Al-Rafidain Journal of Engineering Sciences
Al-Rafidain Journal of Engineering Sciences (RJES) is a double-blind, peer-reviewed, open-access journal founded in 2023. RJES is a semiannual journal published by Al-Rafidain University intended to be interesting and useful for researchers and practitioners in academic and industrial communities. RJES is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering sciences. While it encourages a broad spectrum of contributions in the engineering sciences, its core interest lies in issues concerning material modeling and response. RJES invites research papers that encompass conceptual analysis, design implementation, and performance evaluation. All papers submitted for possible publication at RJES are subject to the peer review process. Submissions are accepted only in the English language. The journal accepts research articles, review articles, case reports, and selected conference articles. The editors reserve the right to reject papers without sending them out for review. The RJES is committed to upholding publication ethics (COPE) and obligates the authors to avoid any plagiarism or illegal citation. The subject areas include the following fields: Engineering and Technology (General) Chemical Engineering Civil and Environmental Engineering Dames and Water Resources Engineering Mechanics, Mechanical Engineering Hydraulic Engineering Electrical and Electronic Engineering Communication Engineering and Information Technology Computer Engineering Mechatronics Engineering and Robotics Architecture and Environmental Design Sustainable Engineering Nano-scale Design Engineering RJES publishes: 1. Origional articles, 2. Review articles. 3. Special issues with selected papers from relevant conferences. © 2023, The Author(s). Published by Al-Rafidain University College. This is an open access journal issued under the license https://creativecommons.org/licenses/by-nc-sa/4.0/ Journal title: Al-Rafidain Journal of Engineering Sciences Journal abbreviation: RJES (Rafidain J. Eng. Sci.) Frequency: 2 issues per year ISSN (Online): 3005-3153 DOI prefix: 10.61268 National Library No. : 2675 (2023) Publisher: Al-Rafidain University Language: English
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
Review on Hydraulic and Geotechnical Mechanisms of Riverbank Roadway Erosion and Collapse
Riverbank erosion is a critical geomorphological process with significant impacts on river systems and adjacent infrastructure. In many regions, transportation networks are constructed near riverbeds, making them susceptible to bank instability and erosion. Hydraulic forces including flow velocity, turbulence, and shear stress play a major role in determining the rate of riverbank erosion. Concurrently, the geotechnical properties of bank materials, such as cohesion, permeability, and soil structure, influence the riverbank’s resistance to erosion and slope failure. The interplay between hydraulic dynamics and soil mechanical properties results in various bank failure mechanisms, including rotational slip, cantilever failure, and block failure. Additionally, traffic loads and human activities can exacerbate the instability of riverbanks adjacent to transportation infrastructure. This paper reviews previous researches on riverbank erosion, soil characteristics, hydraulic flow conditions, and the instability of roads near rivers. The review aims to identify the key factors controlling riverbank erosion and to highlight existing research gaps, particularly regarding the combined effects of hydraulic loading and traffic-induced stresses. A comprehensive understanding of these mechanisms is essential for developing effective strategies to stabilize riverbanks and safeguard transportation infrastructure.
Copyright (c) 2026 Al-Rafidain Journal of Engineering Sciences https://creativecommons.org/licenses/by-nc-sa/4.0/
Traffic Congestion Mitigation on Highways by using the Feedback Strategy
The design of highways in urban areas aims to reduce congestion. When congestion occurs on a highway, it presents a problem that must be solved. The Al-Dawrah Expressway experienced severe congestion, particularly at its entrance, negatively impacting traffic flow. Data was collected via cameras installed along the highway. After calibrating and verifying the data, the ALINE algorithm was used to control traffic flow at highway entrances (Ramp Metering) to congestion mitigation by control of three parameters(flow, occupancy and speed). The results were significant, with four scenarios performed at different time points. The fourth scenario, lasting 10 seconds, showed the highest improvement rate of 20.1%, encompassing increased traffic flow, reduced congestion, decreased critical occupancy and increased speed.
A Review study on foundation above the soil containing voids; challenges and obstacles
Foundations constructed on sandy soils that contain underground voids or cavities often face significant geotechnical challenges. Underground cavities occur as the results of natural causes, manmade causes or sometimes a complex combination of both. Despite considerable research on foundation performance in soluble soils, the effects of cavity geometry specifically, cavity depth and horizontal position relative to foundation centers and load eccentricity on the bearing capacity This paper discusses an extensive survey on the previous investigations the challenges and obstacles on the conducted foundations conducted on soil containing voids footing not fully understood. Various factors were considered effect of void size, depth, void location relative to the foundation. These studies have effectively shown that the presence of voids directly affects soil behavior, leading to reduce shear strength, decrease bearing capacity, and increase settlement, which can cause sudden failures. The greatest impact occured when the voids were located within the critical zone below the foundation, while their effect decreased with increasing depth or horizontal distance.
