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2007 research outputs found
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Engineering and Microstructure Properties of Soft Clay Improved with Ordinary Portland Cement and Polymers
No full textThis study investigated properties of soft Bangkok clay stabilized with ordinary Portland cement (OPC) and various polymers. Variables included initial water content (1.0LL, 1.5LL, and 2.0LL; LL = liquid limit), polymer type (polyvinyl alcohol (PVA), polyethylene glycol (PEG), and polyvinylpyrrolidone (PVP), polymer concentration (1%, 3%, 5% and 7%), and curing time. The unconfined compressive strength (UCS), consolidation, permeability, and microstructure were analyzed. UCS decreased with increasing water content due to delayed polymer bonding; however, at 1.0LL, polymers effectively bonded clay particles, resulting in higher UCS. A 3% polymer concentration yielded the highest UCS, while 5–7% led to non-homogeneous structures and reduced UCS. The UCS of the sample with PEG outperformed those with PVA and PVP. At 1.0LL and 3% polymer, 28-day UCS values were 1.20 MPa (PEG), 1.12 MPa (PVA), and 1.04 MPa (PVP), all exceeding the Department of Highways' standard 1.0 MPa. Higher polymer concentrations decreased void ratios and permeability by forming hydrogel layers and thin films, increasing soil density. SEM/EDS analysis confirmed 3% polymers formed uniform films enhancing soil bonding, while 7% resulted in thicker, irregular films, reducing UCS. These findings suggest that polymers could be a promising alternative to OPC in environmentally friendly deep mixing applications. Doi: 10.28991/CEJ-2025-011-04-022 Full Text: PD
Effect of Graphene Oxide on the Performance of Fly Ash Concrete Exposed to Ambient Temperature
No full textThe rising global temperatures due to climate change are accelerating concrete deterioration by shortening its service life, which subsequently increases maintenance costs. Therefore, the objective of this investigation is to analyze the graphene oxide (GO) effect on the mechanical characteristics and microstructural properties of fly ash (FA) concrete exposed to ambient temperatures. Concrete specimens were created by employing GO from 0.01% to 0.05% by weight of cement and cured using two distinct methods. These include standard curing in immersed water and for 7 days followed by ambient exposure. The mechanical test showed that GO significantly enhanced compressive strength, with 0.04% GO observed to have increased strength by approximately 16% at 28 days. However, exposure to ambient conditions led to decreased compressive and flexural strength and increased mass loss. The microstructural analysis also showed that ambient-exposed concrete exhibited higher porosity and incomplete hydration. The results showed that the addition of GO enhanced durability by refining the microstructure, reducing porosity, and enhancing thermal stability. Thermal analysis also confirmed that GO minimized moisture loss and improved thermal resistance. Furthermore, Fourier Transform Infrared Spectroscopy (FTIR) validated the improvement in bonding for the GO-FA concrete. These results showed that GO could mitigate the adverse effects of environmental exposure, leading to its identification as an advantageous additive to increase the long-term durability and concrete performance in different temperature conditions
Hydraulic Conditions Created by Passing Flow Through and Over a Combined Weir
No full textIn this study, a novel broad-crested weir was designed to investigate the effect of varying rectangular gate widths (vertical slots) on the discharge coefficient and free surface profile of a compound weir. Six weir models with vertical slots were theoretically and experimentally examined in a laboratory flume under uniform flow conditions. Each weir model measured 9.5 cm in height, 30 cm in length, and 10 cm in width. The vertical slots were uniformly 7.5 cm in height, with six different widths ranging from 0.5 cm to 3.0 cm, corresponding to a range of opening area ratios (OAR) from 10% to 60%. Under different head conditions, six flow rates between 10 and 35 m³/hr were tested. Dimensional analysis and multivariable regression techniques were applied to derive a formula relating the discharge coefficient to key influencing variables. These variables include the ratio of total energy head to flume width (Ht/B), the ratio of upstream water head to flume width (Hw/B), and the ratio of slot width to flume width (Bg/B). The results indicated that the discharge coefficient (Cd) of the compound weir increases with both Ht/B and Hw/B, and with increasing slot width (Bg/B). The proposed model, which describes the relationship between measured and computed discharge coefficients, demonstrated excellent accuracy, with R²= 0.998. Furthermore, the findings showed that the width of the weir openings has a significant impact on upstream water depth, downstream free surface profiles, and the hydraulic characteristics of the resulting flow transitions. Doi: 10.28991/CEJ-2025-011-05-016 Full Text: PD
Effect of Axial Load on the Seismic Performance of Steel Reinforced Concrete Beam-Column Joint
