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2007 research outputs found
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Innovative Advancements in Construction: The Sustainable Promise of Aerated Concrete Incorporating Fly Ash and River Sand
No full textAerated Concrete, or lightweight concrete, is primarily used in construction work for non-load-bearing structures and is typically produced with cement as a primary binding material. Cement production accounts for 7 to 8% of the environmental CO2 emissions. Furthermore, the dumping of industrial waste and the consumption of aggregates disrupt the environment and ecosystem. This research aims at developing sustainable AC by partially substituting cement with FA and hill sand with IRS while maintaining the fundamental properties of aerated concrete. The study was conducted to investigate the physical and chemical properties of the materials and the physical and mechanical properties of aerated concrete. Variations of fly ash, i.e., 10%-70%, were incorporated as a CRM to get optimum FA usage in terms of density and compressive strength. Optimum FA was incorporated as CRM and IRS as sand replacement, used in four variations, i.e., 10% - 25%. Specimens were cured using the conventional curing method and autoclaving for NAAC and AAC, considering both manufacturing processes, CO2 emissions and time limitations in respective curing methods. Conventional curing was performed at 7, 14, and 28 days, while autoclaving was performed at various pressures, i.e., 0.5 bar, 1 bar, and 1.5 bar. The optimum compressive strength of AAC and NAAC was achieved when 20% of the IRS and 50% of FA were replaced with hill sand and cement, respectively, for both AAC and NAAC. Additionally, approximately 32% and 39.3% of CO2 emissions were reduced with 50% FA and 20% river sand replacement with cement for AAC and NAAC specimens. Although AAC demonstrated slightly lower water absorption due to densification through autoclaving, NAAC performed satisfactorily in offering a more cost-effective and energy-efficient alternative
Assessment of Soil Shear Strength Parameters: Insights from Direct Shear and Direct Simple Shear Testing
No full textThe direct shear test is widely used to determine shear strength parameters ( ) of soil. However, its validity has been questioned due to several issues, such as uneven stress distribution, the creation of a predetermined failure plane, lateral constraints, difficulties in controlling drainage conditions, and limitations in measuring pore water pressure, which is essential for understanding soil behaviour under different conditions over time. This study addresses these concerns by comparing the shear strength parameters obtained from a direct shear test (DST) and a direct, simple shear test (DSST). To further explore these issues, a fully automated universal shear device was used to perform shear tests on clay, sand, and composite soil (clay + sand), covering both consolidated and shear phases of DST and DSST. Specimens were fabricated at their optimal moisture content, and the composite soil was developed by mixing clay with sand in proportions of 10%, 25%, 50%, and 75% of the mass of sand. This research aims to clarify the relationship between these two testing methodologies through comprehensive testing and to enhance the knowledge of the principal mechanism of the 2 tests. The findings revealed that the DST yielded higher shear strength values than the DSST results. It was also observed that the friction angle of sand specimens generally decreased with the addition of clay for both tests. Additionally, the the kaolinite soil in DST and DSST, decreased in the sand as the clay contents increased
Effect of Infill Wall Opening Ratio on the Mechanical Characteristics of Reinforced Concrete Frames
No full textThis study investigated the influence of infill wall (IW) opening ratios on the mechanical performance of reinforced concrete (RC) frames using a novel numerical model. The proposed model incorporated stiffness degradation and a nonlinear "Gap Element" to simulate the interaction between RC frames and IWs under seismic loading. A 3D finite element model was developed in SAP2000 and calibrated using validated experimental data. Parameters such as IW thickness, opening ratio (0–100%), and opening position (symmetric, asymmetric, corner) were systematically varied to assess their effects on lateral displacement , fundamental period , shear force , and bending moment . The results indicated that increasing the opening ratio significantly reduces frame stiffness, especially beyond 40%, and leads to substantial increases in displacement. Corner openings were found to have the most detrimental impact, while thicker walls (≥220mm) can partially mitigate stiffness loss. However, at ratios above 60%, even thick IWs failed to preserve structural performance. Based on these findings, a limit of 40% opening ratio was recommended for design purposes, and reinforcement was advised for higher ratios. The study provides a practical framework for optimizing the seismic and structural design of RC frames with openings in IWs, contributing new thresholds and modeling strategies for improved performance
Investigating the Influence of Functional Units on the Life Cycle Assessment of Asphalt Pavements
