Steel corrosion in reinforced concrete (RC) structures is a major concern in civil engineering, as it directly affects the safety, serviceability, and lifespan of infrastructures. This study focuses on understanding how corrosion-induced deterioration in steel reinforcement impacts the structural performance of concrete beams. In particular, it investigates how different levels of reinforcement loss influence the load-bearing capacity and failure mechanisms of RC beams under combined bending and shear. To achieve this, a numerical approach was adopted using the ABAQUS finite element software. Three beam models were developed, each representing a different degree of corrosion: intact reinforcement, 20% loss, and 25% loss in cross-sectional area. The models were designed to closely simulate the physical behavior of corroded beams, incorporating nonlinear material properties, bond degradation, and failure criteria. The findings reveal a clear decline in structural performance as corrosion severity increases. Beams with higher reinforcement loss exhibited reduced ultimate loads, increased deflections, and altered failure modes - particularly in shear-dominated regions. The simulation results aligned well with available experimental data, demonstrating the accuracy and reliability of the modeling approach. This research highlights the importance of early detection and quantification of corrosion damage in RC structures. By employing advanced finite element tools like ABAQUS, engineers can better predict structural degradation, evaluate safety margins, and plan timely interventions. The study provides valuable insights into infrastructure asset management and supports the development of effective maintenance strategies aimed at extending the service life of aging concrete structures exposed to aggressive environments.

KEYWORDS

Reinforcement, Deflection, Numerical, Analysis, ABAQUS, FE Modelling

Cite This Paper in IEEE or APA Citation Styles

(a). IEEE Format:

[1] Bashir Saleh , "Numerical Evaluation of Corrosion-Induced Degradation in Reinforced Concrete Beams Using Finite Element Analysis in ABAQUS," Civil Engineering and Architecture, Vol. 13, No. 5, pp. 3967 - 3974, 2025. DOI: 10.13189/cea.2025.130536.

(b). APA Format:

Bashir Saleh (2025). Numerical Evaluation of Corrosion-Induced Degradation in Reinforced Concrete Beams Using Finite Element Analysis in ABAQUS. Civil Engineering and Architecture, 13(5), 3967 - 3974. DOI: 10.13189/cea.2025.130536.

Marine water jet propulsion systems require optimized nozzle and duct geometries to maximize thrust and efficiency, yet design improvements are often limited by the lack of integrated hydrodynamic analysis. This study aimed to design an efficient marine water jet and evaluate its hydrodynamic performance through combined CAD modeling and CFD simulation. A three-dimensional model of the propulsion system was developed in SolidWorks, incorporating optimized nozzle and internal duct geometry. Computational Fluid Dynamics (CFD) simulations were performed to analyze velocity fields, pressure distributions, and thrust output under defined fluid properties and boundary conditions. Both the complete jet system (including a free-surface water interface) and a simplified nozzle-only configuration was examined. Simulations revealed clear correlations between geometric parameters and performance, identifying configurations that improved energy conversion and reduced wall friction. The optimized design achieved higher thrust efficiency compared to baseline geometries. Integrated CAD–CFD analysis provides a robust framework for marine water jet design, enabling targeted geometry refinements that enhance hydrodynamic performance. The findings support future development of high-efficiency propulsion systems for marine applications. 

