Rotating machinery is a critical component in mechanical systems, widely used across industrial applications. Due to time-varying speed conditions and complex operating environments, it is highly prone to various failures. Without timely diagnosis and maintenance, such failures can lead to significant performance degradation or catastrophic outcomes. To address the challenges posed by non-stationary operating conditions and vibration signals, researchers have developed diverse fault diagnosis methods, including advanced non-stationary signal processing techniques and data-driven approaches. Among these, generalized demodulation (GD) has demonstrated particular effectiveness in extracting fault-related features from complex signals. This paper provides a comprehensive review of GD-based fault diagnosis methods for rotating machinery. It revisits the fundamental concepts and theoretical basis of GD, analyzes the limitations of traditional approaches, and systematically compares GD with other widely used methods. Furthermore, existing GD-based techniques are categorized into speed-dependent and speed-independent methods based on their reliance on rotational speed, with representative studies and applications discussed. Finally, future research directions and current challenges in GD-based diagnosis are outlined, offering valuable insights for researchers and practitioners in the field.

Planetary gear systems in industrial equipment, characterized by highly integrated structures, non-stationary operating conditions, and heavy-load characteristics, result in significant attenuation of fault-related vibration features, severely limiting the effectiveness of fault diagnosis. Traditional studies often use periodic modulation terms, such as the Hanning window function, to approximate the spatiotemporal distribution characteristics of meshing forces. Although these methods can effectively extract global vibration features of healthy gears (such as meshing frequency and its harmonics), they have significant limitations and are unable to analyze the nonlinear diffusion process of fault impacts through complex paths, resulting in distortion in the phase delay and amplitude attenuation patterns of fault impacts. To address these issues, this study investigates the vibration transmission characteristics of a planetary gearbox through rigid-flexible coupling simulation analysis, focusing on the transmission delay effects of impact responses to the vibration sensors at different housing locations. The study qualitatively clarifies the intrinsic relationship between transmission delay characteristics of the gear-sensor spatial relationship, providing a theoretical foundation for accurate analysis of vibration signals in planetary gear sets. The research highlights the significant spatiotemporal characteristics of the vibration responses when the fault collisions occur at different locations during the rotation and revolution of planet gears.

Early and precise diagnosis of brain tumors is essential for successful treatment planning and improved patient outcomes. This paper introduces a novel hybrid deep model that incorporates DenseNet121, a convolutional neural network (CNN), and the Swin Transformer, a vision transformer model, by feature-level fusion to classify brain tumors from magnetic resonance imaging (MRI) scans. The suggested method provides a more discriminative and better representation by uniting the global context capability of the Transformer model with the local feature extraction capability of the CNN model. The suggested method was trained and assessed on a publicly available brain MRI dataset of four classes: glioma, meningioma, pituitary tumor, and no tumor. Experimental results indicate that the proposed approach outperforms many baseline models including VGG16, MobileNetV2, and AlexNet with an accuracy of 99.39%, precision of 99.36%, recall of 99.34%, and F1-score of 99.35%. Grad-CAM was utilized to visualize class-discriminative regions in the MRI scans to enhance interpretability, hence validating the model's emphasis on tumor-relevant regions. These outcomes prove the efficacy of coupling Transformer and CNN architectures in obtaining accurate and interpretable brain tumor classification from MRI scans.

 Fuzzy Neural Networks (FNNs) enhance conventional Artificial Neural Networks (ANNs) by incorporating fuzzy membership functions, which enable the handling of uncertainty, ambiguity, and imprecise information. While Fuzzy Backpropagation Neural Networks (FBNNs) improve classification performance across noisy datasets, the effectiveness of fuzzification heavily depends on the proper tuning of membership function parameters—typically optimized manually. This paper presents a novel Artificial Immune System framework for optimizing Fuzzy Backpropagation Neural Networks used in the classification of biological image data. The approach integrates a fuzzy min–max fuzzification layer with a feed-forward backpropagation network and applies an optimization version of an Artificial Immune Network model, derived from opt-aiNet, to tune trapezoidal membership functions. Experimental results confirm that the proposed immune-driven optimization is an effective technique for enhancing FBNN robustness and generalization.

Abstract— This study examines the current state of conformity assessment (CA) in Libya, with a specific focus on the cement manufacturing sector as a case study. Conformity assessment, encompassing testing, inspection, certification, and accreditation, plays a critical role in ensuring product quality, safety, and market access. Libya faces challenges in implementing effective CA practices, hindering its economic diversification. This research evaluates stakeholder awareness, infrastructure adequacy, legal/institutional frameworks, and alignment with international standards using a mixed-methods approach. A stakeholder survey (N=54) and qualitative analysis of Libyan legal and regulatory documents were employed. Key findings reveal nominal CA awareness (82.69%), yet practical implementation gaps exist due to inadequate infrastructure (18.8% of respondents citing this as an obstacle), weak enforcement (18.8%), and limited technical expertise. The cement sector showed low Quality Management Systems (QMS) adoption (48%) and inconsistent adherence to the Libyan Portland Cement Standard LNS 340:2009. While support for aligning with international standards is strong (average rating 4.04/5), obstacles like lack of awareness (31.1%) and technical expertise (30.2%) impede progress. The study proposes actionable recommendations to strengthen Libya’s CA system, including developing a unified national framework, investing in accredited laboratories, and promoting collaboration.

Keywords— Conformity Assessment, Cement Industry, Libya, Quality Standards, Economic Diversification, Stakeholder Awareness.

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. 

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.

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.