Al-Nahrain Journal for Engineering Sciences

Real-life strategies are applied to assess pavement functionality, high-quality performance, and durability throughout its service life. Estimating pavement maintenance and sustainability is difficult. High-performance continuous reinforced concrete pavement (CRCP) structural design and Jordanian natural zeolite (JNZ) as a sustainable supplementary cementitious material (SCM) and unique mixed cement for green manufacturing are researched in this paper. The results obtained from this study showed that replacing cement with JNZ powder at 0%, 10%, 15%, and 20% improved concrete performance. Natural zeolite-mixed cement preserved concrete quality and reduced the need for ordinary Portland cement (OPC) and sulfate-resistant cement (SRC) clinker. After that, slab universal testing equipment and Jordanian zeolite-blended cement-reinforced concrete slabs were developed for CRCP performance. Therefore, fresh concrete was tested for partial cement substitution and standard mixture workability. Compressive, tensile, and flexural strength tests on 7 and 28 days and durability test (water absorption) were utilized to assess concrete strength and natural zeolite's potential to reduce resource consumption and carbon footprint while maintaining structural integrity using Open LCA. Sustainable CRCP structure development improved performance, resource conservation, and carbon footprint over the prior mix, according to EIA (Environmental Impact Assessment) software and chemical tests. This research improves materials and supports global sustainability goals.

An overview of electro-osmosis (EO) and electrokinetic (EK) soil treatment methods is provided in this paper, along with their impact on pile capacity, installation, and foundation shear strength after improving the geotechnical properties of weak soils, particularly soft clays. As a result of their low shear strength, high compressibility, and poor drainage characteristics, soft clayey soils pose significant challenges in civil engineering. With EO and EK, pore water and ions are moved through the soil matrix under an applied electric field, resulting in consolidation, increased shear strength, and reduced plasticity. This review explores the fundamental principles of EO and EK, including the mechanisms of water transport, ion migration, and electrochemical reactions. It examines various electrode configurations, treatment parameters, and their influence on soil improvement. Furthermore, the paper analyzes the effects of EO treatment on pile capacity, considering both the increase in soil strength and the reduction in pore water pressure during installation. The impact on pile installation methods, such as reducing driving resistance and improving grout penetration, is also discussed. Finally, the review investigates the enhancement of foundation shear strength through improved soil properties achieved by EO/EK treatment. By synthesizing existing research, this paper aims to provide a comprehensive understanding of the potential benefits and limitations of EO and EK methods for ground improvement in soft clayey soils, offering valuable insights for future research and practical applications in geotechnical engineering.

The properties related to Synthetic fibers such as significant strength, ductility, and durability lead the fibers to be adequate in enhancing the mechanical properties of asphalt concrete mixtures and that indicated by several studies. This paper aims to deliver an overview about the reinforcing influence of synthetic fibers on the mechanical and performance properties of asphalt concrete mixture.
This paper surveys the literature on synthetic fibers and their applications in enhancing the mechanical features of asphaltic mixtures. It could serve as a reference for prospective modification and development of asphalt pavement by synthetic fibres. The characteristics of prevalent synthetic fibers are introduced, and their usage in asphalt mixtures is evaluated. A review of fiber surface treatment techniques demonstrates that they can enhance the performance of synthetic fibers in asphalt concrete mixtures, especially on the chemical surface. The article debates how synthetic fibre inclusion influences asphalt concrete mechanical performance, including rutting resistance, tensile strength, water susceptibility, and cracking resistance. The review indicates that using fibers such as aramid, glass, polyester, polyamide, and carbon improves asphalt pavement resistance to permanent deformation.