Phase Change Materials in Cooling Technologies: A Systematic Review of Applications and Studies
Phase Change Materials (PCMs) are substances capable of storing and releasing thermal energy through phase transitions, primarily between solid and liquid states. Due to their high latent heat capacity and nearly constant temperature operation during melting and solidification, PCMs have gained considerable attention for thermal management and energy efficiency applications. This review summarizes recent investigations into PCM-based cooling systems across multiple fields, including building thermal regulation, photovoltaic panels, electronic devices, batteries, and domestic appliances. The integration of PCMs in these systems effectively reduces temperature fluctuations, peak loads, and overall energy consumption, leading to enhanced system performance and environmental benefits. Various organic, inorganic, and eutectic PCMs have been analyzed according to their thermal properties, and selection criteria. Software can simulate the PCMs applications are ANSYS Fluent, EnergyPlus, and Design Builder. The findings highlight that optimized PCM design and integration strategies play a crucial role in sustainable cooling technologies, supporting the global shift toward energy-efficient and low-carbon solutions.
Self-Healing in Asphalt Pavements for Enhanced Road Sustainability: Opportunities and challenges
Self-healing is a characteristic obtained by the asphalt mixture through production or operational conditions, the application of paving, or by modification of the components constituting the asphalt mixture. The asphalt binder is a hydrocarbon material composed of carbon chains that are bonded together to form the asphalt structure. The chemical composition of the asphalt binder can be modified, and the carbon chains enhanced to mitigate the deterioration resulting from the ageing of the asphalt binder and the volatility of its constituents. This is achieved by using an asphalt binder with a softer penetration grade and a high concentration of low molecular weight components, hence reducing volatility-induced ageing. Alternatively, the capability of the asphalt mixture for self-healing can be achieved through the incorporation of components that repair damage and rebuild cracks in the asphalt pavement, such as regeneration agents, steel fibres, or other materials.
Reactive Powder Concrete: Composition, Mechanical Properties, and Structural Applications : A Review
This paper reviews the composition, mechanical properties, and structural applications of Reactive Powder Concrete (RPC), an advanced cementitious material that represents a major leap in concrete technology. Unlike conventional concrete, RPC contains no coarse aggregates. Instead, it relies on a carefully engineered mixture of cement, silica fume, fine sand, superplasticizers, and steel fibers to achieve an ultra-dense microstructure. The result is exceptional mechanical performance, with compressive strengths ranging from 170 to 800 MPa, depending on the curing method and mix design. The review of previous studies reveals that heat treatment significantly enhances strength, with curing at 90°C raising compressive strength from 120 MPa to 180 MPa. Adding steel fibers at volume fractions of 1–2% improves splitting tensile strength by up to 39%, flexural strength by 29%, and impact resistance by nearly 150%. Interestingly, some researchers successfully incorporated crushed coarse aggregate into RPC without reducing compressive strength, contradicting early assumptions that coarse aggregate must be eliminated. Structural applications, including beams, T-beams, and bridge decks, demonstrate superior load capacity, ductility, and crack control. However, RPC remains expensive, requires high cement content, and often needs special curing conditions, limiting its widespread use. This review concludes that while RPC is an outstanding material for high-performance applications, further research is needed to reduce costs and validate long-term durability under real-world conditions.
High-Performance Concretes: A State-of-the-Art Review of Material Behavior, rigid pavement under earthquakes
High performance concrete can substitute conventional concrete in rigid pavement systems in earthquake-prone regions due to its enhanced mechanical properties, such high compressive strength, high flexural strength, and durability. An overview of relevant study on HPC material properties, rigid pavement design and performance, the effects of seismic loading on concrete structures in general, as well as specialized materials that include ultra-high-performance concrete (UHPC), engineered cementitious composites (ECC), and fiber-reinforced versions are presented in this review. Engineered cementitious composites (ECC), fiber-reinforced forms, and so forth. Metrics such loss of energy capacity, crack control ability, load transfer ability, and mechanical–environmental durability are highlighted in the review. Additionally, link slab technology, joint design, and additional cementitious materials for enhancing seismic performance and sustainability are among the subjects addressed. In addition to suggesting future directions for research on seismic optimum of nanomaterials-enabled HPC rigid pavement systems, this report discusses cost, standardizing, and performance data over the service life for HPC rigid pavements.
