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An Innovative Design of Strip and Circular Footings on Sand Surface: Stress–Density Framework
No full textThe bearing capacity of shallow foundations subjected to vertical centric loads has been extensively investigated. Despite the variability in the bearing capacity factor Nγ as proposed by different methodologies, the classical solution remains dominant in design codes. Critical variables affecting the bearing capacity of sand encompass sand particle morphology, footing width or diameter (B or D), mean effective stress level (p'), and sand relative density (Dr). Different sand types may exhibit distinct mobilization friction angles (ϕm) at the same Dr and p', resulting in varied stress-strain behaviors. Thus, the actual bearing capacity may not be accurately reflected by estimates of Ngamma derived from a constant peak friction angle (ϕp) value. In this study, a Three-Dimensional Finite Element Model (3D-FEM) has been applied to both strip and circular footings, employing a hypoplastic constitutive sand model to replicate sand behavior. The model efficiently replicates the compression and shear behavior of sand across a wide range of confining pressures and densities. A comprehensive parametric analysis has been conducted, encompassing a broad range of parameter variations. The principal objective is to present an innovative design approach concerning the bearing capacity of footings for diverse sand characteristics across an extensive array of sand properties. Additionally, a correlation has been established between the bearing capacity factors for strip and circular footings.
Doi: 10.28991/CEJ-2025-011-03-03
Full Text: PD
Impact of Wind Turbine Distraction on Crash Severity: Assessment and Prediction Study
No full textWind turbines are increasingly installed near highways, yet their potential role as external distractions impacting traffic crashes remains underexplored. This study investigates the effect of wind turbines on crash severity and frequency along Jordan's King's Highway, analyzing data through a mixed-effect logit model and machine learning techniques. Key factors, including driver demographics, road geometry, and environmental conditions, were incorporated to provide a comprehensive analysis. The findings indicate a 117.4% increase in severe injury crashes (KAB) and a 25.7% rise in property damage only near wind turbines. Using MK models such as Bagged Tree classifiers and SMOT-balanced datasets, the study achieved a high prediction accuracy of 89.6% for crash severity. Shapley value analysis identified crash type and wind turbine proximity as critical predictors, while other influential factors included younger drivers, poorly separated roads, and higher speed limits. By integrating statistical and ML approaches, this research provides actionable insights into the relationship between wind turbines and road safety. The results underscore the need for regulatory policies to optimize wind turbine placement and reduce their potential as driver distractions. This study also demonstrated the potential of ML techniques to enhance traffic safety analysis, paving the way for future research to address multi-class crash severity predictions and other external roadside distractions. Doi: 10.28991/CEJ-2025-011-04-018 Full Text: PD
Performance of Auto Glass Powder-High Calcium Fly Ash Geopolymer Mortar Exposed to High Temperature
No full textWaste glass enhances concrete sustainability by reducing virgin material use and recycling waste. In traditional concrete, it boosts strength through pozzolanic reactions, while in geopolymer concrete, it improves durability, insulation, and resistance to harsh conditions. This study investigated the viability of substituting auto glass powder (AGP) for high-calcium fly ash (FA) in geopolymer mortar formulations. AGP was utilized as a substitute for high-calcium FA at substitution levels ranging from 0% to 40% by weight. The study examined the physical properties, compressive strength, thermal insulation, and high-temperature performance of the geopolymer composites. The findings indicated that a higher AGP content corresponded with a reduced mortar flow, while increasing the proportion of AGP resulted in the diminished compressive strength of the geopolymer composites. Incorporating 10–20% AGP into the geopolymer mortar gave satisfactory compressive strengths (75–85%) compared to the reference mortar. Thermal conductivity testing indicated that AGP enhanced the thermal insulating properties of mortar. Notably, the compressive strength, after being exposed to 600–900°C, improved with the inclusion of the AGP. Based on XRD, the combeite crystalline phase was present in the mortars containing 20% and 40% AGP after being subjected to 900ºC. This phase contributed to the durability and stability of the material. Thus, it was confirmed that AGP not only served as a beneficial additive but also could play a crucial role in the thermal resilience of geopolymer systems
Manholes Detecting and Mapping Using Open-World Object Detection and GIS Integration
