2031 research outputs found
Sort by
Self-Cleaning Cement Material with Bismuth Titanate Photocatalytic Additive
No full textNowadays, mortars are building materials with various properties that can be achieved through the careful selection of components and the introduction of different modifying additives. An additive based on the TiO₂–Bi₂O₃ oxide system can be considered a modifying component with photocatalytic and biocidal properties capable of decomposing organic pollutants, viruses, bacteria, and fungal spores. The purpose of the work was to obtain cement compositions containing the additive, study their physical and mechanical properties, evaluate their photocatalytic activity in accordance with the UNI 11259-2016 standard, and assess their resistance to mold fouling. In this study, samples of cement–sand plaster with the TiO₂–Bi₂O₃ additive synthesized via citrate-based technology at 1.7 and 5.0 wt.% were prepared, and their physical, mechanical, photocatalytic, and biocidal properties were examined. As a result, the authors identified photocatalytic activity in both the UV and visible spectra, achieving 69% after 26 hours of UV irradiation. The samples demonstrated 100% resistance to mold fouling. The compressive strength of the modified samples increased by 32.0–39.0%; bending strength by 33–38.0%; and adhesion strength to the base by 60–70%. The cost calculation also confirmed the feasibility of introducing the additive at 1.7 wt.% into the cement composition. The resulting cement material formula can be recommended for designing fungi-resistant, self-cleaning plasters
Statistical (SPSS) Models: Ultimate Uplift Capacity of Horizontal Square Anchor Plate
Full text linkThis paper examines the relationship between ultimate capacity and vertical displacement for single anchors and line anchor groups (1×2), (1×3), (1×4), and (1×5), in relation to the number of anchors and the embedment depth. Studies addressing statistical analysis in this area are limited; therefore, it was considered appropriate to conduct a statistical investigation to support this field with analytical results and to provide a foundation for future research. The statistical analysis for the single anchor plate indicated that the correlation between ultimate capacity, number of anchors, and embedment depth was strong, with acceptable values of R and R² and a well-fitting mathematical model. In contrast, vertical displacement showed insufficient mathematical representation when analyzed against the number of anchors and embedment depth, as vertical displacement is influenced by additional factors such as loading duration (creep effects), soil unit weight, plate shape and dimensions, internal friction angle, and moisture content, rather than by ultimate capacity alone. When the number of anchor plates in a group exceeds three, the vertical displacement at system failure increases due to the reduced strength of the soil associated with larger anchor groups
The Influence of the Fine Earth Composition of the Soil Mixture on the Parameters of Its Filtration, Moisture Content, and Density
No full textThe article presents the results of laboratory studies on the patterns of change in the filtration coefficient of the fine-grained component (fine earth) of the soil mixture from a number of influencing factors. The study was conducted to assess the impact of the fine earth fractional composition of a soil mixture on its filtration parameters and density-moisture state. The experiments were conducted using a compression device, the use of which is regulated by the standard of the Republic of Kazakhstan. One hundred and twenty-six fine earth samples were tested, containing 50 to 75% (by weight) of various fractions with particle sizes smaller than 5 mm. An analysis of the test results revealed that for large fractions (with particle sizes of 5 mm or less, but more than 1 mm), the filtration coefficient of fine earth increases as the weight content of fractions in it increases (from 50 to 75%), while for small fractions (with particle sizes of 1 mm or less), it decreases. It was determined that similar patterns are characteristic of the increase in moisture content and increase in the density of fine earth, which occur when water is filtered through it. The scientific novelty of the research lies in the fact that, based on the identified patterns, correlation dependencies were established between the filtration coefficient and the weight content of various fractions, as well as the increase in moisture content and the increase in the density of fine earth. Correlation dependencies of the filtration coefficient on the weight content of various fractions, as well as on the increase in moisture content and increase in the density of fine earth, were established. Based on the established relationships, formulas were developed for predicting the filtration coefficient, moisture content, and density of fine earth, which adds practical value to the research. These formulas are recommended for use in selecting optimal fine earth compositions for soil mixtures used in dam construction
Data-Driven Approach to Predict Fire-Resistance Ratings of Timber Columns