Spatial Analysis and Performance Evaluation of Hospital Wastewater Treatment Plants Using GIS-Based Kernel Density Estimation and Buffer Analysis: Basrah Governorate
Higher Hospital wastewater (HWW) poses a significant and under-regulated environmental threat in developing regions. This study aimed to evaluate the performance of hospital wastewater treatment plant (HWWTs) in Basrah governorate, Iraq, using a spatially explicit approach to address the critical gap in site-specific environmental assessment. A geospatial database was developed for seven HWWTs, and their pollutant removal efficiencies were quantified. Performance variability was analyzed using Kernel density estimation (KDE) to identify pollution hotspots and multi-distance buffer analysis(500-1500m) was employed to assess environmental risk zones. Results indicated inconsistent treatment performance, with acceptable removal for organic load (BOD/COD) but critically low efficiency for nutrients particularly phosphate (PO43-), which ranged from 20.4% to 64.1%. the spatial analysis revealed district clusters of low-performing facilities, identifying specific hospitals as significant hotspots for effluent discharge. The buffer analysis confirmed a cumulative environmental risk, with potential impacts extending to the Shatt Al-Arab River, the region’s primary water source. The study concludes that the current treatment infrastructure is inadequate for the complex HWW matrix, especially concerning nutrient management. The finding validates GIS-Based spatial analysis as powerful evidence-based tool for environmental authorities to prioritize regulatory enforcement and implement site-specific management strategies in Basrah governorate.
Enhanced Classification of UML Class Diagram Components Using Deep Learning for IT and Engineering Solutions
UML class diagrams are key to object-oriented software development, capturing structural descriptions of software systems. But manual interpretation and analysis of UML diagrams is time-consuming, prone to errors and not scalable, especially during software maintenance and reverse engineering. In this paper, a deep learning model is presented to automate recognition of UML class diagram components by using a Convolutional Neural Network (CNN). The proposed approach does not use text-based features of UML class components and instead uses only visual features to classify UML class components into four classes: Entity, Service, Controller, and Utility. A dataset of real UML class diagrams was labeled for supervised learning. Our model outperformed baseline methods, with a classification accuracy of 92.3% on the test set and generalised well to different diagram types. This paper makes the following contributions: (1) a lightweight CNN-based model for UML component classification, (2) a benchmark labeled dataset, and (3) comparison of visual-only learning with traditional techniques in UML component classification. This research advances the field of Software Engineering, especially Computer-Aided Software Engineering (CASE), by facilitating smart automation in software modeling and documentation tools. The paper presents a feasible and effective roadmap to intelligent software modeling tools and automated documentation systems.
General Concept and Structural Behavior of Reinforced Concrete Wide Beams: A Review
This paper presents a comprehensive review of experimental and numerical studies on the structural behavior of reinforced concrete wide beams, defined by a width-to-effective depth ratio (b/d) of 2 or greater. The synthesis of findings from investigations over the past two decades reveals that wide beams exhibit fundamentally different responses to loading compared to conventional narrow beams, particularly in shear distribution, crack propagation, and failure mechanisms. Key findings indicate that reinforced concrete wide beams demonstrate an inherent extra shear resistance of 30-40% beyond first cracking, a beneficial characteristic underestimated by current design codes including ACI 318 and Eurocode 2. The review confirms that properly designed web reinforcement, including stirrups, hybrid steel-FRP configurations, and polypropylene fibers, substantially enhances shear strength, ductility, and crack control, with fibers capable of completely substituting minimum stirrups. Additionally, horizontal openings introduce stress concentrations requiring special detailing. The findings emphasize the critical need for revised code provisions that account for width effects and modern reinforcement strategies in wide beam applications.
Optimization of Production Throughput and Quality Control Metrics: A Case Study in Home Appliance Assembly
The given research paper is a rather detailed quantitative study of the daily and monthly production rates and defects in the production of home appliances in the General Company of Light Industry, Iraq, in the first quarter of 2026. Three different product lines have been studied in terms of the 10-foot vertical display unit (January), 2-door display unit (February), and 3-door display unit (March). The methods of statistical process control (SPC) such as p-control chart and Pareto analysis were used to describe the defects occurrences, out-of-control conditions and the root causes. Total production volumes were 1,250, 360, and 250 units, with overall efficiencies of 96.72%, 94.17%, and 90.80% respectively. The overall rate of rejection in all three months was 4.07% (75 rejected out of 1,860 total units). The most common types of defects were injection mold (March), shortages in the quantity of the foam (all months), and external structural damage (January and March). Ishikawa analysis was applied to trace the causes systematically to materials, machine conditions, human factors, and environmental disturbances such as lack of power. Recommendations that combine Six Sigma DMAIC approach, preventive maintenance regimes and corrective process controls to operational excellence are provided.