The study investigates the mechanical performance of isolated steel–concrete composite joints subjected to both positive and negative bending moments through an advanced finite element modeling approach. The research addresses a critical gap in existing design codes, which often overlook the nonlinear interaction between steel and concrete components in semi-rigid connections. The primary aim is to evaluate the joint behavior under realistic service and extreme loading conditions, offering a validated numerical framework that can support future enhancements in structural design standards. A three-dimensional nonlinear finite element model was developed using ABAQUS, integrating the Concrete Damaged Plasticity (CDP) model to capture concrete cracking, steel yielding, and interfacial slip. Experimental data from composite specimens tested in Warsaw University of Technology and replicated at the Libyan Concrete Materials Laboratory were used for calibration and validation. Parametric analyses considered variations in end-plate geometry, bolt arrangements, and column dimensions to assess their influence on joint stiffness, ductility, and load transfer mechanisms. The results demonstrated excellent agreement between experimental and numerical findings, with a correlation coefficient exceeding 0.95 for moment–rotation relationships. Among the tested configurations, the CSB7 joint (eight-bolt symmetric extended end plate) exhibited the highest performance, achieving sagging and hogging moments of 144 kNm and 269 kNm, respectively. These results highlight the joint's superior ductility and robustness under nonlinear loading, indicating its suitability for structures exposed to exceptional conditions such as seismic or fire events. The research contributes to the ongoing development of Eurocode-based composite joint design, emphasizing the need for nonlinear performance criteria and predictive modeling tools. While the study was limited by the absence of cyclic and high-temperature testing, its implications extend to safer, more resilient, and sustainable structural systems, promoting optimized connection design and improved global design standards.

KEYWORDS

Numerical Modeling, Steel, Concrete, Composite, ABAQUS, Finite Element Method

Asphalt roads are one of the most important elements of infrastructure in any country, as they play a vital role in achieving comprehensive development and economic growth. They provide safe and reliable means of transportation for citizens and companies, which develops trade, contributes to the development of industry, and expands the investment base. In addition, asphalt roads contribute to enhancing tourism and developing the infrastructure of cities and rural areas. Therefore, it is necessary to invest heavily in the construction and maintenance of asphalt roads to ensure economic growth and social stability. The Superpave system is an important development in the field of paving, as it contributes to improving the design of hot asphalt mixes and evaluating the performance of paving facilities. This system relies on precise standards to evaluate the quality of asphalt mixes, which leads to improving the durability of the pavement and its efficiency in withstanding different traffic loads. This system is also the result of field and laboratory research and studies included in the Strategic Highway Research Program in the United States of America. Due to the importance of this system, we present this study to examine previous studies on the classification of asphalt binders in the high-performance asphalt paving system (Superpave) and to identify the significance of classifying the binder in this system.

Ischemic brain stroke is the most prevalent type of stroke caused by the occlusion of blood vessels via thrombi or blockages and is the second most common cause of mortality globally, after ischemic heart disease. To improve patient outcomes, ischemic stroke must be diagnosed timely and precise. This paper introduces a novel approach toward ischemic stroke detection from computed tomography (CT) images by integrating deep learning and optimization techniques for feature extraction and selection. The VGG16 model is utilized to extract high-dimensional spatially rich features that efficaciously capture the intricate texture and spatial patterns within the CT scans. To optimize these features, a genetic algorithm (GA) is leveraged to select the most discriminative subset and reduce redundancy. The new method was developed and evaluated on a unique, first-hand dataset gathered from a specialized private hospital in Palestine. The findings show that the suggested combined technique VGG16-GA highly enhances the performance of all classifiers. Notably, the VGG16-GA-XGB model attained superior outcomes, with an accuracy of 98.89%, precision of 98.85%, recall of 98.93%, and an F1-score of 98.92%.

Resonance occurs when the operating frequency of a system aligns with its natural frequency, resulting in amplified vibration amplitudes. To prevent potential damage and ensure optimal performance of a compressor's base frame at Souq Al-Khamis Cement Factory, researchers found the resonance in has been occurred when both the natural frequencies and rotating frequency were overlapped. Resonant Vibration in the base frames arises when the rotating vibration frequency aligns with the frame’s natural modes that leads to structural instability, fault unplanned shutdowns and production losses. This study analyzes resonant vibration in a cement factory compressor base frame and proposes a redesign using finite element methods to mitigate this issue. Four distinct modifications were made to the base frame on its shape, weight and boundary conditions: the first introduces fixed points to enhance rigidity, the second adds supports for increased stability, the third incorporates elements to improve durability, and the fourth enhances the thickness of the compressor. The results indicate that the redesigned configuration most effectively mitigates resonance and improves the system's natural frequency response.