Ain Shams Engineering Journal最新文献

{"title":"Investigating BIM adoption for seismic response prediction in geotechnical structures: An empirical framework","authors":"Nimer Alselami ,&nbsp;Shamshad Alam ,&nbsp;Madhusudhan Bangalore Ramu ,&nbsp;Abdullah O. Baarimah ,&nbsp;Taimoor Mustafa ,&nbsp;Hamad R. Almujibah","doi":"10.1016/j.asej.2025.103582","DOIUrl":"10.1016/j.asej.2025.103582","url":null,"abstract":"<div><div>Building Information Modeling (BIM) has transformed engineering and construction practices by enabling digital representations of physical and functional characteristics of structures. However, its specific role in enhancing seismic response prediction of geotechnical structures remains underexplored, with limited understanding of the key factors that enable or hinder its integration in this domain. This study investigates the integration of BIM for predicting seismic responses in geotechnical structures through a data-driven approach. The findings indicate that BIM contributes significantly to advanced simulation capabilities, improved data integration, compliance with seismic standards, and proactive risk mitigation resulting in enhanced accuracy, lifecycle management, and resilience of geotechnical structures. The key pathway from BIM integration to seismic prediction yielded a strong path coefficient (β = 0.669, p &lt; 0.001). This study does not involve computational seismic modeling, but instead develops a perception-based framework through expert input and structural equation modeling to guide BIM integration for seismic response prediction. These insights offer a foundational framework for engineers and decision-makers to adopt BIM in geotechnical seismic design, providing a pathway toward safer, more resilient, and sustainable infrastructure development.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103582"},"PeriodicalIF":6.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of thermal transfer in rotor-stator systems with multiple jets: impact on energy efficiency","authors":"Abdellatif EL hannaoui ,&nbsp;Chadia Haidar ,&nbsp;Rachid Boutarfa","doi":"10.1016/j.asej.2025.103586","DOIUrl":"10.1016/j.asej.2025.103586","url":null,"abstract":"<div><div>This study presents an integrated numerical and experimental investigation of convective heat transfer in a rotor–stator system subjected to multiple impinging air jets a configuration commonly used in the cooling of rotating machinery. The methodology combines computational fluid dynamics (CFD) using ANSYS Fluent with the Reynolds Stress Model (RSM) for turbulence modeling, selected for its capability to capture anisotropic and rotation-driven turbulent structures. The computational domain is discretized with a refined tetrahedral mesh, and simulations are validated against experimental data obtained through infrared thermography and thermocouple measurements. This combined approach ensures both physical fidelity and computational accuracy in evaluating flow and heat transfer behavior. The analysis focuses on three dimensionless parameters: the jet Reynolds number (Rej), varying from <span><math><mrow><mn>1.6</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mn>4</mn></msup></mrow></math></span> to <span><math><mrow><mn>5.4</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mn>4</mn></msup></mrow></math></span>; the rotational Reynolds number <span><math><mrow><mo>(</mo><mi>R</mi><mi>e</mi><mi>ω</mi><mo>)</mo></mrow></math></span>, ranging from <span><math><mrow><mn>2.32</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mn>5</mn></msup></mrow></math></span> to <span><math><mrow><mn>5.4</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mn>5</mn></msup></mrow></math></span>; and the dimensionless gap ratio (G = e/R), between <span><math><mrow><mn>0.02</mn></mrow></math></span> and <span><math><mrow><mn>0.16</mn></mrow></math></span>. The local Nusselt number (Nur) is analyzed as a function of normalized radial position (r/D). Results reveal that increasing both Rej and Reω enhances convective heat transfer, with distinct thermal behaviors observed across the disk: weak transfer near the center, intensified interaction in intermediate regions, and thermal gradient amplification at the periphery due to recirculation. Correlations are established for the maximum Nusselt number <span><math><mrow><mo>(</mo><mi>N</mi><mi>u</mi><mi>m</mi><mi>a</mi><mi>x</mi><mo>)</mo></mrow></math></span> and average Nusselt numbers, revealing nonlinear dependencies on Rej, Reω, and G. These findings contribute valuable design guidelines for improving cooling efficiency in rotating systems such as wind turbines, high-speed electric motors, and generators, where thermal performance is critical for reliability and energy efficiency.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103586"},"PeriodicalIF":6.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating and enhancing thermal comfort and indoor air quality in a library building through measurement and simulation","authors":"Linlan Chang ,&nbsp;Indra Permana ,&nbsp;Tian Setiawan ,&nbsp;Alya Penta Agharid ,&nbsp;Fujen Wang","doi":"10.1016/j.asej.2025.103580","DOIUrl":"10.1016/j.asej.2025.103580","url":null,"abstract":"<div><div>Thermal comfort and indoor air quality (IAQ) are critical for occupant well-being and productivity. This study evaluates a library’s self-study reading room for adults and children’s learning center, aiming to optimize temperature and airflow conditions for different age groups. Methods included questionnaire surveys, field measurements, and Computational Fluid Dynamics (CFD) modeling. Findings revealed that occupants preferred cooler temperatures than standard Predicted Mean Vote (PMV) models predicted, with optimal conditions being 26 °C and 0.2 m/s for adults and 24 °C and 0.2 m/s for children. Installing a Total Heat Exchanger (THX) system reduced CO₂ levels by approximately 200 ppm. CFD simulations identified CO₂ distribution issues, leading to recommendations such as removing partition walls and rearranging desks to improve airflow. The study highlights the importance of tailored ventilation strategies to enhance IAQ and occupant comfort in diverse spaces, providing insights applicable to similar building environments.