Simultaneous Removal of Nutrients from Water Using Advanced Sustainable Technologies: A Comprehensive Systematic Review (2005-2024)
Nitrates (NO3-) and phosphates (PO43-) are released in high amounts of industrial waste and runoffs, causing the eutrophication process, which can impair aquatic life. The present paper is a systematic and comprehensive review of the most recent removal technologies, consisting of physical, chemical, biological, and nanotechnology technologies, and concentrates on newly published articles (since 2005). One of the studies deals with the complicated mechanisms of reaction, like ion exchange, electrostatic attraction, and the formation of a surface complex. The experiments showed that using a biochar-based adsorption system and nano-modified adsorbents is the most sustainable and economically applicable method, as the efficiencies of removal reach over 90% within the most favorable working environment. The review concludes that water treatment lies in the emergence of "Hybrid Systems" combining high efficiency with qualitative nutrient recovery in conformity with the concept of the circular economy and the resource-constrained areas.
PPO-Based Neural Architecture Search for Efficient and Hardware-Aware Windows Malware Detection in Cybersecurity
The rapid growth of malicious software targeting Windows operating systems has created significant challenges for modern cybersecurity systems. Traditional malware detection approaches often rely on manually designed models or signature-based techniques, which struggle to identify newly emerging and highly obfuscated malware variants. Although recent deep learning methods have improved detection accuracy, many of these models depend on fixed neural architectures and require extensive manual tuning, limiting their adaptability and efficiency. To address these challenges, this study proposes a reinforcement learning–driven Intelligent Agent framework for automated neural architecture search in Windows malware detection. The proposed system employs a reinforcement learning agent based on Proximal Policy Optimization to explore a simplified architecture search space and automatically construct an effective deep learning model. The approach was evaluated using the EMBER Dataset, a widely used benchmark dataset for malware classification. Experimental results demonstrate that the architecture discovered by the proposed Intelligent Agent achieves strong detection performance, reaching an accuracy of 97.3 while also outperforming several baseline approaches in terms of Precision, Recall, F1-Score, and AUC metrics. These findings indicate that integrating reinforcement learning with neural architecture search can significantly enhance malware detection performance while reducing the need for manual model design. In addition to achieving high detection accuracy, the proposed approach considers computational efficiency by enabling the discovery of hardware-aware neural architectures suitable for real-world deployment. The proposed framework highlights the potential of intelligent agent–based systems in developing adaptive and efficient cybersecurity solutions capable of addressing evolving malware threats.
A Review of Origami–Inspired Structure According to Pattern, Manufacturing and Application
Abstract: Corrugations can be one of several methods to enhance the properties of thin-walled mechanical structures in terms of handling surface area. It is considered a solution to many application problems for devices and equipment and to solve engineering problems. This paper highlights the most famous origami shapes, manufacturing methods, and applications. The shapes of origami are dependent on the degree of freedom. This means that the shapes in this paper discussed the five most common types and the cell units they consist of. These units are repeated over the whole sheet. All shapes were drawn up with all their details or details of units that they were formed from. Also, it showed the single-cell unit that makes up each of the five most common patterns in engineering applications. The five most common patterns were waterbomb, Miura-Ori, Yoshimura, Kresling, and Resch. Owing to the ability of energy absorption and mechanical features, Miura-Ori was deeply discussed. For manufacturing processes, the most known methods were highlighted, which gave a hint for manufacturing differences in origami. In addition, the applications of origami in seven fields were presented in detail. The seven fields were automotive, aeroplan, aerospace, buildings and structures, biomedical engineering, map and floor plan, and architecture. Applications of origami in engineering have been extensively reviewed in fields such as the architecture field in the five specific disciplines. The five specific disciplines were building facades and adaptive shading systems, transformable vaults, bridge with load bearing capacity, transformable canopies, and sawtooth roofs. The objectives of the study are to make readers recognize the shapes of origami, to show the most important methods of manufacturing processes for origami, and to provide the applications in major origami-based engineering fields. In conclusion, this paper provides a significant reference for those interested in this matter or in the field of origami to obtain a wide and concise view of patterns, manufacturing processes, and applications of origami structures.
