Journal of Materials and Engineering Structures
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Utilization of recycled concrete aggregates mixed with fly ash as the road base materials in Vietnam
Full text linkRecycling of construction and demolition waste (CDW) and industrial by-products presents substantial environmental, economic, and social benefits. In developed countries, recycling efforts have been actively promoted for decades, resulting in a high proportion of CDW and industrial by-products being reutilized. However, in developing nations, including Vietnam, the recycling rate of such materials remains relatively low. This study investigates the utilization of the mixtures of recycled concrete aggregates (RCA) and fly ash (FA) as road base materials in Vietnam. In which, FA functions as the fines within the aggregate gradation and is varied from 0% to 20%. A series of laboratory tests was conducted to assess the mechanical behavior of the prepared samples. The findings demonstrate that FA can effectively substitute fines in aggregate mixtures for road base layers, with an optimal content of 5%. The pozzolanic reaction of FA contributes to an enhancement in the California Bearing Ratio (CBR) of RCA-FA mixtures compared to that of natural aggregates, ensuring compliance with required bearing capacity standards, except for specimens containing 20% FA. Moreover, the adhesive mortar generated through the pozzolanic reaction of FA exhibits lower bonding strength than that formed by RCA fines, leading to a lower CBR value and a higher particle breakage index in RCA-FA mixtures compared to RCA-only samples
Machine Learning Approaches for Predicting Flexible Asphalt Pavement Elastic Modulus: ANN vs. Random Forest vs. XGBoost
Full text linkThis study compared four predictive models—Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for estimating the elastic modulus of flexible asphalt pavement. ANN demonstrated the highest predictive accuracy, with an optimized architecture featuring a single hidden layer with nine neurons. It achieved the best performance, with an R² value of 0.997, RMSE of 0.677, and MAE of 0.483. LR showed the weakest results, while RF and XGBoost performed better, with XGBoost slightly outperforming RF. However, neither model surpassed ANN. The findings highlight the importance of selecting appropriate predictive models for pavement modulus estimation, as ANN effectively captured complex nonlinear relationships. Due to limited data, the study evaluated models without splitting the dataset, which may have led to an overly optimistic assessment. Future research should incorporate larger datasets from diverse pavement structures and traffic conditions to improve model generalization. Additionally, exploring hybrid modeling approaches that combine multiple machine learning algorithms could further enhance predictive accuracy, offering more robust solutions for pavement design and quality assessment
Soft Computing Approaches for Tensile Strength Prediction of Sustainable Concrete Mixtures
Full text linkConventional concrete consumes large amounts of cement and aggregates, contributing to environmental degradation. This study investigates eco-friendly concrete mixtures incorporating industrial by-products such as fly ash, ground granulated blast furnace slag (GGBFS), and granite powder to enhance tensile strength and promote sustainable construction. A dataset of 252 experimental samples was analyzed using five statistical models—Linear Regression (LR), Pure Quadratic (PQ), Logarithmic (LOG), Interaction (INT), and Full Quadratic (FQ)—to predict tensile strength based on eight input parameters, including curing time, pull-off layer thickness, and mix ratios. Among the models, the FQ model exhibited the highest accuracy with the lowest residual error, followed by the INT model. Sensitivity analysis identified curing time, pull-off layer thickness, and the cement-to-sand ratio as key parameters affecting tensile strength. Results confirm that optimizing the water-to-cement and cement-to-sand ratios can significantly improve the performance of eco-friendly concrete. Overall, this study demonstrates that incorporating industrial by-products into concrete mixtures effectively balances mechanical performance with environmental sustainability, supporting the transition toward greener construction practices
A study on post-fire durability performance of concrete
