameters
Publication Name: Materials and Design
Publication Date: 2025-06-01
Volume: 254
Issue: Unknown
Page Range: Unknown
Description:
This study presents a numerical investigation and predictive modeling framework to evaluate the influence of microscale frictional parameters on the mechanical behavior and failure mechanisms of cementitious composites. In the first phase, discrete element modeling (DEM) was employed to analyze the effects of bonded friction angle and non-bonded friction coefficient on the stress–strain response, failure evolution, and macro-scale properties. The results revealed a distinct transition from tensile to shear-dominated failure modes beyond a critical friction angle, accompanied by notable changes in compressive strength and deformation characteristics. Additionally, the role of non-bonded friction coefficient in post-failure behavior was identified, emphasizing its influence on load-redistribution. In the second phase, an AutoML-driven artificial neural network (ANN) was optimized via grid search, selecting an optimal four-layer model to predict macroparameters from microscale DEM inputs. The proposed ANN demonstrated high predictive accuracy, effectively capturing nonlinear dependencies while significantly reducing the need for additional numerical simulations. This integration of DEM and AI-based predictive modeling provides a computationally efficient, scalable solution for material characterization, enabling faster, data-driven insights into cementitious composite behavior without reliance on extensive simulation campaigns.
Open Access: Yes
Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB’s GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies.
Open Access: Yes
Evaluation of Various Deep Learning Algorithms for Landslide and Sinkhole Detection from UAV Imagery in a Semi-arid Environment
Publication Name: Earth Systems and Environment
Publication Date: 2024-12-01
Volume: 8
Issue: 4
Page Range: 1387-1398
Description:
Sinkholes and landslides occur due to soil collapse in different slope types, often triggered by heavy rainfall, presenting challenges in the semi-arid Golestan province, Iran. This study primarily focuses on the detection of these phenomena. Recent advancements in unmanned aerial vehicle (UAV) image acquisition and the incorporation of deep learning (DL) algorithms have enabled the creation of semi-automated methods for highly detailed soil landform detection across large areas. In this study, we explored the efficacy of six state-of-the-art deep learning segmentation algorithms—DeepLab-v3+, Link-Net, MA-Net, PSP-Net, ResU-Net, and SQ-Net—applied to UAV-derived datasets for mapping landslides and sinkholes. Our most promising outcomes demonstrated the successful mapping of landslides with an F1-Score of 0.95% and sinkholes with an F1-Score of 89% in a challenging environment. ResUNet exhibited an outstanding Precision of 0.97 and Recall of 0.92, culminating in the highest F1-Score of 0.95, indicating the best landslide detection model. MA-Net and SQ-Net resulted in the highest F1-Score for sinkhole detection. Our study underscores the significant impact of DL segmentation algorithm selection on the accuracy of landslide and sinkhole detection tasks. By leveraging DL segmentation algorithms, the accuracy of both landslide and sinkhole detection tasks can be significantly improved, promoting better hazard management and enhancing the safety of the affected areas.
Open Access: Yes
Shear Bond Strength in Stone-Clad Façades: Effect of Polypropylene Fibers, Curing, and Mechanical Anchorage
Publication Name: Polymers
Publication Date: 2024-11-01
Volume: 16
Issue: 21
Page Range: Unknown
Description:
This study investigates the shear bond strength between four widely used façade stones—travertine, granite, marble, and crystalline marble—and concrete substrates, with a particular focus on the role of polypropylene fibers in adhesive mortars. The research evaluates the effects of curing duration, fiber dosage, and mechanical anchorage on bond strength. Results demonstrate that Z-type anchorage provided the highest bond strength, followed by butterfly-type and wire tie systems. Extended curing had a significant impact on bond strength for specimens without anchorage, particularly for travertine. The incorporation of polypropylene fibers at 0.2% volume in adhesive mortar yielded the strongest bond, although lower and higher dosages also positively impacted the bonding. Furthermore, the study introduces a novel fuzzy logic model using the Dombi family of t-norms, which outperformed linear regression in predicting bond strength, achieving an R2 of up to 0.9584. This research emphasizes the importance of optimizing fiber dosage in adhesive mortars. It proposes an advanced predictive model that could enhance the design and safety of stone-clad façades, offering valuable insights for future applications in construction materials.