Pressure Drop Analysis Using Deep Learning Techniques to Enhance Micro-channel Cooling Performance in Energy Conversion Systems
The high power energy conversion systems are quickly increasing in power, which requires a great deal of efficiency in thermal management, and micro-channel heat sinks have found considerable use in this area. But it is computationally expensive and time consuming to determine the pressure drop in these complicated micro-geometries accurately by using the conventional experimental or computational fluid dynamics methodology. In a bid to overcome this critical issue, this work suggests a new deep learning model, which is referred to as the Channel-Inspired Neural Network (CoINN), which is specially designed to identify pressure drop in the variety of micro-channel arrangements. A large dataset was created and described that was based on physics, and it included different operational fluid dynamics and geometric parameters. CoINN model was trained and strictly benchmarked against known machine learning baseline models, such as Gradient Boosted Regression Trees, shallow Artificial Neural Network, and Support Vector machine. The CoINN framework has been shown by quantitative analysis to be absolutely superior with a Coefficient of Determination of 0.9998 and a Mean Absolute Error of only 0.859 kPa which means that CoINN would compare with the traditional algorithms that would have trouble with the non-linearities inherent with the fluid data. Moreover, systematic sensitivity analysis confirmed the physical interpretability of the deep learning model and accurately found the two key physical mechanisms governing the pressure drop due to frictional inlet velocity and hydraulic diameter. The suggested framework offers a very robust, predictive surrogate model, which is accurate and computationally fast, and it opens the way to faster design and optimization of advanced micro-scale cooling technologies.
Applying Software Engineering for Robust Early Fault Detection in Industrial Systems Using Heterogeneous Ensemble Learning Models
Software engineering plays a critical role in developing robust and scalable systems to monitor industrial systems to predictive maintenance. Industrial systems should have early fault detection and prevent equipment failures and downtime. Single classifiers do not however work well in identifying subtle and rare defects especially in imbalance data set. In this paper, I am proposing a heterogeneous ensemble learning model consisting of Random Forest, Gradient Boosting and Support Vector Machine (SVM) with a stacking method in order to enhance the detection accuracy. Data pre-processing methods were used to improve model performance such as feature scaling and class balancing. The experimental results demonstrate that the proposed ensemble had the accuracy of 0.95, precision of 0.93, recall of 0.91, and F1-score equal to 0.92 with the original dataset (1000 samples). As the number of samples grew to 5000, the accuracy, precision, recall and F1-score improved to 0.96, 0.94 and 0.93 respectively. This paper presents a heterogeneous stacking ensemble structure that is scalable, which incorporates multiple classifiers in a modular software design, and is capable of providing robust early fault detection during small and large data sets. The analysis of the importance of features showed that Temperature and Vibration are the most important indicators of early faults. The suggested solution is very robust and reliable in fault detection, it has low false alarms and it offers a good predictive maintenance solution to industrial and IoT enabled systems.
Lightweight Concrete: Types, Properties, and Applications,A Comprehensive Analytical Review
Lightweight Concrete (LWC) has come to be regarded as one of the most strategically important construction materials in contemporary construction engineering, offering a balance of reduced self-weight with structural, thermal, and acoustic performance. In this paper, a comprehensive, analytically focused review of the major types of LWC, namely Lightweight Aggregate Concrete (LWAC), Foamed Concrete, Autoclaved Aerated Concrete (AAC), No-Fines Concrete, and High-Strength Lightweight Concrete (HSLWC), as well as the newly developed type of Geopolymer Lightweight Concrete, is presented. Instead of merely presenting a descriptive classification of these types of LWC, the review is carried out from a critical analytical perspective, examining the mechanisms of performance trade-offs, the Interfacial Transition Zone (ITZ) as the key factor in determining mechanical response, and the efficacy of supplementary cementitious materials, nanomaterials, and fibers in overcoming the drawbacks of these materials. Five sets of analytical tables comparing the performance of these types of LWC, drawing on over 75 peer-reviewed publications, are presented. The review is concluded by presenting a forward-looking perspective on the newly developed areas of self-healing LWC, digital twin monitoring, and geopolymer-based LWC.
Dimensionality Reduction in Data Mining Application: A Systematic Review
The rapid growth of high-dimensional data introduces redundancy and noise that hinder data mining and machine learning, a challenge commonly known as the curse of dimensionality. Dimensionality Reduction (DR) techniques, including feature selection and feature extraction, alleviate these issues by reducing computational complexity while maintaining informative structures and enhancing predictive accuracy. This review systematically examines classical DR methods such as Principal Component Analysis (PCA) and its variants, alongside recent advances in nonlinear and manifold learning approaches, including Isomap and Locally Linear Embedding (LLE). Emerging probabilistic, soft computing, automated pipeline, and semi-supervised DR techniques are also discussed. Applications across bioinformatics, recommender systems, trajectory mining, and disease diagnosis are reviewed, with comparative analyses highlighting the strengths and limitations of each approach.