No full textSteel-reinforced concrete (SRC) provides numerous advantages, such as enhanced energy dissipation, ductility, stiffness, and strength, particularly in seismic performance. Several studies on the effect of axial loads on columns found that axial loads have an insignificant influence on column capacity, though they influence long-term performance. Beam-column joint elements are among the critical components that determine the seismic behavior of a structure. Inaccurate design of these joints can lead to fatal structural damage, potentially causing structural collapse. This study aimed to perform a numerical analysis of various joint configurations under cyclic and axial loads to identify models with the best seismic performance that consisted of four models using different SRC length parameters. The research used nonlinear finite element methods with the ABAQUS software, which enables detailed simulations of joint behavior, including predictions of failure mechanisms that are difficult to observe in experimental testing. The results of the analysis showed that the CS-02 model demonstrated the best seismic performance. Axial load increased the capacity in all models, improved energy dissipation in the RC model, slightly reduced dissipation in CS models, and caused different rotational behavior across models
Impact of Unhydrated Lime on the Geotechnical Properties of Clayey Soil
No full textThis study investigates the impact of quicklime (CaO) on improving the geotechnical properties of clayey soil. Quicklime was mixed with soil in varying proportions (2%, 5%, and 8% by dry weight) to assess its effects. The results showed that increasing lime content reduced specific gravity, while the optimum moisture content (OMC) and plasticity index increased. Additionally, the liquid limit, plastic limit, and plasticity index decreased, and there were improvements in compressive strength, friction angle, and unconfined compressive strength. Compression parameters such as the compression index (Cc), rebound index (Cr), volume change coefficient (mv), and compression modulus (av) decreased with increasing lime content. The most significant improvement was observed at 2% lime, with further increases to 5% and 8% resulting in less improvement. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were conducted to explore the mineralogical and structural changes in the soil, demonstrating the chemical and physical interactions between lime and soil. This research provides valuable insights into the role of quicklime in modifying clayey soil properties, with implications for improving geotechnical performance in civil engineering applications, particularly in road and infrastructure projects. Doi: 10.28991/CEJ-2025-011-05-012 Full Text: PD
Optimized Feature Selection for Predicting the Number of Casualties in Traffic Crashes
No full textTraffic crash prediction remains a critical challenge in transportation safety management, with increasing emphasis on leveraging machine learning techniques for accurate casualty prediction. This study aims to develop an optimized feature selection framework for traffic crash casualty prediction by comparing six selection techniques: Design of Experiments (DOE), Forward and Backward Sequential Feature Selection, Information Gain, Lasso Regularization, and Random Forest (RF) Feature Importance, with subsequent integration using the Borda count method. By analyzing 517,000 UK traffic crash records (2019-2023), 25 machine learning models (linear models, decision trees, ensemble methods, and neural networks) were evaluated across 12 critical attributes. Results demonstrate eXtreme Gradient Boosting (XGBoost)'s superior performance with a Root Mean Square Error (RMSE) of 0.671 and Mean Absolute Error (MAE) of 0.372 using the proposed Borda count integration method while maintaining efficient computation time (11.3 minutes compared to the baseline's 17 minutes). Five factors consistently emerged as the most influential predictors across all selection methods: number of vehicles involved, speed limit, police officer attendance, day of the week, and urban/rural classification, while environmental factors showed lower importance than traditionally assumed. The novel integration of multiple feature selection techniques through Borda count provides a more robust feature subset than any individual method, offering an optimal balance between computational efficiency and prediction accuracy. The framework enables transportation safety authorities to implement more efficient crash prediction systems while providing actionable insights about key risk factors for targeted interventions, especially to support the Highway Safety Manual development. Doi: 10.28991/CEJ-2025-011-04-01 Full Text: PD
A Novel One-Sided Push-Out Test for Shear Connectors in Composite Beams
No full textThe small-scale push-out test (POT) is widely utilized to investigate the characteristic behavior of shear connectors as an available alternative to full-scale beam tests, which are often costly and time-consuming. However, several researchers have expressed issues regarding the POT specimen setup during testing due to inconsistencies between the results of POTs and beam bending tests. In this paper, a new configuration for a one-slab POT is developed to address these issues. To validate the developed method of testing, several POTs and OSPOTs were conducted and compared against each other and with those of previous research. The load-slip curves obtained from the OSPOTs were then evaluated against the curves obtained from four empirical expressions. Furthermore, a database of different POT configurations and setups, specifically 114 tests, selected from the previous research that employed the 19 mm shear stud, was analyzed in detail. Subsequently, the results of these tests and the proposed OSPOT method were compared with the predictions offered by several empirical equations. The results indicated that the results of the OSPOT are more consistent with the codes and empirical equations compared to typical POT. Hence, this OSPOT setup could be used as an efficient and economic option for the POT, as it has the potential to double the number of results for the same resources and simplify the casting procedure, which is particularly significant when numerous tests are required for the experimental campaign. Also, the OSPOT results revealed more ductile behavior for the shear studs, which is consistent with the full-scale beams’ testing