No full textThe Life Cycle Assessment (LCA) of asphalt pavements is an essential tool for reducing environmental impacts. The definition of the functional unit (FU) within LCA can significantly influence the results, affecting the assessment of greenhouse gas (GHG) emissions and, consequently, the selection of asphalt mixtures. In this context, this study aims to analyze the impact of different functional units on the selection of asphalt mixtures for road pavements, considering the phases of raw material extraction, material production, mixing, and construction. To this end, the mechanical behavior of two distinct asphalt mixtures was evaluated under two different loading conditions, and their contributions to climate change were assessed using three functional units: t CO₂ eq/km of roadway, kg CO₂ eq/t of HMA, and kg CO₂ eq/m³ of HMA. The results indicated that asphalt mixtures with a higher resilient modulus require thinner pavement layers, leading to lower GHG emissions. However, when asphalt mixtures are analyzed individually and compared, no clear pattern in GHG emissions is observed, reflecting the specific characteristics of each production process. Additionally, it was found that the environmental impact varied according to the adopted functional unit, demonstrating that this choice can significantly influence decision-making regarding the selection of asphalt mixtures in terms of their contributions to climate change. It was concluded that the selection of the FU in pavement LCA should be aligned with the study's objective and the context of the analysis, as an inadequate choice may compromise the selection of asphalt mixtures
Geodynamic Processes Monitoring of Subway Infrastructure Using Geodetic and Remote Sensing Methods
No full textThis article examines the development of a methodology for monitoring geodynamic processes during the construction of the Almaty metro using an integrated approach that incorporates geodetic methods, laser scanning, and aerospace technologies. The study aims to enhance the safety of underground structures in the context of complex engineering-geological conditions and high-density urban environments. Monitoring was conducted at the "Saryarka" and "Bauyrzhan Momyshuly" stations, employing underground polygonometry, aerial surveys with unmanned aerial vehicles (DJI Mavic 3 multispectral), laser scanning (Faro Focus 3D X), and finite element numerical modeling (PHASE 2, AutoCAD Civil 3D). The geodetic work covered a 3201-meter section with the installation of 34 benchmarks, ensuring a relative measurement error of no more than 1:30,000. Laser scanning achieved an average point cloud density of 7 mm, enabling the creation of precise 3D tunnel models, identification of deviations from the design axis, and determination of critical stress zones. The study revealed that at a depth of 32.28 m, the maximum vertical stress reached 11.2 MPa, and horizontal stress was 2.7 MPa. At a depth of 19.58 m, the vertical stress reached 10.5 MPa, while the horizontal stress was 2.47 MPa. The maximum concentration of stresses in critical zones reached 20 MPa. The use of UAVs and aerospace technologies facilitated the creation of a highly accurate digital terrain model and the identification of potential deformation zones. The findings confirm the necessity of regular monitoring in dense urban and seismically active areas and demonstrate the potential of integrating modern technologies to improve the precision and efficiency of geodynamic assessments. The proposed methodology can be applied not only to metro construction but also to other underground structures, including mining industry facilities, both in Kazakhstan and internationally
Predicting Soil Electrical Resistivity Using Geotechnical Properties and Artificial Neural Networks
No full textThis study investigates the influence of key geotechnical parameters—water content, dry density, and plasticity index—on soil electrical resistivity, with the goal of improving prediction accuracy for substation grounding system design. A dataset comprising 150 laboratory test results was compiled from soil samples collected at three substations in Thailand, representing diverse moisture conditions to reflect field variability. Two modeling approaches were applied: multiple regression (MR) and artificial neural networks (ANN), evaluated using the coefficient of determination (R²) and root mean square error (RMSE). The MR models achieved relatively strong correlations, with R² values up to 0.8281; however, their higher RMSE values indicated limited precision under variable conditions. In contrast, the ANN models, particularly those incorporating the plasticity index, demonstrated superior performance, achieving lower RMSE values—down to 0.057—highlighting their ability to capture complex nonlinear relationships. In comparison to prior studies that often relied on single-variable models or uniform soil datasets, this research adopts a more integrative and generalizable framework. By incorporating multiple soil parameters into the ANN model and validating against a diverse dataset, the study offers practical insights for engineering applications. The findings are particularly valuable in tropical regions where soil moisture variation significantly impacts resistivity and grounding system performance
Three-Dimensional Finite Element Evaluations of H-Steel Beams Strengthened with Various Types of Steel Stiffeners