Several Studies Proceed For the HTPS: Challenges and Optimization
The rapid expansion of oil production has increased the demand for effective separation systems, with oil separators playing a vital role as the first stage in oil production due to their fast response. The horizontal three-phase separator (HTPS) is the most commonly used type, valued for its effectiveness and low maintenance requirements. HTPS is an essential component in oil and gas production systems, efficiently separating crude oil into oil, water, and gas phases. This paper examines the limitations of current research and the challenges associated with a comprehensive review of HTPS components and optimization methods. It outlines the design fundamentals, internal configurations, and operational challenges, while also analysing mitigation techniques. This study provides a comparison between different optimization approaches in the oil recovery (OR) separator. This study contributes to ongoing research and serves as a practical guide for enhancing the design, efficiency, and reliability of TPSHs in diverse operational environments.
Fifth Annual Scientific Research Conference (BCHS)
Proceedings of the Fifth Annual Scientific Research Conference Baghdad College High School/Adhamiya
Stabilizing expansive clay using nano-silica and recycled brick under sulfate exposure
Expansive clay soils present significant durability challenges when exposed to sulfate-rich environments due to volumetric instability and mineralogical transformations. Conventional stabilizers such as cement and lime often experience long-term degradation under sulfate attack, primarily through ettringite and thaumasite formation. This systematic review evaluates recent experimental investigations (2022-2025) focusing on the durability performance of expansive clays stabilized using nano-silica (NS) and recycled brick powder (RBP), with particular emphasis on hybrid systems. A structured screening methodology yielded 37 eligible peer-reviewed studies. The review synthesizes mechanical indicators including unconfined compressive strength (UCS), swelling reduction and residual strength under sulfate exposure, alongside microstructural evidence from SEM and XRD analyses. Findings indicate that hybrid NS-RBP systems refine pore structure, promote low Ca/Si ratio C-S-H formation, and significantly reduce sulfate ion diffusion. Compared to cement only system, hybrid binders demonstrate superior strength retention (typically 78-92% under high sulfate concentrations) and enhanced durability stability. Sustainability considerations further support hybrid stabilization due to reduced embodied carbon relative to conventional cement treatment. Despite promising laboratory results, long-term field validation remains limited. The study concludes that Ns-RBP hybrid stabilization represents a technically viable and environmentally responsible solution for sulfate-prone expansive soils.
Punching Shear Behaviour of Reinforced Concrete Flat Slabs with Openings: Mechanisms, Failures, and Strengthening Strategies: A Review
Reinforced concrete flat slabs are widely used for their architectural flexibility and construction efficiency, but they are vulnerable to punching shear failure—a sudden collapse at slab-column connections. Openings for stairs, elevators, and utility ducts worsen this vulnerability by removing concrete and reinforcement from critical regions. This review synthesized studies from 2007 to 2025 to evaluate punching shear behavior in slabs with openings and the effectiveness of strengthening techniques. Findings showed that openings adjacent to columns reduced punching shear capacity by 29% to 65%, with circular openings causing less damage than rectangular ones. Openings beyond four times the slab thickness had minimal effect. Strengthening methods proved highly effective. Externally bonded CFRP sheets restored or enhanced capacity by 22% to 77%. ECC applied around columns increased punching capacity by 34% to 38% and transformed failure from brittle to ductile. Super elastic Shape Memory Alloy (SMA) stirrups improved energy absorption by over 100%. Sustainable alternatives like geopolymer concrete with CFRP strengthening achieved up to 77% capacity improvement, while basalt-FRP bars reached 89% of steel-reinforced slab strength. Traditional design codes like ACI 318 were overly conservative, whereas Artificial Neural Network models provided more accurate predictions with only 9% average error. This review confirms that while openings severely weaken flat slabs, modern strengthening techniques effectively restore performance.
Improving The Engineering Properties of Gypseous Soils by Adding Polymers: A Review
gypseous soils are distributed in many regions in the world. These soils have a considerable potential for collapse because of the presence of Gypsum, Gypseous soils was one of the most complicated salty soils that challenged the geotechnical engineers. Also, Buildings constructed on gypsum soil will experience unanticipated distortions that will eventually lead to disastrous of collapse. A number of stabilizers and methods have been used to stabilize the soil and render it to be more suitable for construction purposes. This paper gives an overview of some previous researches on the behavior of this soil when it stabilized with polymers. These studies reveal that treating the soils with polymers in liquid, powder or Fiber form improves the soil properties. These include a decrease in the collapse potential, an increase in durability, and improved permeability. Moreover, polymer stabilization is more economical and environmentally friendly than conventional techniques.