Open Access: Yes
Detection of sinkholes and landslides using deep-learning methods and UAV images
Publication Name: Watershed Engineering and Management
Publication Date: 2024-09-01
Volume: 16
Issue: 3
Page Range: 316-330
Description:
Introduction Landslides and sinkholes damage social, economic, and natural infrastructure. These processes have direct and indirect impacts on important infrastructure, including residential areas, and influence land use change and migration from rural to urban areas. Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall. One of the main goals in sustainable land management is the identification and control of natural disasters, which on the one hand leads to the quantitative and qualitative improvement of production in the long term, and on the other hand, maintains the quality of the soil and prevents soil degradation. In order to manage better and more stable, it seems necessary to know how to change and identify different forms of erosion such as sinkholes and landslides. Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall. Materials and methods Recent advances in acquiring images from unmanned aerial vehicles (UAV) (UAV) and deep learning (DL) methods inherited from computer vision have made it feasible to propose semi-automated soil landform detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL deep learning segmentation models, the vanilla U-Net model, and the Attention Deep Supervision Multi-Scale U-Net model, applied to UAV-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran). Results and discussion Landslides: The performance of the U-Net model shows that it has fewer false positives, but at the same time, it has missed many landslide cells. Meanwhile, the ADSMS U-Net model has performed better in detecting landslide cells, but it attributed many cases to incorrect predictions (which is explained by the low accuracy score). The best F1 score achieved for the ADSMS U-Net model is 0.68. Sinkholes: For all band combinations, the performances of ADSMS U-Net are better than those of the traditional U-Net model. The best overall scores by ADSMS U-Net were obtained when trained on the ALL data. Regarding the effectiveness of the various combinations evaluated in this study, we can observe the contradictory behaviors of the models. The traditional U-Net achieves the best performance using the RGB optical combination, while the ADSMS U-Net can leverage topographic derivative information and optical data, showing the best results with the ALL combination. Moreover, it is evident that the DSHC data alone provides the worst results for both models. In overall, the results show that the ability of ADSMS U-Net to predict landslides is closer to the ground reality compared to U-Net. This model identifies most of the landslides in the test sections. Also, for all combinations of sinkhole bands, ADSMS U-Net performs better than the U-Net model. The best overall scores were obtained by ADSMS U-Net when trained on ALL data. Conclusions Since this kind of soil erosion is the main origin of some major soil erosion including gully initiation and extension, applying new technology namely, UAV and deep learning is highly important and recommended. Our framework can successfully map landslides in a challenging environment (with an F1-score of 69 %), and topographical derivates from UAV-derived DSM decrease the capacity of mapping sinkholes and landslides of the models calibrated with optical data. Future research could explore the use of such an approach to map landslides and sinkholes over time to assess time-based changes in the formation and spread of natural hazards.
Open Access: Yes
Assessment of soil erosion through spatial analyzing of soil properties using statistical-based functions
Publication Name: Bio Web of Conferences
Publication Date: 2024-08-23
Volume: 125
Issue: Unknown
Page Range: Unknown
Description:
The significant geomorphological hazard of collapsed cavities (CC) causes notable environmental transformations. To address this issue, the pipe collapse pattern was examined using two statistical methods, the Density Correlation Function (DCF) and the Mark Coloration Function (MCF). Key predictor variables like organic carbon (OC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP) were utilized to comprehend their impact on spatial distribution over time. The study was found that lower OC levels increase susceptibility to CC, while higher SAR and ESP amounts enhance the potential for collapsed cavities. The methodology and discoveries of this research can offer valuable insights for land managers, stakeholders, and researchers.
