Arabian Journal for Science and Engineering最新文献

{"title":"Dynamic Adsorption of Sulfate Propoxylate and Alkylbenzene Sulfonate Surfactants: Correlation with 3D Pore Geometry in Highly Heterogeneous High-Salinity Carbonate Reservoirs","authors":"Aleksei O. Malakhov,&nbsp;Emil R. Saifullin,&nbsp;Omar Al-Janabi,&nbsp;Aleksei O. Bykov,&nbsp;Dinara I. Zharkova,&nbsp;Sergei A. Nazarychev,&nbsp;Dmitrii A. Zharkov,&nbsp;Rail I. Kadyrov,&nbsp;Thanh H. Nguyen,&nbsp;Mikhail A. Varfolomeev","doi":"10.1007/s13369-025-10112-1","DOIUrl":"10.1007/s13369-025-10112-1","url":null,"abstract":"<div><p>Surfactant flooding is a well-established method for enhancing oil recovery in carbonate reservoirs with hydrophobic properties. The adsorption of surfactants onto carbonate rock surfaces alters wettability to a more hydrophilic state, improving oil extraction. However, surfactant adsorption can lead to losses, reducing the cost-effectiveness of the technique. This study evaluates the adsorption mechanisms of sulfate propoxylate and alkylbenzene sulfonate surfactants through dynamic adsorption experiments and 3D micro-CT analysis. Dynamic adsorption experiments were conducted following a classical methodology, including core preparation, brine saturation, surfactant solution flooding, and periodic fluid sampling to quantify adsorption. Core samples from Bashkirian and Vereian horizons, differing in pore structure, were analyzed. Low-resolution (57 µm) 3D micro-CT provided an overview of pore distribution and effective porosity, while high-resolution (6.4 µm) 3D micro-CT revealed ultra-small pores in the Bashkirian core. Dynamic adsorption and 3D micro-CT scans results indicated higher surfactant adsorption in the Vereian core, despite its lower effective porosity and total pore surface area. In the Bashkirian core, surfactant flooding predominated in larger connected pores (&gt; 0.1 mm), while smaller pores (&lt; 0.1 mm) were weakly or not involved. In contrast, the Vereian core’s more uniform large-pore distribution provided a higher accessible contact area for surfactants.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 13","pages":"10489 - 10506"},"PeriodicalIF":2.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Self-Healing and Plastic Shrinkage Reduction in Superabsorbent Polymer (SAP) Concrete: Synergistic Effects of Micro-silica and Fly Ash","authors":"Md. Taki Tajwar,&nbsp;Md. Tanvir Hasan Mahodi,&nbsp;G. M. Sadiqul Islam","doi":"10.1007/s13369-025-10108-x","DOIUrl":"10.1007/s13369-025-10108-x","url":null,"abstract":"<div><p>Concrete is susceptible to cracks that may arise from shrinkage under tensile stress, which reduces its mechanical strength and endangers the durability of structures. Superabsorbent polymers (SAP) have emerged as promising self-healing agents as they can reduce crack closure and help mitigate plastic shrinkage. However, incorporating SAP may increase porosity, reduce workability, and negatively influence the mechanical properties of cementitious composites. This study utilized micro-silica (MS) and fly ash (FA) to counteract the reduction in compressive strength and workability caused by SAP while enhancing the self-healing performance of mixtures incorporating these supplementary cementitious materials (SCMs). Thirteen mix proportions with varying SAP, MS, and FA replacements were analyzed to assess their effect on self-healing efficiency, mechanical performance, and shrinkage resistance. These specimens were preloaded to generate micro-cracks, and these pre-cracked specimens were exposed to a wet–dry cycle. The results demonstrate that the combination of SAP (0.2–0.4%) with FA (15–30%) accelerates crack closure and recovers workability up to 28%, while MS (3–5%) densifies the matrix, reducing permeability and recovering compressive strength up to 17%. Notably, a mixture of 0.4% SAP, 5% MS, and 15% FA achieves a maximum crack closure ratio of 97%, recovering the lost workability and restoring full compressive strength in the long term. This study represents a practical way to incorporate SAP for self-healing quality while maintaining workability and strength with the optimum proportions of MS and FA. The findings can contribute to developing long-lasting, self-healing concrete for real-world implications.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 20","pages":"17075 - 17090"},"PeriodicalIF":2.