Steel Fiber-Reinforced Concrete: From Material Design to Structural Performance—A Review
Steel Fiber Reinforced Concrete (SFRC) has been developed as a high-performance material that can overcome the limitations of conventional concrete, especially its low tensile strength. This review aims to provide a critical evaluation of SFRC by correlating its material design properties with its mechanical properties. Emphasis has been given to the fiber properties, especially its geometry, aspect ratio, dosage rate, and distribution. The fiber properties are important at both the micro-level and the macro-level. The fiber-matrix bond, especially at the interfacial transition zone (ITZ), plays a crucial role. The crack-bridging mechanism has been identified as the main contributor to the improved tensile strength, bending strength, toughness, and post-crack load capacity. SFRC has also shown promising results in terms of compressive strength, impact resistance, fatigue resistance, and durability. SFRC has shown promising results at the structural level, especially in beams, slabs, pavements, and seismic-resistant structures. SFRC can provide ductility and increase the life of structures, thereby reducing the amount of conventional reinforcement used. However, there are some limitations, especially with regard to the standardization of designs and the durability of SFRC.
Functionally Graded Materials beyond Conventional Design: Multiscale Modeling, Advanced Manufacturing, and Emerging Engineering Applications
Functionally graded materials (FGMs) are a new generation of superior materials that default compositions, microstructure, and functions with smooth changes in volume within a material. The paper is a thorough review of the history of FGMs since the 1980s when it was first developed in Japan, until now where it has seen a wide variety of uses in aerospace, biomedical and energy industries. The theoretical considerations of the design of FEM are systematically discussed in the paper the composition variability, optimization of the gradient, and mechanical and thermal behavior analysis. A special emphasis is laid on the functional graded additive manufacturing (FGAM) technology, which focuses on the optimization of procedures of the techniques, i.e. selective laser melting, fused deposition modeling, and the new multi-material additive manufacturing technology. Significant applications to engineering examined by the review are lightweight aerospace components, which have better thermal management, orthopedic implants, which have better integration and reduced stress shielding, and energy systems, which include new thermal barrier coating. In spite of the significant advances, the extensive industrial implementation of FGMs are still burdened by the challenges such as the excessive costs of production, the technical restrictions connected with defect control and standardization of quality products, and the inexistence of the regulatory frameworks. The paper ends with the definition of the future research directions, which include the use of artificial intelligence and machine learning in predictive design, the introduction of nanocomposite-enhanced FGMs, and convergence with smart technologies to monitor and adapt to real-time. These developments make FGMs become a key facilitator of sustainable, high-performance engineering solutions to the next-generation applications.
Research Trends and Performance Evolution of Sustainable Self-Compacting Concrete: A Systematic and Bibliometric Review
Sustainable self-compacting concrete is an important material in the modern engineering construction sector due to its distinctive mechanical properties, ease of shaping, and significant contribution to reducing environmental impact. This paper provides a comprehensive systematic and bibliometric review of publications on this topic between 2010 and 2026. The systematic PRISMA-based approach was taken to define, filter, and examine the pertinent journal articles that are registered in Scopus. Bibliometric mapping techniques were used to gauge global research trends, publication development, dominant countries, and theme development. The results obtained indicate that the number of publications has increased considerably in the past 10 years, with the use of supplementary cementitious materials, recycled aggregates, and low-carbon binders as the main contributors. The most popular research topics are mechanical and durability performance, whereas the new research topics include rheology modification, fiber reinforcement, and life-cycle assessment. The review shows that there are crucial knowledge gaps concerning long-term sustainability, large-scale system application, and comprehensive sustainability assessment. The research has provided a knowledge map structure for research and practice, and has identified future research areas to improve high-performance sustainable self-compacting technologies.
Engineering Performance of Sulfate-Bearing Soils in Iraq and Their Impact on Concrete Foundations: Mechanisms, Evaluation Methods, and Mitigation Approaches – A Critical Review
In Iraq, which has a very wide distribution of sulfate-bearing soils, especially in the central and southern regions, these present a continuous issue for the concrete base’s durability and service life. Also, we see that in areas with elevated sulfate levels in the soil and groundwater, together with high temperatures, salinity, and the wetting and drying cycles, we get enhanced chemical and physical degradation. Into the concrete go the sulfate ions, which in turn react with the hydration products, mainly tricalcium aluminate and calcium hydroxide, to produce expansive compounds such as ettringite and gypsum. These reactions produce internal stresses, cracking, and long-term structural integrity issues. This review brings together available geological, geotechnical, and materials research to look at the spatial distribution and classification of sulfate soils in Iraq, the chemical and mechanical processes which underpin soil – concrete interaction, and what those issues mean for shallow, raft, and pile foundation systems. In this study, both traditional and advanced evaluation techniques, which included chemical sulfate analysis, expansion testing, durability indices, and field-based non-destructive methods. Also, we went in-depth into mitigation approaches, which covered sulfate-resistant cements, supplementary cementitious materials, optimized mix design, soil stabilization, and protective barrier systems. This review also reports that present-day Iraqi engineering practices fall short in some areas, which in large part is a result of the low use of durability-based design frameworks and also the lack of in depth long term monitoring.