Open Access: Yes
Analysis of early warning signal of land degradation risk based on time series of remote sensing data
Publication Name: Bio Web of Conferences
Publication Date: 2024-08-23
Volume: 125
Issue: Unknown
Page Range: Unknown
Description:
This study explores the spatio-temporal dynamics of the Normalized Difference Vegetation Index (NDVI) to detect early signs of land degradation. Utilizing high-resolution NDVI data from the Google Earth Engine, spanning from 2004 to 2023 with a 30-meter resolution, this research analyzes monthly variations. To illustrate these dynamics, the study focuses on Sabzevar County, located in northeastern Iran, which extends over 7,217 km2and is approximately 220 kilometers distant from Mashhad. Validation of the NDVI data was performed using field observations from strategically located vegetation plots. One square meter plots were systematically established along 100-meter transects (10 transects in total), where the vegetation coverage in each plot was quantitatively assessed by experts. Comprehensive statistical analysis incorporated Kendall's tie test, alongside measurements of autocorrelation, coefficient of variation, and standard deviation, using R software to assess the trends and intensities of NDVI changes. The findings revealed a critical breakpoint in 2020, with increases in all three statistical indices—autocorrelation 0.82, coefficient of variation 0.65, and standard deviation 0.58—indicative of accelerating degradation prior to this year. Furthermore, the intensity of NDVI changes varied significantly across the study area, ranging from 0.05 in central and northern regions to 0.76 in the western parts. This research underscores the value of integrating field data with remote sensing technology to provide a robust analytical tool for early detection of land degradation. This method enables precise, timely assessment and proactive management of vulnerable ecosystems, particularly in arid regions.
Open Access: Yes
Strategic assessment of groundwater potential zones: a hybrid geospatial approach
Publication Name: Applied Water Science
Publication Date: 2024-08-01
Volume: 14
Issue: 8
Page Range: Unknown
Description:
Groundwater aquifers constitute the primary water supply for populations in arid regions, exemplified by the Goharkooh Plain in Iran's driest drainage basin, where conditions of high evapotranspiration and low precipitation prevail. With the escalating demand for water resources, driven mainly by agricultural expansion, the strategic management of groundwater assets has become increasingly critical. This study focuses on delineating groundwater potential zones (GWPZs) through an integrated approach combining multi-criteria decision analysis and geospatial tools. Based on an extensive literature review, nine thematic layers were selected and developed: lithology, geology, drainage density, slope gradient, elevation, vegetation cover, lineament density, land use, and precipitation. These criteria were initially weighted using the analytical hierarchical process (AHP) and subsequently integrated via weighted overlay analysis. In this research, the strategic selection of thematic layers for assessing groundwater potential in arid regions has been identified as an innovative approach that could significantly advance studies in similar settings. The analysis revealed that approximately 60% of the study area, primarily in the southwestern parts, exhibited moderate to very high groundwater potential. This potential is primarily attributed to the presence of alluvial deposits, low drainage density, and favorable slope and elevation conditions. Applying the receiver operating characteristic (ROC) curve yields an area under the curve (AUC) of 81.5%, indicating a relatively high level of predictive accuracy. These findings demonstrate the efficacy of this integrated approach, suggesting its broader applicability in regions with analogous groundwater challenges and management needs.
Open Access: Yes
Numerical Study of the Ultimate Bearing Capacity of Two Adjacent Rough Strip Footings on Granular Soil: Effects of Rotational and Horizontal Constraints of Footings
Publication Name: Buildings
Publication Date: 2024-06-01
Volume: 14
Issue: 6
Page Range: Unknown
Description:
In this paper, the numerical study of the ultimate bearing capacity (UBC) of two closely spaced strip footings on granular soil is investigated using the finite element method (FEM) and upper bound limit analysis (UBLA). Although the UBC of two adjacent footings has previously been studied in other experimental and numerical research, in all the previously reported studies, the footings were not allowed to rotate and move horizontally freely. Due to the deformation of the soil medium, two closely spaced footings are subjected to horizontal movements and tilting, even under central vertical loads. When the two adjacent footings are not permitted to rotate and move in the horizontal directions, the unwanted bending moment and horizontal force act on the footings. Indeed, the UBC of two closely spaced rough footings is evaluated under incorrect constraints in earlier research. In the present research, the UBC of two adjacent rough footings is evaluated with and without these incorrect constraints. The key finding of this study is that constraining the horizontal and rotational movement of the foundation artificially increases the UBC, which does not reflect field conditions. When foundations are permitted to rotate and move horizontally, there is no increase in UBC; however, there is an increased risk of differential settlement and structural instability.