Moisture damage in terms of stripping; and aging surface in terms of raveling and abrasion are among the primary distresses that lead to the deterioration of asphalt pavement, diminishing the overall quality and functionality of road surfaces. This study investigates the impact of using low-cost and locally available waste aluminum scrape powder (WASP) with a particle size ranging from sieves No.8 to No.200. WASP exhibits a high bulk specific gravity and melting point temperature on HMA mixtures, which could also potentially enhance the density and stiffness of modified mixtures. Five quantities of additives 0.5, 1.0, 1.5, 2.0, and 2.5% have been used to enhance the mechanical-durability features. The aggregate sources of AlDoz and AlNibaa'e were chosen, and different mixtures were produced utilizing Marshall and Roller compaction methods. The study's findings indicated that WASP enhanced mechanical-durability characteristics and reduced the asphalt mixture's sensitivity to abrasion, moisture damage, and aging. The optimal amount of WASP was determined to be 1.5%. In addition, based on the influence of the aggregate source and compaction technique, it is visible that the AlNibaa'e source and roller compaction mode provide superior outcomes compared to the AlDoz aggregate source and the Marshall method.

The toxicity of permanent implants is the main concern. The release of ions from the substrate leads to toxicity. Because of how the human body works biologically, the toxicity of corrosion compounds is a byproduct of wear and fretting debris. aimed to improve the corrosion resistance of a 316L stainless steel substrate. Bio ceramic Nano-hydroxyapatite (HA) was coated using the Electrophoretic Deposition (EPD) technique. Stainless steel has good mechanical properties and high compatibility, but it suffers from body fluid attack due to its chloride content, which can penetrate the passivation layer, resulting in the release of chromium and nickel ions. Tissues and organs are damaged by the ions and debris that are released. To address this problem, it was coated with bioceramic using the EPD method. Suspensions of various powders—hydroxyapatite, magnesium oxide, zinc oxide, and the composite—were prepared and coated by electrophoretic deposition. The coated samples were dried at room temperature to ensure a homogeneous coating structure. The zeta potential test for magnesium oxide and hydroxyapatite suspensions was positive, while zinc oxide and complex suspensions were negative. One of the important parameters for achieving electrolyte and implant balance is the open circuit potential (OCP). A substantial change towards a more noble direction (less negative) was seen in the OCP-coated (316 L) alloy, suggesting excellent thermodynamic stability. Tafel extrapolation analysis was used to obtain the corrosion potential (Ecorr) and corrosion current density (Icorr) values of composite-coated stainless steel 316L, which are generally derived from the polarization curve. The findings that are in line with the MgO, HA, and ZnO coatings show a significant decrease in corrosion current (Icorr), an increase in corrosion potential (Ecorr), and a decrease in corrosion rate from (4.386 × 10-¹ mm/y) Stainless Steel 316 L to (1.417 × 10-² mm/y) MgO Coated and (1.222 × 10-³ mm/y) (65%MgO+25%ZnO+10%HA coated).

Hybrid metal composite materials, combining diverse metal components, have emerged as promising alternatives in engineering applications, offering a unique synergy of mechanical properties. This review comprehensively examines the fatigue life of hybrid metal composites, delving into the intricate interplay of materials, manufacturing processes, and environmental factors. Drawing from a rich array of literature, the review explores the evolution of hybridization strategies, emphasizing their impact on fatigue resistance. Key factors influencing fatigue behavior, including material selection, manufacturing techniques, and environmental conditions, are systematically analyzed. The article highlights the significance of strategic hybridization in enhancing fatigue characteristics, reducing costs, and optimizing the overall performance of metal composites. The insights presented contribute to advancing the understanding of fatigue mechanisms in hybrid metal composite materials, offering valuable guidance for future research and engineering applications. Hybrid metal composite materials, characterized by the combination of diverse metal components, have garnered significant attention in engineering applications due to their potential to provide a unique synergy of mechanical properties. This comprehensive review delves into the intricate aspects of the fatigue life of hybrid metal composites, offering a thorough analysis of the interplay between materials, manufacturing processes, and environmental factors.

The Old City of Najaf stands out for its unique urban fabric and rich historical heritage, serving as a key destination for religious tourism while hosting worship facilities and housing for seminary students. As both a vibrant religious center and a cultural landmark, it presents a complex urban context that demands a careful balance between residents’ and visitors’ needs. This raises the question of how to adapt sustainability standards to align with the city’s environmental, historical, and cultural dimensions. Using descriptive and analytical methods, including field observations, literature reviews, and expert consultations in urban planning and heritage preservation, the study examines challenges like uncontrolled urban growth, strained infrastructure, and land-use conflicts. Initial findings suggest these issues significantly hinder sustainable development, particularly with growing demand for heritage tourism and Najaf’s role as a hub for religious studies. The study proposes practical strategies to preserve Najaf’s cultural identity, improve residents’ quality of life, and enhance its status as a sustainable heritage tourism destination, boosting its long-term appeal and sustainability.

