Journal of Science and Transport Technology

Influence of Graphene Oxide (GO) and Fly Ash (FA) on the workability and mechanical properties of self-compacting concrete

2025-03-24T03:00:58+00:00

Self-compacting concrete (SCC) is an advanced material for complex construction applications requiring accurate formwork, high reinforcement density, and superior surface finishes. The ability to flow and consolidate under its weight eliminates the need for mechanical vibration, thereby improving construction efficiency, reducing labor costs, and ensuring greater homogeneity. SCC is widely employed in underground structures, high-rise buildings, and load-bearing elements where strength, durability, and workability are critical. Graphene oxide (GO) and fly ash (FA) enhance SCC's performance. GO enhances compressive strength, crack resistance, and durability through microstructural refinement and crack propagation inhibition, while FA improves workability, reduces water demand, and increases durability by lowering permeability and shrinkage. The partnership between GO and FA meets SCC performance standards and enhances its applicability in high-performance and sustainable construction. This study examined the effects of GO content (0% and 0.03% by binder weight) and FA content (15%, 25%, and 35%) on the workability and mechanical qualities of SCC. A series of studies were performed to assess the workability features of SCC, together with two principal mechanical properties: compressive strength and flexural tensile strength at 28 days. The findings demonstrate that while an increase in GO content diminishes flowability owing to its elevated specific surface area, the appropriate incorporation of FA mitigates this effect, leading to a combination with enhanced mechanical properties. SCC with 0.03% GO and 25% FA attained the maximum compressive strength at 28 days.

Optimizing Solar Energy Systems: Enhancing Photovoltaic Efficiency through Mirror Integration and Parameter Optimization

2025-06-09T07:18:55+00:00

This study presents a geometric optimization approach to enhance photovoltaic PV efficiency by integrating a ground-mounted mirror. The method aims to identify optimal values for the panel tilt angle β, mirror tilt angle α, and mirror length L that maximize reflected irradiance onto the PV surface without obstructing direct sunlight. A mathematical expression is developed from geometric constraints to define the optimal mirror configuration. The convergence history of the numerical solution is analyzed, and a sensitivity study on design variables is provided. An energy estimation based on clear-sky conditions in Khouribga shows that the optimized mirror configuration can increase annual irradiance from 1680 kWh/m² (no mirror) to 1895 kWh/m². The optimal setup is achieved with β = 30°, α = 15.1°, and L = 2.45 m. The proposed method offers a simple, accessible design tool suitable for practical use without the need for complex simulation software.

An application of the response surface method to structural optimization problems

2025-04-12T13:32:04+00:00

Structural problems are commonly analyzed using implicit solvers, such as the finite element method (FEM), which often demand manual processes and are computationally intensive, especially for structural optimization problems that involve multiple FEM evaluations to identify the optimal design solutions. In addition, implementing optimization algorithms also demands significant expertise for accurate application. This study proposes a simple yet efficient procedure to address structural optimization problems by transforming the implicit analysis into explicit performance functions using the response surface method and simulating the space of input variables by random samples. The explicit performance functions enable quick evaluations for all generated samples of inputs, and a search routine is developed to identify optimal solutions efficiently. The proposed procedure is validated through three case studies, demonstrating its ability to achieve accurate solutions within minutes of analysis.

Enhancing Seismic Performance of Reinforced Concrete Exterior Joints with Ultra-high-performance Steel Fiber Reinforced Concrete: A Parametric Finite Element Study

2025-03-25T15:20:43+00:00

Beam-column joints are vital to the stability and performance of reinforced concrete (RC) frame structures, particularly under seismic conditions. Understanding their stress-strain behavior is crucial for evaluating their capacity and ductility. However, performing detailed experimental studies with numerous specimens is often impractical due to significant costs and time constraints. As a result, finite element (FE) analysis, supported by tools like ABAQUS, has become a preferred approach for studying joint behavior effectively. This study employs the finite element method (FEM) to analyze exterior beam-column joints designed for high ductility (DCH) and enhanced with ultra-high-performance steel fiber reinforced concrete (UHPSFRC). The FE results are validated against experimental data through comparisons of load-displacement responses, failure patterns, and reinforcement strain progression. Furthermore, the research examines the effects of parameters like UHPSFRC strengthening length, axial column load, and steel fiber content on joint tensile stress, providing insights into optimizing seismic performance.