Open Access: Yes
Integrating push-out test validation and fuzzy logic for bond strength study of fiber-reinforced self-compacting concrete
Publication Name: Construction and Building Materials
Publication Date: 2024-04-26
Volume: 425
Issue: Unknown
Page Range: Unknown
Description:
This study offers a comprehensive analysis of Fiber-Reinforced Self-Compacting Concrete (FRSCC) with a focus on shear bond strength influenced by specific compositions of microsilica, zeolite, slag, and polypropylene fibers. Twenty distinct FRSCC mixes underwent extensive testing, including 28-day compressive strength, tensile strength assessments, and push-out and slant shear tests. A significant outcome is the strong correlation between the push-out and slant shear test results, exemplified by an R² value of 0.88, confirming the push-out test as a viable and practical alternative for bond strength assessment. Experimentally, fibers were found to enhance tensile strength, with the inclusion of 15% microsilica and slag further amplifying this effect, highlighting the critical role of precise pozzolan selection in achieving optimal mechanical performance and workability in FRSCC. Furthermore, the study introduces a fuzzy logic system for predicting shear bond strength, achieving high predictive accuracy with R² values reaching up to 0.96, depending on the t-norms utilized. This research not only validates the push-out test as a reliable method for evaluating shear bond strength in FRSCC but also demonstrates the efficacy of the fuzzy logic approach, representing a groundbreaking contribution in both computational analysis and practical methodology for concrete structural integrity.
Open Access: Yes
Assessing Future Hydrological Variability in a Semi-Arid Mediterranean Basin: Soil and Water Assessment Tool Model Projections under Shared Socioeconomic Pathways Climate Scenarios
Publication Name: Water Switzerland
Publication Date: 2024-03-01
Volume: 16
Issue: 6
Page Range: Unknown
Description:
Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability.
Open Access: Yes
DOI: 10.3390/w16060805
Soil erosion monitoring using the perpendicular soil moisture index as a remote sensing index (case study: Salehiya Wetland, Iran)
Publication Name: Advanced Tools for Studying Soil Erosion Processes Erosion Modelling Soil Redistribution Rates Advanced Analysis and Artificial Intelligence
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: 527-542
Description:
Continuous monitoring of soil erosion is necessary but challenging, especially in wetland ecosystems like Salehiya where water stress causes soil erosion and poses a threat to the environment of Tehran and Karaj. This study aims to use the perpendicular soil moisture index (PSMI) as a remote sensing index to determine the trend and zoning of soil erosion potential in Salehiya wetland. Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data from 2003 to 2017 were used to estimate adjusted soil vegetation index (SAVI), land surface temperature (LST), and finally PSMI, where higher PSMI values indicate lower soil moisture. Kendall's seasonal time series test results showed a significant increase in PSMI (with tau correlation coefficient τ=0.25) and significant decreasing trends in hydrometric time series data (with the values of the τ −0.36 and −0.27, respectively) from stations located on Kharroud and Haji Arab rivers leading to Salehiya wetland, which indicates the continuation of the water stress in the region. The zoning of soil erosion indicated that human interventions have accelerated soil erosion in addition to water stress.
Open Access: Yes
Fuzzy Logic and Push-Out Test Innovations for Fiber-Reinforced Self-compacting Concrete Assessment
Publication Name: Fib Symposium
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: 855-862
Description:
This research addresses the deterioration of concrete infrastructures, emphasizing the efficacy of Fiber-Reinforced Self-Compacting Concrete (FRSCC) in repair applications. The study investigates the bond strengths between new and existing concrete layers, employing both experimental and numerical methods to evaluate traditional and innovative testing approaches, including slant shear and push-out tests. Results demonstrate that FRSCC, enhanced with polypropylene fibers, significantly improves structural resilience and mechanical properties. The introduction of fuzzy logic models further refines the prediction of bond strengths, offering a robust framework for future concrete technology advancements.