Full text linkConcrete structures are often exposed to fire hazards, which can significantly affect their durability and structural integrity. Post-fire durability assessment is crucial to understanding the residual strength and long-term performance of concrete after exposure to elevated temperatures. Limited research has concentrated on durability attributes, with the majority examining primarily mechanical aspects. We have thus initiated the endeavor. This study aims to evaluate the post-fire durability characteristics of concrete, focusing on strength retention, microstructural changes, and degradation mechanisms. This investigation adapted concrete compositions containing 75% recycled marble aggregate (RMA) to the mix. We exposed the prepared concrete to various temperature conditions, including 200°C, 400°C, 600°C, and 800°C. The study examined the concrete's durability features, including their acid, chloride, and carbonation resistance. This investigation revealed that the fire significantly impacted the mechanical and durability properties of the concrete. The results show that fire, in any temperature environment, significantly reduces both the mass and strength of concrete. This is true regardless of the temperature environment. Regardless of temperature, fire has a greater impact on chloride penetration in concrete. Though it was still higher than concrete mixed at ambient temperature, RMA concrete did better than the reference concrete mix. Limiting chloride penetration in the RMA concrete mixture depends critically on the alumina concentration of RMA
Study on the Application of Simultaneous Localization and Mapping Solution for Surveying Tasks: A Case Study of RS10 3D Handheld SLAM
Full text linkThe article assesses the suitability of handheld SLAM for surveying tasks, focusing on visual observation and elevation accuracy from point cloud data. The study uses an RS10 handheld SLAM device, an integrated solution of GNSS technology, IMU sensors and three cameras to scan about one hectare using either real-time kinematic or post-processing kinematic working modes at approximately 4 Km/h speed. The elevations extracted from point cloud data are compared to those of 108 checking points measured by GNSS-RTK single base with 4G working mode at a close distance. Results showed the RS10's feasibility for surveying due to its accuracy and visual capabilities. Using different thresholds for assessment shows that 50 points (46.3%) deviate ≤ 0.010 m. 87 points (80.56%) deviate ≤ 0.020 m. 98 points (90.74%) deviate ≤ 0.030 m. 106 points (98.15%) deviate ≤ 0.040 m. Only points 85 (0.045 m) and 91 (0.052 m) deviate significantly. The standard deviation of 108 checking points is only 0.016 m. These results, excellent even without control points to adjust elevation elements, highlight the RS10's ability to provide detailed, feature-rich point cloud data along the scanning route
Investigation of permanent deformation characterisation of different asphalt mixtures
Full text linkPermanent deformation has emerged as a primary distress factor in asphalt pavements, largely driven by the recent rise in tyre pressures and axle loads. This deformation significantly impacts pavement performance, safety, and driving comfort, particularly when rutting depth exceeds a critical threshold. To address this issue, polymer-modified asphalt (PMA) mixtures have been widely adopted as an effective solution to resist permanent deformation (or rutting) under high temperatures. In this study, three different asphalt mixtures—one unmodified and two PMA mixtures—were selected for permanent deformation testing, which included the wheel tracking rutting test and the repeated load axial test under various conditions. The results were then compared, analysed, and discussed. The findings demonstrated a significant improvement in permanent deformation resistance in the PMA mixtures compared to conventional mixtures at both 45°C and 60°C. Furthermore, the results indicated that the use of PMA markedly enhanced both dynamic stability and strain rate, with the improvements becoming more pronounced at higher temperatures
Effect of different heat treatments on the fractural and microstructural behavior of pipeline steel
Full text linkAPIX70 pipeline steel is classified under the American Petroleum Institute (API) specification as one of the high-strength low-alloy (HSLA) steels. The APIX70 has a very fine-grained microstructure so they have high strength and ductility properties. In this research work, an attempt has been made to compare the microstructural and mechanical behaviour of base and heat treated APIX70 pipeline steel. Heat treatment was performed at two different temperatures 900 ̊C and 800 ̊C. Different phases such as ferrite, pearlite as well as blend of both were observed in base and heat treated APIX70 steel. Specimen heat-treated at 900°C shows an increase in hardness to 37.5 HRC as compared to the specimen heat treated at 800 ̊C which is 22.5 HRC. Base metal shows a 33.75 HRC hardness value. Microstructure of X70 base metal exhibit blend of ferritic and pearlitic phases. Heat treatment of X70 at 900 ̊C and 800 ̊C shows there is a significant grain growth takes place while lesser grain growth observed for specimen heat treated at 800 ̊C
Effect of geopolymerization on geotechnical characteristics of palm kernel shell ash stabilized laterite