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103580"},"PeriodicalIF":6.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling health impacts of individuals in industrial zones: Leveraging game theory-based LSTM approach for predictive analysis of public health dynamics","authors":"Deepalakshmi Perumalsamy ,&nbsp;Susymary Johnson ,&nbsp;Rajermani Thinakaran","doi":"10.1016/j.asej.2025.103579","DOIUrl":"10.1016/j.asej.2025.103579","url":null,"abstract":"<div><div>In recent years, the proliferation of industrial zones has raised concerns about the potential health impacts on nearby populations. Understanding and predicting these effects are crucial for policymakers and public health officials to implement targeted interventions. This study proposes a novel approach that combines game theory and Long Short-Term Memory (LSTM) networks to unravel the complex dynamics of public health in industrial zones. By incorporating game theory into predictive analysis, it can capture the multi-agent nature of decision-making processes that influence public health outcomes in industrial areas. Additionally, LSTM networks, a type of recurrent neural network, is well-suited for modeling the temporal aspects of health dynamics. The proposed framework uses historical data on industrial activities, environmental factors, demographic characteristics, and health outcomes to train the LSTM model. By integrating game theory principles, the model considers the strategic behavior of different factors and their impact on public health indicators over time. Through iterative learning and optimization, the model can generate predictive insights into future health trajectories in industrial zones. This application-driven framework leverages existing synergies between game theory and deep learning to model the strategic and temporal dynamics of public health in industrial zones. Overall, the integration of game theory and LSTM-based predictive analysis offers a promising avenue for understanding and addressing the health impacts of industrialization. The proposed method is implemented in Python software and has an accuracy of about 99.12 % which is 3.1 % higher than other existing methods like HealthFog, Conv LSTM and GoogleNet- Deep Neural Network (DNN).</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103579"},"PeriodicalIF":6.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imputation techniques utilizing higher order moment of a supplementary variable under simple random sampling","authors":"Nazish Masood ,&nbsp;Muhammad Iqbal ,&nbsp;Soofia Iftikhar ,&nbsp;Abdullah Mohammed Alomair","doi":"10.1016/j.asej.2025.103544","DOIUrl":"10.1016/j.asej.2025.103544","url":null,"abstract":"<div><div>This paper presents certain log-based categories of imputation techniques utilizing higher order moment of a supplementary variable under simple random sampling. The pertinent categories of point estimators have been developed to estimate the population mean. Bias and MSE expressions are derived up to the approximation of first order. The performance of suggested estimators are investigated in relation to the estimators proposed by Bahl and Tuteja <span><span>[2]</span></span>, Izunobi and Onyeka <span><span>[10]</span></span> and Zaman and Iftikhar <span><span>[28]</span></span>. The comparative analysis shows that the suggested estimators outperform those suggested by Bahl and Tuteja <span><span>[2]</span></span>, Izunobi and Onyeka <span><span>[10]</span></span> and Zaman and Iftikhar <span><span>[28]</span></span>. The theoretical results are supported by a numerical study on two real populations as well as a simulation study using a hypothetical population. A simulation study also reveals that the suggested estimators performed superior at high correlation than at low correlation.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103544"},"PeriodicalIF":6.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TNeXt: A convolutional neural network for remote harbor classification","authors":"Mert Gurturk ,&nbsp;Sengul Dogan ,&nbsp;Turker Tuncer","doi":"10.1016/j.asej.2025.103545","DOIUrl":"10.1016/j.asej.2025.103545","url":null,"abstract":"<div><div>Harbor recognition from aerial images faces two main challenges: deep models are often too large for real-time use on drones, and there is no large, diverse harbor dataset to train them. This work overcomes these obstacles in two ways.</div><div>We built the Turkish Harbor Image Dataset (THID) by flying a UAV over 207 harbors in Türkiye and capturing 13,199 clear-weather images. We split THID into 73.4 % training, 18.3 % validation, and 8.3 % test sets, and applied simple augmentations (rotations, flips) to improve robustness.</div><div>TNeXt, a fully convolutional network is proposed in this research. On THID, TNeXt achieved 97.71 % accuracy. Without changing its architecture, it scored 83.30 % top-1 on ImageNet1k. For the UC-Merced Land Use dataset, TNeXt reached 97.14 % accuracy; when used as a feature extractor in a simple pipeline, it hit 99.76 %.</div><div>This research provides high accuracy and rapid inference and is therefore suitable for real-time harbor detection for autonomous platforms.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103545"},"PeriodicalIF":6.