No full textAccurate detection and mapping of manholes are essential for urban infrastructure management, facilitating efficient maintenance and safety. This paper introduces a novel methodology that integrates the open-world object detection model, Grounding DINO, with geographic information systems (GIS) to detect and geolocate manholes in urban environments. Unlike traditional object detection approaches that rely on extensive labelled datasets and predefined object categories, Grounding DINO, a transformer-based model, leverages natural language processing for adaptable, scalable detection. Grounding DINO processes natural language descriptions to detect the manholes in an open-world context, overcoming the limitations of predefined object categories. Detected manholes are localized using multi-view triangulation, which refines their 3D positions by leveraging redundant camera viewpoints and intrinsic calibration parameters, which ensures accurate geometric mapping of manhole centers. The resulting geospatial coordinates are transformed into the WGS84 system using a global navigation satellite system/inertial navigation system (GNSS/INS) for compatibility with GIS platforms. The proposed approach achieved sub-meter precision, with mean localization errors of 0.36 meters in easting and 0.34 meters in northing, evaluated on KITTI dataset sequences under various urban conditions. The seamless integration of object detection and geospatial mapping demonstrates the potential of this approach for efficient and scalable urban infrastructure management. Doi: 10.28991/CEJ-2025-011-04-07 Full Text: PD
Analyzing and Modeling Toll Road Service Performance: TRSQ Model and Emerging Influencing Variables
No full textThe construction of toll roads supports economic and social mobility while driving regional development. However, toll road services face challenges such as deteriorating road quality, lack of facilities, and traffic disruptions due to accidents or repairs. This study aims to identify variables, examine their relationships, and develop a model for the factors influencing toll road services. The research uses both quantitative and qualitative approaches with explanatory research. The initial model of variables refers to the TRSQ model, which includes information, accessibility, reliability, mobility, safety and security, rest areas, and responsiveness. A questionnaire instrument is used and tested with SPSS for data validity and reliability. The data is then processed with SmartPLS to examine the relationships between variables. The results show a positive and significant impact on toll road service performance. However, 36.9% of toll road service performance is influenced by factors outside the model. To identify additional variables, bibliometric analysis using VOSviewer and expert opinions was used. The findings revealed that environmental factors, innovation, climate change, and public-private partnerships also affect toll road service performance. This led to the development of a model that serves as a framework for improving toll road service quality
Circularized and Corner-Rounded Rectangular Reinforced Concrete Columns Wrapped with CFRP Under Eccentric Compression
No full textThis paper presents experimental and analytical investigations on the behavior and mechanical properties of carbon fiber reinforced polymer (CFRP) confined circularized and corner-rounded rectangular reinforced concrete (RC) columns under eccentric loading. Twelve RC columns with cross sections of 150×200 mm were tested. Two columns were used as control specimens. Five columns were circularized and then wrapped with five CFRP configurations. The other five columns were corner-rounded and then wrapped with the above configurations. These twelve columns were eccentrically loaded until they failed. The results indicated that CFRP-confined circularized RC columns failed by CFRP rupture at the eccentric side, while CFRP-confined corner-rounded RC columns failed by CFRP rupture localized at corners. The outstanding effectiveness of the circularization method was its increase in the ultimate load of CFRP-confined circularized RC columns by 3.0–4.3 times that of the control columns. In contrast, the corner-rounding method moderately increased the ultimate load of CFRP-confined corner-rounded RC columns by 1.3–1.7 times that of the control columns. The circularization method outstandingly improved the elastic stiffness by 273.9%–419.3% compared with that of control columns, whereas the corner-rounding method exhibited no effect on the elastic stiffness. The rotation ductility of CFRP-confined circularized and corner-rounded RC columns significantly improved to high ductility when confined with more than 1.33 CFRP layers. Theoretical analyses were performed, and simple models were proposed for reasonably estimating the ultimate loads of the CFRP-confined circularized and corner-rounded RC columns under eccentric loading
Prediction the Dynamic Modulus of Hot Asphalt Mix Using Genetic Algorithms and Neural Network Modeling