No full textThis study aims to determine whether a data-driven-based approach provides more accurate predictions of timber fire-resistance ratings (FRR) compared to conventional empirical methods. To achieve this, a machine learning framework based on the Deep Belief Network (DBN) was employed. A comprehensive database collected from previously published reports was used to train and validate the DBN model. The model’s predictive performance was benchmarked against traditional empirical equations derived from mechanics-based methods. The comparison demonstrated that the DBN model provided superior accuracy in predicting fire-resistance ratings. Model evaluation was further conducted using the Coefficient of Determination (R²) and Root Mean Squared Error (RMSE), confirming the robustness of the proposed approach. In addition, a parametric analysis was performed to assess the influence of input variables on the output. Results indicated that induced load (IDL) and breadth (BRH) were the most influential parameters, whereas ultimate strength (ULS) and elasticity modulus (ELM) had relatively minor effects. This study highlights the potential of advanced machine learning techniques, particularly DBN, to enhance predictive accuracy in structural fire engineering, offering a significant improvement over conventional calculation methods
Shrinkage Characteristics and Abrasion Resistance of Porcelain Waste-Based Geopolymers Mortar Under Chemical Exposure
Full text linkThis study investigated microstructural analyses, dry shrinkage, and autogenous shrinkage of mortar using defective sanitary ware porcelain as a low-calcium material with sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃). Additionally, the abrasive resistance of concrete was examined under chemical corrosion environments of 5%, 10%, 15%, and 20% H₂SO₄, HCl, and MgSO₄. The microstructural analyses using XRF, DTA-TGA, and SEM were conducted at 28 days. For specimen preparation, mortar specimens were oven-cured for 2 h at 105°C, while concrete specimens were oven-cured for 24 h and air-cured for 28 days before undergoing chemical immersion at 3, 7, 14, 21, 28, 60, and 90 days. NaOH concentrations of 8, 10, 12, and 14 Molar (M) were used. The results indicated that shrinkage in porcelain-based geopolymer mortars increased with higher NaOH concentration, and increasing the initial curing temperature led to increased mortar shrinkage. The autogenous shrinkage of 14M alkali-activated porcelain mortar was found to be higher than that of 8M, 10M, and 12M NaOH concentration mortars. Additionally, increasing the NaOH concentration reduced the abrasive resistance of the concrete. The maximum weight loss values were 8.21%, 6.91%, and 0.96% for 20% H₂SO₄ (90 days immersion), HCl (90 days immersion), and 20% MgSO₄ (90 days immersion), respectively. The microstructural findings confirmed the formation of gel-intact phases, highlighting the importance of curing time and NaOH concentration in low-calcium binder material. This study emphasized the critical role of curing temperature in optimizing the mechanical and durability properties of defective sanitary ware porcelain-based geopolymer
A Procedure for Nonlinear Analysis of Laterally Loaded Single Piles and Pile Groups
No full textThis research introduces an analytical procedure for simulating the nonlinear behavior of single piles and pile groups under lateral loads in multi-layered, heterogeneous soil. The methodology combines the finite element method, the p-y technique, and the p-multiplier concept. Duncan and Chang's hyperbolic equation, characterized by three parameters, was employed to represent the soil reaction for sand and clay soils. A newly proposed equation to derive p-multipliers as a function of a pile's location and spacing within a pile group. Its predictions show satisfactory agreement with those from existing methods. The procedure was implemented in a computer program to enable rapid and accurate computation. The proposed program validation involved comprehensive comparisons against results from field load tests and sophisticated 3D finite element analyses. These comparisons confirm that the developed program is both reliable and efficient, making it well-suited for preliminary design stages. A subsequent parametric study on a single pile revealed that replacing soft upper clay with a compacted sand layer significantly decreases lateral deflection and bending moment. For the cases examined, an optimal compacted layer thickness of three pile diameters and a stiffness 5.6 times that of the native soft clay were identified
Empirical Analysis of Risk Behavior in Truck Drivers Across Industrial Zones and Policy Recommendations
No full textTruck drivers play a crucial role in industrial development but face disproportionately high risks of traffic-related injuries and fatalities. These risks arise from complex traffic conditions, especially in truck-congested industrial zones, and economic pressures that encourage risky driving behaviors. This study investigates key factors influencing these behaviors among truck drivers in industrial zones using an integrated framework combining the Health Belief Model and Protection Motivation Theory, a novel approach in this context. A random parameter model was employed to account for unobserved heterogeneity in drivers’ responses. The results highlight several significant psychological factors: perceived susceptibility (when drivers perceive the risk of crashes while driving), perceived severity (when drivers feel that crashes will impact their work), perceived barriers (when truck drivers perceive that fastening seat belts causes discomfort and when they perceive safety equipment for vehicles as expensive and unaffordable), cues to action (when truck drivers encounter safe driving campaigns), and health motivation (when truck drivers prioritize adequate rest and relaxation). Additionally, the study identifies route familiarity as a random effect, revealing variations in how this factor influences behavior across individuals. The study provides practical, evidence-based policy recommendations aimed at reducing road injuries and fatalities among truck drivers, offering valuable insights for policymakers, transport authorities, and logistics stakeholders