Mapping Cadmium Contamination Potential in Surface Soil for Civil Engineering Applications: A Comparative Study of Machine Learning and Deep Learning Models in the Gianh River Basin, Vietnam

2025-04-19T15:41:06+00:00

Cadmium (Cd) is a toxic heavy metal with significant environmental and human health risks, particularly when accumulated in surface soils. Its presence reduces soil fertility, disrupts microbial ecosystems, and poses long-term ecological threats. This study explores the application of artificial intelligence (AI) models for mapping the potential distribution of Cd contamination in surface soils within the Gianh River Basin, Quang Binh Province, Vietnam. Four machine learning (ML) models Logistic Regression (LR), Radial Basis Function Network (RBFN), Random Forest (RF), and Support Vector Machine (SVM) and four deep learning (DL) model variants (DNN-Opt1 to DNN-Opt4) were developed and compared. The DNN variants differ based on the configuration of hidden layers and neuron counts.

A total of 100 topsoil samples were collected and classified using the Geoaccumulation Index (Igeo), serving as the target variable for supervised learning. Thirteen conditioning factors were used as input variables, including Elevation, Soil Type, Slope, Curvature, proximity to roads and rivers, and seven Landsat 8 spectral bands. The dataset was divided into training (70%) and testing (30%) subsets. Model performance was evaluated using multiple metrics, including the area under the ROC curve (AUC), accuracy (ACC), Kappa coefficient, root mean square error (RMSE), and confusion matrix.

Among the tested models, the DNN-Opt2 variant demonstrated the highest predictive performance with AUC = 0.858, ACC = 73.33%, Kappa = 0.47, and RMSE = 0.45. The resulting contamination potential map, particularly that derived from the RBFN model, categorized the region into five contamination risk levels: very low, low, moderate, high, and very high. This spatial information is critical not only for environmental management but also for assessing risks to groundwater quality and the structural integrity of buildings located in high-risk zones. The study demonstrates the efficacy of deep learning in enhancing predictive accuracy for heavy metal contamination mapping and underscores its practical relevance in civil and environmental engineering applications.

Pre-stressed Ultra High Performance Concrete Slab Subjected to Blast Impact - A numerical study

2025-05-11T12:12:40+00:00

The purpose of this article is to study the blast resistance capacity of Pre-stressed Ultra High Performance Concrete slab. Three-dimensional numerical models, including the explosion source, Ultra High Performance Concrete (UHPC) slab reinforced by pre-stressed tendons and steel bars under boundary conditions, are developed to simulate the full-scale behavior of the structure under blast load. The proposed simulation model is validated through comparison with the results of a prior experimental study. Chosen explosions with the stand-off distance and TNT charge weight in two cases of close-in and near-field regime. The analysis results are compared to the UHPC slab reinforced by steel bars and High Strength Concrete (HSC), with a focus on blast resistance capacity and the level of damage reduction in the structure. The results of the analysis indicate that HSC slabs sustain a 70% higher level of severe damage compared to UHPC slabs reinforced with pre-stressed tendons. The dynamic increase factor of UHPC is determined to be 32% larger than HSC. Concerning UHPC slabs, while the pre-stressed tendon serves as crucial in decreasing deflection of slabs under blast loads, its influence on altering the damage level of the structure is less pronounced.

Investigation of Cable Tension in Cable-Stayed Bridges Through Field Measurements and Numerical Simulation

2025-05-05T03:49:56+00:00

This study investigates cable tension forces in a one-plane cable-stayed bridge in Vietnam using field measurements and numerical simulation. Cable forces obtained from the Finite element method (FEM) are compared with design values and field-measured data from lift-off and vibration-based method. Results show that field-measured forces generally deviate within 7% of the design values, confirming their reliability. Both measurement methods effectively capture cable force variations, with low tension in long cables and high tension in shorter ones. Numerical simulations accurately represent cable rigidity, with frequency discrepancies remaining below 3%. However, larger errors of 12% to 15% occur in shorter cables near the tower, while longer cables closely align with design values within 3%. Despite these differences, simulation-based preliminary analysis is acceptable for minimizing field measurements and serves as a valuable reference for structural assessments in service stage.

Data-driven approach in Predicting Truck Arrival Time in logistics: A Field study of India

2025-06-19T08:19:56+00:00

This study investigates the application of the Particle Swarm Optimization-tuned Gradient Boosting (GB-PSO) model for predicting truck arrival times. The proposed model incorporates optimized hyperparameters to enhance predictive performance, as measured by the coefficient of determination (R) and Root Mean Square Error (RMSE). Using a real-world logistics dataset, GB-PSO outperforms conventional Gradient Boosting (GB) and expected travel time estimations, achieving a higher R value and lower RMSE across training and testing datasets. The analysis of SHAP values highlights the dominant influence of transportation distance on model predictions. These findings validate the effectiveness of GB-PSO in practical logistics optimization, reducing error and improving reliability in time-sensitive transportation systems.