In this study, behavior of steel hollow short columns fabricated from steel square section under axial load is investigated with and without CFRP strengthening, five specimens of SHSC without strengthening are tested by applying concentric axial force; and the obtained results are compared with fifteen SHSC strengthened with CFRP wrapping with different five percentage from the total length of the specimens as follows (20%, 40%, 60%, 80%, and 100%) and each strengthening length consist from three different layers (one, two and three) layers. The curves of load-displacement are plotted for the specimens with maximum strength load. The results show that the most effective type of CFRP wrap strengthening is the full length of the specimens and especially with two and three layers. The increase in the load carrying capacity is 34.5% from 126.37 kN for SHSC-C to 170.02 kN for SHSC-100-3L, and the increase for ductility index is 23.6 % from 1.39 for SHSC-C to 1.72 for SHSC-100-2L. The pattern of failure for the specimens; non strengthened or strengthened with less than full length is local buckling, while the failure is CFRP rupture with local buckling for specimens strengthened with full length specimen.

Women’s safety remains an urgent challenge, particularly in moments when conventional panic button devices fail due to a victim’s inability to act or poor network coverage. To overcome these shortcomings, TRIAD-Lite is introduced as an IoT-enabled wearable framework that unites multimodal physiological sensing with lightweight deep learning for proactive distress identification. The system captures heart rate, blood pressure, galvanic skin response, and motion patterns, while incorporating a triple-tap gesture to confirm user intent, all processed locally on a Raspberry Pi for real-time inference. Unlike reactive mechanisms, this design anticipates danger by analyzing variations in physiological signals that often precede visible distress. Communication reliability is reinforced through a hybrid strategy: alerts are transmitted via GSM or Wi-Fi under normal conditions, but in the event of limited connectivity, a LoRa-based backup ensures long-range transmission. Experimental analysis using simulated datasets yielded an AUC of 1.000 with flawless precision and recall, highlighting the model’s reliability and calibration. Further field evaluation demonstrated that LoRa maintained connectivity across 5.7 kilometers with complete packet delivery, proving effective for both rural and urban environments. By combining predictive analytics, gesture-based confirmation, and dual communication layers, TRIAD-Lite offers a scalable, privacy-conscious, and highly resilient framework that strengthens women’s safety and extends protective technology into regions where conventional systems often fail.

The usage of non-toxic, eco-friendly natural dyes on textiles has achieved notable attention due to increased environmental attention about avoiding hazardous synthetic dyes. This has prompted a return to natural dyes and the search for new sources, especially locally available ones like licorice. In this study, Glycyrrhiza glabra extract (70 g/l), prepared using ultrasound assistance, was used to dye cotton samples. The natural dyeing process employed a simultaneous mordanting method with zinc chloride and alum as mordants, in many concentrations (1, 3, 5, 7 and 10 g/l). The color fastness of the dyed samples was evaluated using a scanner and ImageJ. The fastness of the dyed fabrics was tested against washing and rubbing, and samples with licorice extract showed excellent stability. A tear strength test was also conducted to assess the impact of licorice extract dyeing on the mechanical properties of the samples. It was observed that dyeing with licorice reduced the tear strength, but increasing the concentration of mordants improved the resistance to tearing, making the mordanted samples stronger than the mordanted ones.

In precision agriculture, crop disease detection can be a highly valuable undertaking in which scalable and correct solutions may save considerable amounts of money and loss of yield. This paper introduces a comparative analysis of state-of-the-art deep learning models with special attention to EfficientNetB3 hybrids, which are trained on a balanced subsample of the PlantVillage dataset with 33 classes based on nine crops. To overcome the shortcomings of the previous studies, which used unbalanced sample, a leakage-free balancing approach was used, resulting in 13,200 training and 3,300 validation samples. Custom head transfer learning was used where it was tested using two strategies; FreezeUnfreeze fine-tuning, and Singlephase training. MobileNetV2, InceptionV3, DenseNet121, GhostNet, in addition to other baseline CNNs, were compared to baseline Convolutional Neural Networks (CNNs). The findings indicate that EfficientNetB3 hybrids are superior with an accuracy of ≥99.5% and 99.9% Area Under the Curve (AUC) and specificity than the previous CNN-based systems. The paper logically defines a performance ladder between model options and real-life deployment demands, such as lightweight mobile applications to precision agriculture systems, and points out future trends in the field-based validation.