Full text linkLaterite soils (LS) in tropical and subtropical regions exhibit poor engineering properties, making them unsuitable for road construction application. This study examined impact of alkaline activation on palm kernel shell ash (PKSA) stabilized LS. LS properties such as Liquid Limit (LL), Plastic Limit (PL), Optimal Moisture Content, Maximum Dry Density, and California Bearing Ratio (CBR) were determined to ascertain whether it is suitable as a roadway building material. The LS was mixed with different proportions (3, 6, and 9%) of PKSA only and then with an alkaline activator (i.e. 10 M Na2SiO3/NaOH). The earlier mentioned properties of the stabilized LS were determined. An X-ray diffraction test was conducted to examine the mineral composition of PKSA. The results show that the LS, in its natural state, is unsuitable for road construction applications without proper stabilization. Increasing the percentage of PKSA decreased LL, OMC and MDD for PKSA-based and PKSA-geopolymer-based stabilized LS. Furthermore, the CBR of PKSA stabilized soil improved by 77%. Upon further addition of alkaline activator, the CBR value increased by 50% when compared with the natural soil. It was concluded that adding an alkaline activator significantly impacts soil properties for construction. This environmentally friendly method can contribute to sustainable soil stabilization practices in tropical and subtropical regions
Influence of Aggregate Type and Size on Residual Mechanical Properties of Post-Heated Geopolymer Concrete: Experimental Study and Applications of Artificial Neural Networks
Full text linkTo mitigate environmental impacts from Portland cement (PC) production, the researcher’s efforts is introducing eco-friendly alternatives such as Geopolymer concrete (GPC). While GPC shows promise, further research is required to understand how fire or elevated temperatures affect GPC’s mechanical properties. This research investigates the effects of elevated temperatures (200℃, 400℃, 600℃, and 800℃) on the residual mechanical properties (compressive, flexural, splitting-tensile strengths, and modulus of elasticity) of ambient-cured fly-ash (FA)-based GPC compared to PC mixtures. The study examined various concrete types (GPC and PC), three coarse aggregate types (basalt, gravel, and crushed dolomite), and three crushed dolomite sizes (40 mm, 20 mm, and 14 mm). Additionally, Artificial Neural Network (ANN) models were developed to predict the residual compressive strength of both ambient-cured and heat-cured GPC after exposure to elevated temperatures. Results showed that basalt aggregate significantly enhanced the residual mechanical properties at 800 ℃, outperforming crushed dolomite and gravel in compressive, flexural, splitting-tensile strengths, and modulus of elasticity, with increases of (20%, 80%), (26%, 244%), (10%, 100%), and (14%, 140%), respectively. Moreover, the residual mechanical properties were found to be inversely proportion with max size of coarse aggregate. In addition, using ANN models proved its efficient in predicting the compressive strength for both ambient and heat-cured GPC with R² values of 0.94 and 0.887, respectively
Real-Time and Full-Scale Numerical Analysis and Monitoring of Displacements in the Left Bank of the Tabellout RCC Arch Dam during Construction and Operation, Jijel Province, Algeria
Full text linkThis study provides a comprehensive evaluation of the Tabellout RCC dam in Jijel Province, Algeria, a unique structure classified as a combined gravity-arch dam. It addresses critical geotechnical, hydraulic and structural stability aspects, presenting significant contributions to the field of dam engineering by advancing the understanding of the interaction between RCC layers and adjacent slopes under operational conditions. This research employs real-time, full-scale numerical modelling using "Plaxis 2D," with a particular focus on arch behavior. The study bridges a gap by integrating hydrostatic and hydrodynamic pressures measured during the initial filling phase into the model, offering insights into the dam’s behavior under both static and dynamic conditions. Monitoring of RCC layers across three elevations prior to operation revealed minimal displacements, with a maximum recorded displacement of 1 mm in the critical interaction zone between the RCC and the left bank slope. Post-operation analysis demonstrated uniform deformation across elevations, with a negligible 1 mm variance, confirming the homogeneity of RCC stiffness—an essential factor for structural stability. The safety factor (FoS) analysis confirmed stability under static conditions, but highlighted vulnerabilities under seismic conditions, underscoring the need for enhanced resilience measures. This work extends findings from previous studies, particularly by validating the importance of a seismic belt at the foundation, through detailed numerical analysis and comparison with real-time monitoring data from PDL pendulums.