Influence of Emulsion Type and Moisture on the Stiffness of Stabilized Granular Soil
No full textThe objective of this study is to investigate how moisture content affects the stiffness of a gravelly-sandy soil stabilized with asphalt emulsion, considering different types of emulsion (medium- and slow-setting) and modified compaction energy. Dynamic triaxial tests were carried out to determine the stiffness of specimens at different moisture contents, considering the dry and wet branches of the compaction curve, all stabilized with 2% asphalt emulsion. The influence of moisture content and emulsion type was assessed using robust analysis of variance (ANOVA), allowing the evaluation of statistical significance and the interaction between factors. The results showed that the stiffness of the stabilized soil is strongly influenced by moisture content, with a peak value observed near the optimum moisture (~8.2%). The slow-setting (SS) emulsion achieved the best performance, reaching 938.94 MPa, representing a 452.32% increase compared to the untreated soil. The medium-setting (MS) emulsion also produced a significant stiffness gain (375.29%). Statistical analysis indicated that emulsion type was the most influential factor (Q = 1747; p = 0.001). This study contributes to the literature by experimentally and statistically demonstrating how moisture content and emulsion type affect the stiffness of stabilized soils
Modeling the Compressive Strength of Metakaolin-Based Self-Healing Geopolymer Concrete Using Machine Learning Models
No full textMetakaolin-based self-healing geopolymer concrete treated with Bacillus bacteria represents a significant advancement in sustainable construction due to its eco-friendly properties, enhanced durability, and self-healing capabilities. It is a transformative material for sustainable construction. By reducing carbon emissions, utilizing waste, improving durability, and lowering lifecycle costs, it aligns with global goals for environmentally friendly and resilient infrastructure. Continued research and development will further unlock its potential, making it a cornerstone of the future of sustainable construction. In this research project, a study on modeling the compressive strength of environmentally friendly metakaolin-based self-healing geopolymer concrete treated with Bacillus bacteria (BB) has been conducted, analyzed, and reported. Machine learning methods such as the "Group Methods Data Handling Neural Network (GMDH-NN)”, "Generalized Support Vector Regression (GSVR), "K-Nearest Neighbors (KNN)”, "Tree Decision (Tree)”, "Random Forest (RF)” and "Extreme Gradient Boosting (XGBoost)” were applied to model the compressive strength of the self-healing concrete. The GMDH-NN model was created using GMDH Shell 3.0 software, while XGBoost, GSVR, KNN, Tree, and RF models were created using "Orange Data Mining” software version 3.36. The research method also included gathering relevant experimental and field data, categorizing it effectively, and performing initial analysis to identify trends and relationships. A global representative database was collected from literature for different mixing ratios of self-healing concrete corresponding to the compressive strength, with a total of 147 records, which contained Fly Ash (FA), Silica Fume (SF), Metakaolin (MK), and Bacillus Bacteria (BB) considered as the input constituents. The collected records were divided into a training set (75%) and a validation set (25%) based on established requirements. At the end of the modeling exercise, the GMDH-NN produced the best model with an accuracy of 0.99, while the KNN and the GSVR followed closely with accuracies of 0.975 and 0.97, respectively. However, the RF and the Tree models also produced good accuracies of 0.965 and 0.955, respectively. Also, the GMDH-NN and the KNN again outperformed the other methods, producing an R² of 1.00 and 0.99, respectively, while the GSVR, RF, and Tree followed in this order with R² of 0.98, 0.97, and 0.96, respectively. The error indices, such as the overall error, RMSE, MSE, MAE, and SSE, also confirm this order of performance. The sensitivity analysis on the modeling of compressive strength of metakaolin-based self-healing geopolymer concrete treated with Bacillus bacteria produced a metakaolin (MK) impact of 30%, a silica fume (SF) impact of 29%, a fly ash (FA) impact of 27%, and a Bacillus bacteria (BB) impact of 14%. This highlights the dominant role of metakaolin (30%), silica fume (29%), and fly ash (27%) in determining the compressive strength of metakaolin-based self-healing geopolymer concrete. Bacillus bacteria (14%) have a smaller but meaningful impact, primarily contributing to self-healing and long-term durability. These insights can guide material selection, mix design, and process optimization to enhance both strength and durability. Doi: 10.28991/CEJ-2025-011-04-020 Full Text: PD
Shrinkage Characteristics and Abrasion Resistance of Porcelain Waste-Based Geopolymers Mortar Under Chemical Exposure
No full textThis 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