Impact of Void Geometry, Size, and Location on the Structural Behavior of RC Hollow Beams: A Review
Reinforced concrete (RC) hollow beams have been increasingly adopted in structural applications due to their potential for reducing self-weight, material consumption, and environmental impact without significantly compromising structural performance. This review investigates the influence of void geometry, size, and location on the structural behaviour of RC hollow beams. Previous experimental and numerical studies were comprehensively examined to evaluate their effects on load-carrying capacity, cracking behaviour, deflection, ductility, and failure modes. The findings indicated that the presence of voids generally resulted in a reduction in first cracking and ultimate load; however, this reduction was often disproportionately small compared to the achieved concrete volume savings. Circular voids were found to be more effective than square or rectangular voids in preserving strength and enhancing ductility. The location of the void played a crucial role, as placing hollows near the neutral axis or within the tension zone minimized adverse effects on structural performance. Increasing void size led to higher deflections and reduced stiffness, particularly when the void area exceeded approximately 10% of the gross cross-sectional area. The incorporation of additional materials such as steel fibers, steel or aluminium inserts, and fiber-reinforced polymer strengthening systems was shown to significantly improve strength, ductility, and failure behaviour. Numerical simulations using finite element software demonstrated strong agreement with experimental results, confirming their reliability for predicting hollow beam behaviour. Overall, RC hollow beams were demonstrated to be an efficient and sustainable structural solution when void characteristics were properly optimized.
Impact of Printing Process Parameters on the Tensile and Flexural Strength of Filament Material
This research evaluates the mechanical properties of three polymer-based filament materials used in Fused Deposition Modeling (FDM): Polylactic Acid Plus (PLA+), Polyethylene Terephthalate Glycol (PETG), and Carbon Fiber-reinforced Polylactic Acid (PLA-CF). The study focuses on maximizing structural integrity and minimizing porosity by employing a 95% solid 3D Honeycomb infill density. Through standardized ASTM D638 tensile testing and ASTM D790 three-point bending tests, the research identifies PLA-CF as the most rigid material, achieving a flexural modulus of 5643.06 MPa. In contrast, PETG and PLA+ demonstrate superior peak tensile forces and higher ductility, making them better suited for applications requiring toughness. The findings indicate that optimized wall loops and high-density infill patterns significantly mitigate catastrophic failure under load. Comparative analysis with recent literature (2024–2026) corroborates the dominance of infill density in determining mechanical performance, while also highlighting critical research gaps in PLA+ specific parameter optimization and the interaction between wall loops and structural strength. This comprehensive assessment provides essential guidance for material selection in load-bearing engineering applications, such as prosthetic components.
Physical Layer Security Enhancement for Cooperative NOAM Systems
NOMA (Non-Orthogonal Multiple Access) has been recognized as new multiple access technology for wireless communication networks of the fifth generation and beyond, owing to its ability to improve spectral efficiency, support massive connectivity, and provide user fairness. However, it has been observed that the superposition-based transmission principle of NOMA is highly susceptible to eavesdropping attacks, especially in an open wireless network where all wireless communications are inherently broadcast in nature. Hence, ensuring secure wireless communications for NOMA systems has now become an important area of research. This research aims to improve the secrecy level of wireless communications for NOMA systems by employing Physical Layer Security (PLS) techniques. Specifically, two prominent PLS techniques, Artificial Noise (AN) and Cooperative Jamming (CJ), have been employed to provide security against passive eavesdropping attacks. The cooperative NOMA system architecture is considered, and the secrecy enhancement is analyzed for two possible scenarios when the eavesdropper is close to the relay node and when the eavesdropper is close to the base station. The secrecy performance of the proposed system is analyzed through various parameters, namely, Secrecy Outage Probability (SOP), Secrecy Capacity (SC), Secrecy Rate (SR), and Secrecy Energy Efficiency (SEE) in terms of the Signal to Noise Ratio (SNR). The numerical results for the secrecy metrics are obtained through MATLAB-based simulations, whereas the system-level model is developed using the Simulink platform to verify the suggested system's functionality in the presence of AN and CJ for the legitimate NOMA users and the eavesdropper. The results show that the secrecy performance is significantly enhanced with the introduction of AN and CJ, and the system shows robust performance in terms of energy efficiency. This paper provides significant insights into the design of the NOMA system from the secrecy perspective and validates the potential of PLS techniques for the design of the future wireless system.