This research is based on developing a flexible strain sensor from graphite-treated fabric using knife coating technology. Three sensors were formed, differing in the number of coating layers (2, 4, 6). The results of studying their properties had shown that with increasing a number of coating layers, the electrical conductivity value of the treated samples increased, reaching a value of (21.8×10-3 S/cm). The treated layer was superficial, as the penetration of the coating into the structure did not increase significantly. It was also shown that the treatment did not affect the fabric properties such as hardness and tear strength. When studying the sensor's performance, it was found that the sensor's resistance value changes with the change in its bending angle. The change rate was higher for the six-layer sample, and the response time was shorter, faster (0.8s), than the other samples. Then, a working system was applied to the sensor to give a command to turn the LED on or off by bending the sensor and it showed good performance. This, in turn, confirms the effectiveness of applying this sensor in smart wearable textiles.

This research studies the capabilities of antenna arrays known as switched active switched parasitic antennas (SASPA) in enhancing the direction of arrival (DOA) estimation of received signals. In these arrays, a single antenna element operates in an active state while the other antenna elements are parasitic at one time of measurement. In the next measurement time, a parasitic element is switched to an active state while the active element is switched to a parasitic state, and the procedure is quickly repeated for each array element. By a straightforward arrangement of SASPA measurements, a functional steering matrix can be produced without any unitary transformation. This steering matrix results from a real-valued matrix that contains the information on the DOAs of the received signals, multiplied by a vector that represents the mutual coupling. The advantages of this steering matrix contribute to obtaining high-resolution DOA estimation with considerably reduced processing time compared to the conventional antenna arrays where all antenna elements are in an active state. The simulations conducted in this study evidently demonstrate that the resolution of DOA estimation with SASPA arrays is considerably superior, irrespective of the array’s compact size and the directions and proximity of received signals to each other. Additionally, other simulations in this work depict that the processing time for DOA estimation using SASPA arrays is significantly reduced (approximately one-third less on average) in comparison to traditional all-active antenna arrays.

Identifying fish species in natural aquatic environments remains challenging due to changing light conditions, turbid water, and complex underwater scenes. Most current deep-learning models rely on controlled datasets, which limits their use in real-world settings. This study presents Auto Fish, a mobile deep-learning system for real-time, offline fish species identification on Android devices. The system uses the MobileNetV2 architecture, optimized with TensorFlow Lite for processing on the device. This approach ensures high accuracy while keeping computational costs low. We trained and evaluated the model on a balanced dataset of 8,000 annotated images, including nine marine species: Sea bass, Red sea bream, Horse mackerel, Gilt-head bream, Shrimp, Black sea sprat, Trout, Red mullet, and Striped red mullet. Extensive preprocessing, image enhancement, and stratified sampling helped the model perform well despite variations in lighting and background conditions. The experimental results showed a validation accuracy of 99.2%, with both macro and micro Precision, Recall, and F1-scores around 99.3%, and an average False Positive Rate (FPR) of 0.09%. The system supports offline recognition, cloud syncing via Firebase, and delivers real-time results within 4.2 seconds per image on mid-range smartphones. These findings show that Auto Fish can effectively classify fish species in the field while remaining efficient and easy to use. This work offers a practical AI-based solution that connects research with ecological monitoring, empowering citizen scientists and conservationists to document biodiversity using mobile technology.