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Named Entity Recognition Based Neural Network Framework for Stock Trend Prediction Using Latent Dirichlet Allocation","authors":"Manas Ranjan Prusty,&nbsp;Apoorv Kumar Sinha,&nbsp;Sanskriti Sanjay Kumar Singh,&nbsp;Shreyas Sai,&nbsp;Vijayakumar Kedalu Poornachary,&nbsp;Subhra Rani Patra","doi":"10.1007/s13369-025-10090-4","DOIUrl":"10.1007/s13369-025-10090-4","url":null,"abstract":"<div><p>Stock price prediction is an extensively researched topic as the precise prophecy of stock trends is decisive in the investment marketing sphere. With increasing opinions by many market giants on the internet about given stocks, it surges the necessity to study these sentiments in detail for forthcoming predictions. From these articles on the internet, natural text is generated by examining factors that affect the values of stocks and therefore these texts are reliable features to go ahead with this study. The idea behind tackling such work is that conglomerates and businesses are able to tangibly understand the aftermath of articles that usually mobilize public opinion and gear them in a certain direction. The aim of this study is to utilize named entity recognition (NER) on a neural network framework for stock trend prediction through latent Dirichlet allocation using these natural texts generated from internet articles. This method is used to understand the words that occur at the highest frequency and add the most information to the corpus depending on the topic’s importance. With this, the model adopts K × K words that have the most decisive impact on the target class that has been created with which it alters the sparse density matrix that has been generated. The proposed model of the NER-based neural network was fitted on a real-world dataset, and its performance was good in comparison with state-of-the-art models developed by fellow researchers. However, since the model does not use the BERT tokenizers, it cannot be adjudged on the FinBERT model, and therefore, the preprocessed data is fed to a pruned recurrent neural network which is robustly stopped with a simple callback function. The final result was a strong 0.81 tetrachoric correlation between the testing target class and the predicted target class. With this, the model provides a different approach to natural language processing, especially those with high sparse density for stock prediction.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 19","pages":"16135 - 16148"},"PeriodicalIF":2.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FEDetect: A Federated Learning-Based Malware Detection and Classification Using Deep Neural Network Algorithms","authors":"Zeki Çıplak,&nbsp;Kazım Yıldız,&nbsp;Şahsene Altınkaya","doi":"10.1007/s13369-025-10043-x","DOIUrl":"10.1007/s13369-025-10043-x","url":null,"abstract":"<div><p>The growing importance of data security in modern information systems extends beyond the preventing malicious software and includes the critical topic of data privacy. Centralized data processing in traditional machine learning methods presents significant challenges, including greater risk of data breaches and attacks on centralized systems. This study addresses the critical issue of maintaining data privacy while obtaining effective malware detection and classification. The motivation stems from the growing requirement for robust and privacy-preserving machine learning methodologies in response to rising threats to centralized data systems. Federated learning offers a novel solution that eliminates the requirement for centralized data collecting while preserving privacy. In this paper, we investigate the performance of federated learning-based models and compare them classic non-federated approaches. Using the CIC-MalMem-2022 dataset, we built 22 models with feedforward neural networks and long short-term memory methods, including four non-federated models. The results show that federated learning performed outstanding performance with an accuracy of 0.999 in binary classification and 0.845 in multiclass classification, despite different numbers of users. This study contributes significantly to understanding the practical implementation and impact of federated learning. By examining the impact of various factors on classification performance, we highlight the potential of federated learning as a privacy-preserving alternative to centralized machine learning methods, filling a major gap in the field of secure data processing.