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based optimization model for document layout and text recognition","authors":"R. Rajan ,&nbsp;M.S. Geetha Devasena","doi":"10.1016/j.asej.2025.103587","DOIUrl":"10.1016/j.asej.2025.103587","url":null,"abstract":"<div><div>In this study, we use deep learning approaches to offer a novel method for layout anchor box recognition and text analysis in scanned documents. Due to differences in layout, picture quality, and text orientations, scanned documents sometimes provide difficulties. As a result, our goal is to create a reliable deep learning model that can recognize anchor boxes and extract important data from scanned papers. In this study, we introduced the DeepDoc method, a deep learning-based strategy for analyzing document layouts. First, DeepDoc detects semantic structure of document including abstract, title etc. Then, the data is preprocessed and fed into optimal feature selection approach based on Coati’s Optimization Algorithm (COA). The YOLOv3 used to analyze the document completely based on the optimum features learned by COA algorithm. The proposed deep learning model outperforms existing approaches and shows promising solution for document analysis, archiving, and information retrieval.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103587"},"PeriodicalIF":6.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive synergistic evolution characteristics of soil arching in supported arch-locked colluvial landslides","authors":"Miao Ren","doi":"10.1016/j.asej.2025.103572","DOIUrl":"10.1016/j.asej.2025.103572","url":null,"abstract":"<div><div>A self-designed landslide physical model system was employed to conduct tests with arch support spacings of 5 cm, 7 cm, and 9 cm. The system utilized tensioned reinforcement bars for activation, while stress–strain data acquisition and high-resolution cameras monitored multi-dimensional deformation characteristics. Results indicate that under optimal arch spacing, the anti-sliding force stabilizes after reaching its peak. Cyclic formation and collapse of soil arches drive the transition from rapid sliding to creep sliding. Crack evolution exhibits stage-specific patterns: rear tensile cracks synchronize with peak anti-sliding force, whereas dynamic migration of shear cracks between support arches reveals a progressive failure sequence—rupture, peak, sub-instability, and instability. Post-peak residual strength remains high, but increased sliding mobility distinguishes static/dynamic sub-instability stages with reduced residual resistance. Displacement synchronization analysis demonstrates a strong correlation between anti-sliding force and displacement synergy coefficients, providing quantitative criteria for identifying sub-instability stages.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103572"},"PeriodicalIF":6.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing knee osteoarthritis severity diagnostics: A GA-enhanced deep ensemble approach in medical imaging","authors":"Thien B. Nguyen-Tat ,&nbsp;Truc-Phuong Nguyen-Duong","doi":"10.1016/j.asej.2025.103524","DOIUrl":"10.1016/j.asej.2025.103524","url":null,"abstract":"<div><div>Knee osteoarthritis (OA) is a common degenerative condition that impairs mobility, particularly in older adults. Early and accurate diagnosis is vital for effective treatment and improved patient outcomes. This study proposes a deep learning model for automatic OA classification from X-ray images, optimized using a Genetic Algorithm (GA) to enhance performance. A diverse, expert-annotated dataset was used, with YOLOv8 employed for image cropping to focus on key knee regions. Multiple deep learning models, including SE-ResNeXt, ConvNeXt, and EfficientNet, were combined into an ensemble optimized by GA. The model achieved 95% accuracy in classifying OA severity levels (normal, mild, severe) and outperformed traditional diagnostic methods in accuracy and consistency. Grad-CAM visualizations highlighted critical diagnostic regions, supporting clinical interpretability. The proposed approach shows promise for assisting radiologists in efficient OA diagnosis, reducing workloads, and improving diagnostic precision. Further validation on larger datasets will ensure broader applicability.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 9","pages":"Article 103524"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlinear coupled dynamics of a wearable sensor in the presence of a soft dielectric layer undergoing squeezing motion","authors":"Mohammad Asadi ,&nbsp;Mousa Rezaee ,&nbsp;Ghader Rezazadeh ,&nbsp;Saber Azizi ,&nbsp;Hadi Madinei","doi":"10.1016/j.asej.2025.103575","DOIUrl":"10.1016/j.asej.2025.103575","url":null,"abstract":"<div><div>This article presents a modelling approach for wearable capacitive sensors utilizing dielectric materials with low Young’s modulus and high polarization capability. The objective is to reduce the working voltage to ensure compatibility with the human body, enabling the sensor to be used as a wearable device. In contrast to conventional modelling techniques, which often employ beams on elastic foundations with continuous springs, this study considers the inertial forces of the dielectric layer. The study derives and discretizes the nonlinear motion equations of the microbeam and the elastomeric dielectric layer and investigates the steady-state response of a system subjected to pressure with constant and harmonic fluctuation components, as well as a biasing voltage, using a learning approach. The study concludes that the elastomeric dielectric layer can significantly enhance the system’s performance. This is because it allows for the creation and production of wearable sensors that require less electrostatic voltage to operate.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 10","pages":"Article 103575"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}