Open Access: Yes
DOI: DOI not available
Land subsidence modeling and mapping in Darab region, Iran
Publication Name: Advanced Tools for Studying Soil Erosion Processes Erosion Modelling Soil Redistribution Rates Advanced Analysis and Artificial Intelligence
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: 275-294
Description:
Land subsidence refers to the collapse of Earth's surface. This study aimed to model land subsidence using machine learning methods in the Darab region of Fars Province, which is recognized as one of the most critical provinces suffering from land subsidence in the country. Nineteen factors affecting the occurrence of land subsidence were selected as independent variables for the modeling process: slope degree, aspect, distance to rivers, stream density, elevation, land use, normalized difference vegetation index (NDVI), plan curvature, profile curvature, topographic wetness index, pH, electrical conductivity, mean annual rainfall, mean weight diameter (MWD), clay, silt, calcium carbonate equivalent (CCE), sodium content, and organic matter. Modeling was conducted using: artificial neural network (ANN), maximum entropy (MaxEnt), and support vector machine (SVM). The performance of algorithms was compared both individually and in combination. Validation results using the receiver operating characteristic (ROC) curve to identify landslide prone areas showed that land subsidence susceptibility maps produced by single MaxEnt model had highest accuracy, with area under the curve (AUC) of 0.92. According to the prioritization of effective factors, elevation and land use were determined to be the most crucial factors for land subsidence. The results of this spatial modeling of land subsidence susceptibility can greatly aid land allocation planning and water resource management in the study area.
Open Access: Yes
Numerical Study of the Geogrid Reinforced Soil Wall Incorporating Strain-Softening Constitutive Soil Model
Publication Name: Advances in Transdisciplinary Engineering
Publication Date: 2024-01-01
Volume: 59
Issue: Unknown
Page Range: 327-333
Description:
This study embarks on a numerical exploration of Geogrid Reinforced Soil Walls (GRSW), employing finite difference analysis to compare two soil constitutive models, highlighting the efficacy of a refined strain-softening model. This innovative approach markedly improves the prediction of GRSW performance, particularly aligning the safety factor more closely with real-world observations. Notably, the strain-softening model demonstrates a superior ability over the perfectly plastic model by significantly reducing the mean overall error in predicting maximum geogrid strain overall from 51% to 30%, reflecting a significant 41% improvement in precision, thereby presenting a significant tool for enhancing geotechnical design practices. The research underlines the potential of this model to elevate the safety and reliability of GRSW constructions, contributing to elevated design standards within the field of geotechnical engineering.
Open Access: Yes
DOI: 10.3233/ATDE240563
Susceptibility mapping for land subsidence and collapsed pipes in north-east Iran
Publication Name: Advanced Tools for Studying Soil Erosion Processes Erosion Modelling Soil Redistribution Rates Advanced Analysis and Artificial Intelligence
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: 579-594
Description:
Land subsidence and collapsed pipes are considered among geomorphological hazards, causing significant damages annually in the form of direct and indirect costs. These hazards lead to notable changes in the landscape, land degradation, soil and water losses, and regional erosion and sedimentation. Consequently, the effective management of these hazards and the determination of relationships between their environmental factors for quantitative susceptibility assessment are of utmost importance. A trustworthy evaluation depends on the quality of available data and the selection of appropriate analytical and modeling methods. Given that no comprehensive study on land subsidence and collapsed pipes in Razavi Khorasan Province has been conducted so far and considering that the land degradation resulting from plain land subsidence and collapsed pipes are among the primary threatening hazards for the country and the province, particularly in recent years, the use of proper analytical and modeling methods for comprehensive and integrated management seems essential. This research was conducted using high-resolution satellite imagery in Razavi Khorasan Province. In this study, topographical and hydrological feature maps were prepared using a digital elevation model based on the boundaries of Razavi Khorasan Province. Physical and chemical tests were conducted on 624 soil samples collected throughout the province, and their raster maps were produced. Data pertaining to vegetation cover, land use maps, geology, and regional precipitation were also prepared and used as inputs for the models. The spatial locations of land subsidence and collapsed pipes across the province were identified in subsequent phases. Following this, using statistical and data mining methods, spatial modeling of the land subsidence and collapsed pipes was performed, and the best regional model for their evaluation was chosen. The AUC numerical value for both the support vector machine (SVM) and maximum entropy (ME) models ranges between 0.8 and 0.9, indicating an excellent evaluation of the models used in zoning the land subsidence. Ultimately, the ability to recognize the behavior and formation conditions of these hazards, to identify areas with greater susceptibility, to present a risk management model for land subsidence and collapsed pipes, and to distinguish critical and susceptible areas for land subsidence and collapsed pipes, along with their control methods, was provided. Notably, the SVM algorithm demonstrated superior efficacy in this study. The insights derived from identifying erosional structures of collapsed pipes and land subsidence and understanding their spatial interrelationships offer a robust foundation for devising timely and strategic management interventions in affected domains.