No full textThe dynamic modulus is a fundamental characteristic of asphalt concrete and expresses the stiffness properties of a hot mix asphalt mixture as a function of temperature and loading rate. This study used artificial neural network modeling and genetic algorithms to evaluate the asphalt concrete dynamic modulus. The experimental database was collected from LTPP DATA that used in the ANN and genetic algorithm development and modeling. The output for the two models was the asphalt concrete dynamic modulus. Moreover, mathematical models were employed to predict the dynamic modulus of asphalt concrete with different parameters. Following the establishment of the model designs, the deficiencies and strengths of the proposed models are evaluated using determination coefficient (R2) values. The evaluation was performed by comparing the dynamic modulus of asphalt concrete predicted from four models with the dynamic modulus obtained from the experimental testing. Notably, the neural network models achieved precise calculations for models 1 and 2, with R2 values of 0.96 and 0.93, respectively. The genetic algorithm models achieved R2 values of 0.73 for model 1 and 0.64 for model 2. The two models, the genetic algorithm model and the artificial neural network model, contributed to the generation of two new empirical equations
Effects of H₂SO₄, HCl, and MgSO₄ Attack on Porcelain-Based Geopolymer Concrete
No full textThis study examined the durability of porcelain-based geopolymer concrete when exposed to strong acids, chlorides, and sulfates. Specimens prepared with a 14M NaOH solution and initially cured at 105°C for 24 hours were submerged in acidic and alkaline solutions for varying durations—3, 7, 14, 21, 28, 60, and 90 days. Compressive and splitting tensile strength tests were conducted to assess material performance. The results showed that immersion in H₂SO₄, HCl, and MgSO₄ solutions led to weight loss and reductions in both compressive and splitting tensile strengths. Strength deterioration was more pronounced in the early stages, with a peak weight loss rate of 15.32 g/day. After 90 days in 20% H₂SO₄, 20% HCl, and 20% MgSO₄ solutions, the residual compressive strengths were measured at 2.80, 14.19, and 3.29 N/mm², respectively, while splitting tensile strengths were recorded at 0.40, 1.21, and 0.51 N/mm². The ratio of splitting tensile strength to compressive strength (fsp/f’c) was influenced by molar concentration and immersion duration. Experimental findings revealed that a high molarity NaOH solution and elevated curing temperature enhanced resistance to HCl attack more effectively than H₂SO₄ and MgSO₄. Moreover, the experimental data closely aligned with the ACI 318 design code, though it tended to overestimate tensile strength
Soil Erosion Risk and Mitigation Strategies in Steep and Complex Forest Ecosystems
No full textThe soil erosion risk on the slopes in Luong Son District, Hoa Binh Province, Vietnam, was determined to inform sustainable land management and conservation planning. Remote sensing and geographic information system (GIS) technologies were integrated with the universal soil loss equation (USLE) model to generate thematic maps of rainfall erosivity (R), soil erodibility (K), topographic factors (LS), and vegetation cover. These maps were combined to produce a comprehensive soil erosion risk map. The results showed that 65.09% of the district (23,747.61 hectares), mainly flat and midland areas, had no erosion risk. Light, moderate, and severe erosion affected 19.95%, 7.61%, and 7.35% of the region, respectively. Higher erosion risk is concentrated in mid-level mountainous and limestone regions, characterized by steep slopes and sparse vegetation. These findings highlight the influence of slope gradient and length on erosion severity and spatial patterns. Remote sensing, GIS, and USLE were integrated to spatially assess soil erosion, providing a scientific basis for targeted interventions, such as reforestation and terrace farming. This study contributes to gaps in the literature by comprehensively analyzing spatial soil erosion risk and providing practical recommendations for mitigating soil erosion in vulnerable landscapes and supporting sustainable land use planning under climate change pressures
Effect of Waste Tire Rubber Particles on the Properties of Rubberized Concrete
No full textMillions of waste tires accumulate annually worldwide, posing environmental and public health challenges. Recycling these tires in concrete production presents a sustainable and practical solution. The present study was intended to investigate the effects of waste tire particles of varying sizes and shapes; specifically granular, short fiber, and mixed fine crumb rubber, along with coarse shredded rubber; on the characteristics of rubberized concrete. Fine rubber particles replaced sand, while shredded rubber replaced stone aggregates at 5%, 10%, and 15% substitution levels by weight. Results revealed that increasing rubber content reduced density, compressive strength, modulus of elasticity, and tensile strength. However, workability, Poisson’s ratio, ductility, and toughness improved significantly in comparison with conventional concrete. This study compares the effects of particle size and shape of rubber used in rubberized concrete. Notably, the newly introduced short fiber-type rubber particles exhibited superior mechanical properties compared to the granular and shredded rubber forms, revealing their potential for structural applications