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 19","pages":"16107 - 16134"},"PeriodicalIF":2.9,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-025-10043-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Planning of UAV Flocks via Transfer Learning and Multi-objective Optimization","authors":"Fahad Farooq,&nbsp;Zain Anwar Ali,&nbsp;Muhammad Shafiq,&nbsp;Amber Israr,&nbsp;Raza Hasan","doi":"10.1007/s13369-025-10064-6","DOIUrl":"10.1007/s13369-025-10064-6","url":null,"abstract":"<div><p>Multiple UAVs have been extensively deployed recently to reduce human workload, resulting in increased automation and efficiency. Path planning of numerous UAVs is a challenging optimization problem and a key component in various applications. Traditional strategies cannot provide accurate, optimal solutions rapidly in complex mission settings. In this context, flocks of birds exhibit intricate patterns of group escape when faced with predators. Local group interactions may lead to the autonomy of these patterns. However, most nature-inspired intelligent planning techniques have slow search speeds and easily fall into local areas. An intelligent planning method emulating the behavior of pigeons to achieve intelligence, safety, and consistency in UAV flocks in a complicated environment is designed. The combinatorial approach of pigeon-inspired optimization and transfer learning (TL-PIO) is the focus of the multi-objective optimization task. On the one hand, path planning and formation control of individual clusters with a dynamic agent are dealt with combinatorial efforts of multi-agent systems (MAS) and flocking model. On the other hand, swapping and synchronization of individual clusters construct flocks in a dynamic environment. Specifically, interaction and swapping positions of the best members among all clusters are involved to plan optimized paths and configure agents in one flock. Experimental results have been validated through a detailed numerical analysis of proposed algorithm over other combinatorial approaches, namely social learning pigeon-inspired optimization (SL-PIO), social learning particle swarm optimization (SL-PSO), and social learning ant colony optimization (SL-ACO). TL-PIO achieves an improvement of 25% over SL-PIO and 18% over SL-ACO in seven test functions and 15% over SL-PSO but only in five test functions. Outcomes reveal the developed approach has the fastest convergence rate and high local optimal avoidance and exploration ability, significantly reducing costs and illustrating supremacy over other methods. The presented work practically implies researchers and practitioners adopt it for distinct benefits in real-world applications.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 19","pages":"16089 - 16106"},"PeriodicalIF":2.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-025-10064-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Loading and Release of the Anti-Inflammatory Drug Lornoxicam Implementing Modified Mesoporous Silica KIT-5 Nanoparticles","authors":"Ali H. Khalbas,&nbsp;Talib M. Albayati,&nbsp;Issam K. Salih,&nbsp;Noori M. Cata Saady,&nbsp;Sohrab Zendehboudi","doi":"10.1007/s13369-025-10087-z","DOIUrl":"10.1007/s13369-025-10087-z","url":null,"abstract":"<div><p>Orally administered lornoxicam (LOX) requires relatively high doses to maintain its healing ability. The increased administration frequency could lead to adverse effects such as gastrointestinal damage, skin irritation, headache, and nausea, and in severe cases, it can lead to renal failure. Drug encapsulation onto mesoporous silica nanoparticles can be used to circumvent these challenges. This study synthesized the mesoporous silica nanoparticle KIT-5 and modified it with amine groups to form KIT5-NH₂ as a carrier for LOX drug delivery. The samples were characterized using X-ray diffraction, Fourier transforms infrared spectroscopy, N₂ adsorption–desorption, scanning electron microscopy, transmission electron microscopy, and thermogravimetric analysis. The loading efficiency was examined by studying the impact of carrier dosage, pH, LOX concentration, and contact time. In addition, the drug release was investigated using simulated fluids at pH 1.2 and pH 7.4. The results revealed a significant enhancement in LOX loading efficiency with KIT5-NH₂, reaching a maximum of 58.53%, while no loading was observed with pure KIT-5. The adsorption process followed Langmuir isotherms, with a maximum adsorption capacity of 25.87 mg/g, and the adsorption kinetics of LOX was well described by a pseudo-second-order model. Regarding drug release, the maximum release rate of LOX was 56.43% at pH 7.4 over 360 min, whereas a lower release rate of 39.15% was observed at pH 1.2. In addition, kinetic release models indicated that the release process is governed by the Fickian diffusion mechanism. Lastly, the cytotoxicity study showed that the synthesized formulation has no toxic effects and demonstrates good biocompatibility.