Open Access: Yes
Soil pipe pattern dynamics and illustration of the erosional landforms from a geomorphological perspective
Publication Name: Advanced Tools for Studying Soil Erosion Processes Erosion Modelling Soil Redistribution Rates Advanced Analysis and Artificial Intelligence
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: 59-72
Description:
The primary objective of this research was to analyze the spatial pattern and interactions of dendritic rill channels and surface/subsurface channels (pipes)/collapsed pipes. To achieve this, photogrammetry drones were employed on over 70ha in the protected region of Takhtsoltan in Razavi Khorasan Province. This approach pinpointed areas impacted by erosions from subsurface channels (pipes)/collapsed pipes and dendritic rill channels and surfaces. Subsequent field visits documented 76 instances of subsurface channels (pipes)/collapsed pipes and 58 dendritic rill channels and surfaces, using orthophotos derived from the aerial drone images. In the analytical phase, topographical, hydrological, soil, and biological factors were examined as independent variables, whereas the types of erosion from subsurface channels (pipes)/collapsed pipes and dendritic rill channels were viewed as dependent variables. Spatial patterns of the subsurface channels (pipes)/collapsed pipes and dendritic rill channels were discerned using univariate functions. Further, bivariate functions were deployed to probe the interrelations, revealing that the distribution pattern of the study area's subsurface channels (pipes)/collapsed pipes is predominantly clustered, in contrast to the dispersed spatial pattern of the dendritic rill channels. This analysis also confirmed significant positive correlations between dendritic rill channel erosions and subsurface channels (pipes)/collapsed pipes. Ultimately, by identifying the erosional landforms of subsurface channels (pipes)/collapsed pipes and dendritic rill channels and understanding the spatial and processual relationships between them, a deeper insight into the natural processes inherent in their spatial structure was achieved, paving the way for devising suitable strategies for their timely management.
Open Access: Yes
Non-Linear Time History and Pushover analysis of a Steel Silo Behavior
Publication Name: Advances in Transdisciplinary Engineering
Publication Date: 2024-01-01
Volume: 59
Issue: Unknown
Page Range: 334-341
Description:
Earthquakes, among the most destructive natural hazards, result in substantial economic and demographic losses. An effective strategy to mitigate future structural damage involves investigating past collapses. Numerical modeling proves instrumental in analyzing and identifying deficiencies in collapsed structures. This study numerically evaluates a steel silo damaged during the 2011 Van earthquake. Employing non-linear time history and pushover analyses, the research assesses the silo's performance. Findings highlight inadequate welding dimensions and incomplete fusion with the base metal in fillet welds between columns and the silo tank as primary causes of collapse. Numerical simulations with varied column removal scenarios underscore the importance of robust silo tank-column connections in reducing earthquake-induced damage.
Open Access: Yes
DOI: 10.3233/ATDE240564
Review of multihazards research with the basis of soil erosion
Publication Name: Advanced Tools for Studying Soil Erosion Processes Erosion Modelling Soil Redistribution Rates Advanced Analysis and Artificial Intelligence
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: 295-306
Description:
Soil erosion is a primary geomorphic process that may result in hazards and significant socioeconomic losses. These processes occur mainly through the surface and subsurface flows. We conducted a systematic literature review on the quantitative attribution analysis of soil erosion, presenting state-of-the-art erosion processes and demonstrating the relative importance of soil erosion as a natural hazard responsible for land degradation and desertification. This explains why a multidisciplinary approach is needed to understand how erosion occurs and what factors are involved. This justifies the multihazard analysis and the need to model the erosion processes. Knowledge of the quantitative elements of soil erosion measurement combined with the consideration of multiple risk assessments can help develop conceptual models of slope hydrology and soil erosion that can help decision-makers determine an appropriate early warning system design policy. Filling these gaps will guide us to increase our knowledge of surface and subsurface erosion, thereby helping us to better explore the changing landscape for improvement and develop strategies and effective soil erosion control techniques. However, more research is required to better explore the morphology and connectivity of soil erosion, their subsurface watershed, and behavior, as well as several challenges, opportunities, and strategies facing the analysis of soil erosion.