Bio-Based Modification of Natural Rubber-Modified Asphalt Using Hard Resin from Yang
Full text linkThis study investigates the potential of hard resin derived from the Yang tree (HY), a renewable bio-based byproduct, as a performance-enhancing additive in natural rubber-modified asphalt (NRMA). HY-modified binders (HYMA) containing 3%, 7%, and 15% HY by weight were evaluated through a multi-scale experimental program, including physical, rheological, thermal, chemical, and mechanical tests. Standard binder characterizations (penetration, ductility, softening point, viscosity), spectroscopic analyses (FT-IR, NMR), microstructural observations (ESEM, XRD), thermal profiling (DSC), and performance assessments (DSR, Marshall) were conducted. The results demonstrated that HY improved binder properties at optimal concentration by introducing additional hydrocarbon structures without chemical cross-linking. HYMA3 achieved the most favorable balance of stiffness, flexibility, and compaction efficiency, whereas higher HY contents (≥7%) impaired structural integrity and deformation resistance. Microstructural and thermal evidence confirmed surface modifications and altered thermal transitions, which influenced viscoelastic response. These findings provide new insights into bio-resin–asphalt interactions and establish the viability of HY as a sustainable alternative to synthetic polymer modifiers. Beyond performance improvement, HY promotes circular construction by transforming agricultural byproducts into functional pavement materials, supporting the development of climate-adaptive infrastructure
Multi-Spring Model and Pushover Analysis of Masonry-Infilled Wall in RC Frame Under Tsunami Loading
Full text linkThis study investigated the behavior of masonry-infilled walls (MIWs) within reinforced concrete (RC) frames when exposed to hydrodynamic forces from tsunamis by employing a multi-spring modeling approach across different inundation levels. The proposed analytical model divided the MIW into 1 to 5 horizontal nonlinear spring elements that were allocated along the wall's height. Each spring represented a segment of MIW and was defined by a tri-linear force–displacement relationship. The model was calibrated with the experimental data from previous studies and was analyzed using pushover assessment under uniformly distributed hydrodynamic forces corresponding to four tsunami inundation levels (0.25H, 0.50H, 0.75H, and 1.00H). The models, which had employed four or five horizontal springs, had most effectively replicated MIW behavior under tsunami loading at all inundation depths. Conversely, single-spring models tend to overestimate lateral resistance by up to 50%, particularly when the frame is only partially submerged. This discrepancy arises because less force is transmitted through the MIW, with a greater amount of it being transferred directly to the foundation. The utilization of several spring elements provided a realistic load path, improved the interaction between the frame and MIW characterization, and optimized the precision in simulating lateral resistance and post-peak behavior
Evaluation of Flood Inundation Image Detection Performance Using Deep Learning
Full text linkFloods are the most frequently occurring natural disasters, significantly impacting the environment and society. As part of natural disaster mitigation, the impacts could be reduced through predictive techniques using deep learning for semantic segmentation of inundation images. Therefore, this research aims to evaluate the performance of deep learning architectures in segmenting inundation images using the Flood Segmentation dataset, which comprised 290 aerial images. The following segmentation architectures, U-Net, SegNet, and LinkNet, were compared using backbones such as MobileNet, ResNet, EfficientNet, and VGG, as well as optimizers including Adam, SGD, AdaDelta, and RMSProp. Performance was assessed using Intersection over Union (IoU) score, precision, F1-score, recall, and accuracy metrics. The results showed that U-Net achieved the highest performance with IoU, precision, F1-score, recall, and accuracy of 0.767, 0.862, 0.866, 0.876, and 0.899, respectively. Regarding the backbones, MobileNet excelled with IoU, precision, F1-score, recall, and accuracy of 0.764, 0.866, 0.865, 0.869, and 0.898, respectively. The Adam optimizer outperformed others, yielding IoU, precision, F1-score, recall, and accuracy of 0.712, 0.807, 0.824, 0.873, and 0.843. In conclusion, the combination of U-Net with MobileNet backbone and Adam optimizer was the most effective architecture for flood inundation image segmentation, offering a robust foundation for prediction systems