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 13","pages":"10199 - 10217"},"PeriodicalIF":2.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Combined Influence of Load and Flowing Corrosion Solution on the Sulfate Resistance of Fly Ash-Based Geopolymer Concrete","authors":"Ying Zhao,&nbsp;Yingkun Xue,&nbsp;Wenrui Xu","doi":"10.1007/s13369-025-10093-1","DOIUrl":"10.1007/s13369-025-10093-1","url":null,"abstract":"<div><p>This study investigates the combined influence of loading and flowing corrosive solutions on the sulfate resistance of fly ash-based geopolymer concrete (FABGC) under real service environments. The results demonstrate that both loading and flowing water erosion accelerate the degradation of the FABGC, as evidenced by a reduction in the relative dynamic elastic modulus and an increase in the thickness of the damaged layer. Results show that SO₄²⁻ concentration within FABGC increases progressively with exposure time across all corrosion conditions, with loading significantly facilitating SO₄²⁻ transport, particularly at higher load levels. The combination of loading and flowing water erosion not only accelerates SO₄²⁻ penetration but also enhances the formation of corrosion products through microscopic analyses, specifically ettringite (AFt) and gypsum. While the bending load optimizes the internal structure of the compression zone, it will introduce microstructural damage in the tension zone, thereby potentially accelerating the transport of SO₄²⁻ and modulating the effects of flowing water erosion. Load-induced microcrack expansion significantly enhances SO₄²⁻ transport, accelerating the formation of corrosion products such as AFt and gypsum. This process leads to the increase of porosity, particularly in the proportion of large pores. These results demonstrate that the combined influence of loading and flowing water erosion creates a synergistic effect on the durability of FABGC. This underscores the critical importance of considering these combined effects in the design and long-term maintenance of FABGC structures exposed to severe environmental.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 20","pages":"17063 - 17074"},"PeriodicalIF":2.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fabrication of Biodegradable and Sustainable Hemp Hurds/PVA Composite Membrane and Application to Particulate Matter Filtration","authors":"Hülya Aykaç Özen,&nbsp;Tuğba Mutuk,&nbsp;Onur Yontar,&nbsp;Mevlüt Gürbüz","doi":"10.1007/s13369-025-10092-2","DOIUrl":"10.1007/s13369-025-10092-2","url":null,"abstract":"<div><p>Particulate matter (PM) pollution has emerged as one of the most serious environmental problems, because of its detrimental impact on the well-being of people. Consequently, the development of a PM removal filter that is both economical and energy-efficient is imperative. This study investigates the fabrication and performance evaluation of biodegradable and sustainable nanofiber membranes composed of waste hemp hurds and polyvinyl alcohol (PVA) for particulate matter PM filtration. The fabrication of the hemp/PVA composite membranes was accomplished via electrospinning, with the hemp hurds powder content ranging from 1 to 20 wt%. A comprehensive analysis was subsequently performed on the morphology, thermal properties, and filtration performance of the resulting membranes. Filtration tests using cigarette smoke revealed exceptional PM removal efficiencies, with the 15 wt% HH/PVA membrane achieving the highest efficiency of 98.74% for PM_2.5 and 98.73% for PM₁₀, alongside a superior quality factor of 0.049 Pa⁻¹. The membranes demonstrated enhanced PM capture due to increased surface roughness and particle collision probability. These findings suggest that hemp-doped membranes are viable eco-friendly alternatives to petroleum-based filters, providing efficient PM filtration with sustainable benefits.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 13","pages":"10185 - 10198"},"PeriodicalIF":2.