No full textThree-dimensional finite element analyses were carried out to assess the impact of various types of lateral stiffeners on the response of steel beams. Hot-rolled simply supported H-steel beams were modeled in Abaqus and strengthened with centrally located vertical, V-shaped, inverted V-shaped, single X-shaped, or doubled X-shaped stiffeners. All these stiffeners possess a similar quantity of steel by varying the length and thickness of the stiffeners. The behavior of beams was studied in the elastic phase, hardening phase, necking phase, and failure. The yield stress, ultimate load, deflection value, and hardening in the three phases were also examined. It has been found that the findings indicate that altering the configuration of the stiffener, while maintaining its location and steel volume, can influence the response of the strengthened beam either favorably or adversely. Two stiffeners raised the yield load by 9.6%, the ultimate load by 10.8%, and elastic storage energy by 70% above the reference beam. One kind of stiffener increases in the plastic region, two types drop somewhat, and two others decrease significantly. The necking region shows a rise of 237% in one threshold and 36% to 90% for the other beams compared to the reference beam. Furthermore, the software provides a definitive indication of the kind of stiffener and the degree of its advantage, while simultaneously revealing the type of stiffener that is not advantageous
Predicting Speeding Behavior of Long-Haul Freight Truck Drivers Using Machine Learning Models
No full textThe behavior of long-haul truck drivers is shaped by the weak enforcement of working-hour rules, tight deadlines, and heavy workloads. Over-dimensioning and overloading practices further increase risks by forcing drivers to handle excessive loads and work for prolonged periods. This study predicts speeding behavior among long-haul freight truck drivers using statistical and machine learning models. Data was collected from 370 respondents at two weigh stations in South Sulawesi, Indonesia, covering eight socio-demographic, economic, and operational predictors. Three models were tested: Binary Logistic Regression (BLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The dataset was balanced and split into 70% training and 30% testing, with performance assessed using accuracy, recall, F1-score, and AUROC. XGBoost delivered the best results, achieving 97.3% accuracy, 93.2% recall, a 96.4% F1-score, and a perfect AUROC of 1.000. RF also showed strong performance with 94.05% accuracy and an AUROC of 0.973, while BLR served as a relevant baseline despite weaker predictions. Key predictors of speeding violations were daily sleep duration, monthly income, and driving experience. This study demonstrates how machine learning can be effectively integrated alongside transportation data under imbalanced conditions, providing evidence-based insights to strengthen freight transport safety
A Model for the Reduction of Flood Peak Discharge (ΔQp) Due to the Retarding Basin
No full textThis research aims to develop a model for flood peak discharge reduction (ΔQp) through the placement of retarding basins within a watershed system, represented by the area ratio of the controlled watershed (RAk) and the maximum storage capacity of the retarding basin (Vk). The area ratio of the controlled watershed (RAk) is defined as the ratio between the catchment area of the retarding basin and the total watershed area (Ak/A). The methodology involves simulating various retarding basin placements (RAk) and different maximum storage capacities (Vk) for several flood return periods (QT). This study was conducted in the urban agglomeration area of Wonosari, Gunungkidul Regency, Special Region of Yogyakarta, Indonesia. The placement and utilization of retarding basins result in varying levels of flood peak discharge reduction (ΔQp) at the downstream control point (Taman Pancuran), depending on the maximum storage capacity of the retarding basin (Vk) and its placement within the watershed (RAk). The resulting empirical equations for flood peak discharge reduction (ΔQp) using the retarding basin method are as follows: ΔQp = 0.105654 − 0.014593 Vk − 0.029251 RAk + 0.011089 QT for Vk values in the range (V1–V4) = 36.4–208.8 × 10³ m³, and ΔQp = 1.374989 − 0.003702 Vk − 0.338381 RAk + 0.004773 QT for Vk values in the range (V4–V200) = 136.2–7039.1 × 10³ m³. An observed anomaly was identified, where ΔQp became positive at small values of Vk and RAk, indicating an increase in peak discharge (Qp)
Nonlinear Inelastic Local Buckling Behavior of Steel Columns Subjected to Axial Compression
No full textThis study develops a displacement-based finite element approach using one-element modeling to analyze the second-order inelastic local buckling of steel columns under axial compression. To account for local buckling, two new stress-strain relationships are proposed for steel using an energy method and assumptions from previous studies for both compact and slender cross-sections. Stress-strain curves of post-buckling regimes are modeled as nonlinear curves. Both geometric and material nonlinearity are considered in the buckling analysis. The effects of geometric nonlinearity are traced through stability functions. The tangent stiffness of steel members is continuously updated during the nonlinear analysis by updating the fiber behavior at monitoring cross-sections using the Gauss-Lobatto integration rule. The proposed stress-strain relationships accurately predict the ultimate strength, elastic, and inelastic local buckling behaviors of steel columns under axial compression, compared with ABAQUS and previous studies. The model accurately predicts elastic, inelastic, and ultimate strength behaviors, with post-buckling responses closely matching ABAQUS results (e.g. 0.881 (proposed with residual stress), 1.008 (proposed without residual stress) vs. 0.948 (ABAQUS) load ratio for HB3 specimen). This approach offers significant computational efficiency (~1.0 sec vs. 20–30 min for ABAQUS) and introduces adjustable constitutive models, enhancing practical design applications for steel structures. This study proves that the effects of residual stress on the local buckling cannot be ignored in the case of slender sections, since the differences of the ultimate load (with and without the initial residual stress) are equal to 63.3% for the HI4 specimen and 43.2% for the HS40-SH(B) specimen