This work suggests a Deep Learning (DL) architecture based on You Only Look Once YOLOv11 for Skin Cancer (SC) detection. The similarity between malignant and benign lesions makes visual inspection a failure to distinguish between them. To solve this problem, the proposed approach uses a 3-step pre-processing stage, namely hair removal, color normalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) contrasts, has been conducted to eliminate artifacts and improve image quality. Balanced data augmentation on the training set of the PROVe-AI dataset. In this process, YOLOv11 with C3k2 module and C2PSA module showed significant results in optimized multi-scale feature collection and spatial interest. The experimental outcome demonstrates that the proposed model has a classification accuracy of 93.09% and led the baseline models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). The proposed optimized YOLOv11 architecture allows for skin cancer detection in a computationally efficient framework with promising preliminary results so that the proposed approach can be a beneficial Artificial intelligence (AI) tool for early diagnosis, particularly in a lack of high-tech medical facilities.

Manual handling of semi-knockdown vehicles in assembly plants is unsafe, time-consuming, inefficient, and prone to quality irregularities. To intervene in addressing these problems, this study develops a prototype of an automated load carrier intelligent navigator. The work centre is analysed for space, material type and handling requirements. This is followed by design and testing, whereby software, hardware and mechanical engineering are integrated in the context of process optimisation. The prototype was tested on rough and smooth surfaces, for no-obstacle and obstacle avoidance conditions. On rough and smooth surfaces with no obstacles, the minimum distance considered is 0.5m, and the average speed and time determined are 0.08m/s and 6.23s, 0.17m/s and 2.97s, respectively. For the maximum distance of 3.0m, the average speeds and times determined are 0.081 m/s and 37.42s, and 0.18 m/s and 17.35s, respectively. The average distance considered for both rough and smooth surfaces is 1.75 m, and the average speed and time at each scenario are 0.081 m/s, 21.78s, and 0.17 m/s, 10.26s. The voltage of the battery drops, with a corresponding decrease in the speed of the motors. The automated carrier prototype makes the best decisions when it encounters an obstacle, giving the best outputs. This paper contributes by providing real-time intelligent navigation data and accurate regulation of the automated carrier for automotive assembly plants. Its novelty lies in conducting experimental investigations using the automated loading/unloading intelligent navigator to explore its advantages compared to manual loading/unloading in automotive assembly plants. In conclusion, building a carrier for assembly operations enhances assembly operational performance, correcting inefficient and unsafe loading and unloading processes.

The growing use of “distributed energy resources (DER)” will result in a significant increase in the total number of gadgets or devices that users and third parties own and control. These gadgets rely largely on digital communication and control, placing them in danger due to cyber threats. This study presents a comprehensive framework that is resistant to attacks for defending integrated DER and major power grid infrastructure from hostile cyber-attacks, ensuring the safe integration of DER without jeopardizing system dependability and stability. This research focuses on the development of a cyber-physical power system that incorporates a significant integration of DER and analyses the particular cyber security problems brought about by DER integration. Following that, we provide a systematic DER resilience analysis approach, in addition to effective and measurable resilience measurements and concepts concerning design, and we summarize important DER assault scenarios. In conclusion, we suggest preventive, detective, and responsive measures against cyber-attacks, specifically tailored for integrating Distributed Energy Resources (DER) throughout the physical, cyber device, and regulatory levels of an eventual smart grid.

The stilling basin is a frequently used energy dissipators that converts supercritical flow from a dam's spillway into subcritical flow that is in compatible with the downstream river regime. They serve to prevent scouring, which happens when high-velocity water enters the dam's downstream reach. This scouring not only seriously erodes the downstream area but also damages the dam's foundation. In this research, the behavior of blocks of stilling basin and spillway toe in reducing energy losses and the longitudinal hydraulic jump toe position is evaluated. This research includes two cases ; case1 is a spillway and stilling basin without blocks, and case2 which is a spillway and stilling basin with chute blocks and middle blocks. By increasing the discharge from 0.010 m3/s to 0.020 m3/s, the difference in the dissipation energy for case1 and case2 are (17.6%, 24.7%) respectively, and that the percentage of decrease in the longitudinal hydraulic jump toe position is (83.94%, 85.90%, 86.30%, 87.20%, 88.39%, 87.58%, 80.64%, 77.94%) for the discharges tests from ( 0.010 m3/s to 0.017 m3/s). As a results, The using of blocks in stilling basin and spillway led to a rise in relative energy dissipation and decrease the longitudinal hydraulic jump toe position of stilling basins.