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-025-10092-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Technique for Optimization of Artificial Neural Network Using Ensemble of Chimp, Harris Hawks and Manta Ray Foraging Optimization Algorithms for Enhancing Software Maintainability Prediction","authors":"Varun Goel,&nbsp;Arvinder Kaur","doi":"10.1007/s13369-025-10031-1","DOIUrl":"10.1007/s13369-025-10031-1","url":null,"abstract":"<div><p>For improving software maintainability prediction (SMP), an efficient method for SMP is required in early stages of SDLC to avoid higher maintenance costs involved with the software. To address this issue, we have used three metaheuristic algorithms, i.e., chimp optimization algorithm (ChOA), Harris hawks optimization (HHO) and manta ray foraging optimization (MRFO) algorithm. Chimp optimization algorithm has two variants: ChOA1 and ChOA2 depending on the dynamic coefficients of h vector. These metaheuristic algorithms are used for artificial neural network (multilayer perceptron) hyperparameters optimization which can be useful for improving the predictive capability of ANN which will then correctly predict software maintainability (change) using various OO metrics. In this study, a novel HCHHMRFO algorithm has been developed to select the best hyperparameters of ANN (multilayer perceptron) to build the model, which can predict software maintainability accurately. Performance evaluation of models developed after hyperparameters tuning with ChOA1, ChOA2, HHO, MRFO, Ensemble of ChOA, HHO and MRFO (ChOA1_HHO_MRFO and ChOA2_HHO_MRFO) is performed using performance indicators including MAE and RMSE. UIMS and QUES datasets have been used to assess this work. Tent map and singer map are the best chaotic maps (in terms of iterations; RMSE; and MAE, respectively) that are used for ANN’s hyperparameters initialization on UIMS dataset. Gauss/mouse map and tent map are the best chaotic maps (in terms of iterations; RMSE and MAE, respectively) that are used for ANN’s hyperparameters initialization on the QUES dataset. Comparison results suggest that ChOA2_HHO_MRFO Ensemble is found to give better results to tune ANN hyperparameters for SMP for both the datasets, i.e., UIMS and QUES.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 19","pages":"16053 - 16087"},"PeriodicalIF":2.9,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Introduction to New Elastic Foundation Element Developed in OpenSees: A Simplified Model to Simulate Soil–Structure Interaction","authors":"Morvarid Hajian,&nbsp;Reza Attarnejad","doi":"10.1007/s13369-025-10078-0","DOIUrl":"10.1007/s13369-025-10078-0","url":null,"abstract":"<div><p>In engineering practice, the dynamic analysis of structures subjected to seismic excitations often assumes a fixed base, neglecting the critical effects of soil–structure interaction (SSI). This simplification is especially problematic for structures on soft soils, where soil flexibility significantly alters the dynamic response. To address this, a novel stiffness formulation for soil–foundation systems was proposed, overcoming the limitations of existing approaches by more accurately capturing soil–foundation behavior. Building on this advancement, the study developed a new soil–foundation element, named <i>ElasticFoundation</i>, in OpenSees, enabling efficient incorporation of SSI effects into structural analysis. The ElasticFoundation element was validated through nonlinear analyses of 25 three-dimensional numerical models of buildings with 1, 3, 5, 10, and 15 stories, from which stiffness matrices were derived. Experimental data further confirmed its accuracy, with both the proposed model and experimental prototype showing maximum roof deflections of 24 mm. The average inter-story drift values for the model and prototype were 0.01 and 0.02, respectively, demonstrating comparable behavior within acceptable tolerances. The element captures critical parameters influencing SSI, including foundation dimensions, foundation vertical safety factor (FSV, the ratio of ultimate vertical load capacity to the applied load), soil properties, superstructure height, and the structure's fundamental period. This dual contribution—an innovative soil–foundation stiffness formulation and its implementation as a computationally efficient finite element in OpenSees alternative to full continuum soil modeling—offers a robust tool for SSI analysis, bridging the gap between accuracy and practicality in structural dynamics.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"50 20","pages":"17045 - 17062"},"PeriodicalIF":2.9,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}