Open Access: Yes
Study of Bonding between Façade Stones and Substrates with and without Anchorage Using Shear-Splitting Test—Case Study: Travertine, Granite, and Marble
Publication Name: Buildings
Publication Date: 2023-05-01
Volume: 13
Issue: 5
Page Range: Unknown
Description:
This paper presents an investigation into the bond strength of three common façade stones, namely, travertine, granite, and marble, to a concrete substrate using a shear-splitting test. The effects of anchorage, the number of curing days, and the presence of an anti-freezing agent in cement–sand mortar on bond strength were studied. The results show that the number of curing days had a significant impact on the bond strength between the stones and the substrates. The presence of an anti-freezing agent and accelerator increased bonding during the initial days, but this effect gradually decreased. The use of anchorage had a positive effect on the bond strength, particularly with fewer curing days. Granite had the lowest bond strength when no anchorage was used due to its low permeability. Based on the findings, a novel fuzzy logic approach was proposed to predict the bond strength. This study provides valuable insights into improving the bonding of façade stones to substrates and can aid in the safe and efficient use of these materials in construction.
Open Access: Yes
Interfacial bond strength of coloured SCC repair layers: an experimental and optimisation study
Publication Name: Journal of Structural Integrity and Maintenance
Publication Date: 2023-01-01
Volume: 8
Issue: 3
Page Range: 140-149
Description:
This study investigates experimentally and analytically the interfacial bond strength of coloured SCC repair layers. Ten SCC mixes with 5%, 10% and 15% of blue, green or red pigments were produced to examine their fresh properties. Subsequently, 60 coloured SCC specimens were tested to assess interfacial bond strength using pull-off and push-out tests. The results confirm that pigments reduce the mechanical properties of SCC and its bond strength to concrete substrates, with red pigment reducing (by up to 41%) interfacial bond strength. It is shown that the push-out test is effective to determine the interfacial shear bond strength between the SCC repair layers and substrates. A GNNC-Modified PSO algorithm is proposed to calculate accurately (R2 = 0.95) the interfacial bond strength of coloured SCC repair layers. This study contributes towards developing more effective test methods and more accurate models to calculate interfacial bond strength of the SCC repair layers used in this study.
Open Access: Yes
Assessment of soil erosion patterns in Maharloo watershed using remote sensing techniques and early warning signals
Publication Name: Journal of Arid Environments
Publication Date: 2026-02-01
Volume: 232
Issue: Unknown
Page Range: Unknown
Description:
This study assessed the soil erosion dynamics in Iran's Maharloo watershed using remote sensing indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI), and Topsoil Grain Size Index (TGSI)) and machine learning models (RF, SVM, and BRT). Landsat 8 satellite images (2005–2024) were processed via the Google Earth Engine, with field validation ensuring accuracy. Among the indices, TGSI (R2 = 0.86), NDSI (R2 = 0.89), and NDVI (R2 = 0.87) showed the strongest correlations with ground data (Rain, Soil and Vegetation). The RF outperformed the other models (AUC = 0.89), identifying the central and western regions as warning erosion zones. Breakpoint analysis revealed abrupt changes in NDVI and NDSI (2013), while early warning signals (autocorrelation, variance, and skewness) indicated an escalating erosion warning, particularly near wetlands and rainfed fields. Spatial trends highlighted significant NDVI declines (Kendall's τ = 0.69) in wetland peripheries and NDSI increased (τ = 0.52) in northern farmlands. These findings underscore the efficacy of integrating machine learning and remote sensing for erosion monitoring, providing actionable insights for land management and conservation strategies.
Open Access: Yes
