Ain Shams Engineering Journal最新文献

{"title":"Quintic Hermite Spline Collocation Approach to the Time Fractional RLW and MRLW equations","authors":"Murat Arı","doi":"10.1016/j.asej.2026.104011","DOIUrl":"10.1016/j.asej.2026.104011","url":null,"abstract":"<div><div>This study presents a quintic Hermite spline-based collocation scheme for solving the Regularized Long-Wave (RLW) and Modified RLW equations in both integer-order and Caputo time-fractional settings. To the best of our knowledge, this represents the first application of this technique to these specific equations. Using Legendre and Chebyshev collocation points, the method is rigorously assessed through benchmark problems. A von Neumann analysis confirms the unconditional stability of the linearized scheme. Numerical results demonstrate that the proposed approach yields superior accuracy compared to existing methods, achieving high precision even on coarse spatial meshes. Additionally, the algorithm’s conservative nature is verified by the preservation of mass, momentum, and energy invariants. The findings establish the method as a robust and efficient framework for simulating complex fractional wave dynamics.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104011"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191325","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":"A neuro-climatic sensorless observer-based nonlinear controller for stabilization and robust MPPT in photovoltaic energy conversion systems based on immersion & invariance","authors":"Ambe Harrison ,&nbsp;Hassan M. Hussein Farh ,&nbsp;Wulfran Fendzi Mbasso ,&nbsp;Abdullrahman A. Al-Shamma’a ,&nbsp;Saad Mekhilef","doi":"10.1016/j.asej.2025.103967","DOIUrl":"10.1016/j.asej.2025.103967","url":null,"abstract":"<div><div>This paper proposes a novel sensorless nonlinear controller for Photovoltaic Energy Conversion Systems (PECS) operating under stochastic climatic variations, uncertain system parameters, and plant-model mismatches. The inherent nonlinear dynamics of PECS, compounded by climatic variability and load &amp; system perturbations, challenge system stability and maximum power point tracking (MPPT) efficiency. To address this, a Neuro-Climatic Sensorless Observer-based nonlinear controller (NCSOBN) has been introduced. To tackle the stochastic problem of the PECS which often imposes the need for expensive climatic sensing, a neural network estimates the reference voltage trajectory without costly climatic sensors. A robust Immersion &amp; Invariance (I&amp;I)-based controller then stabilizes the PECS by submerging its dynamics into a lower-dimensional invariant manifold, ensuring convergence to the desired trajectory and robust MPPT under climatic uncertainties, load variations, and parametric disturbances. Unmeasured variables, such as inductor current, are reconstructed via I&amp;I observers, while a finite-time robust exact differentiator estimates voltage derivatives. Rigorous mathematical analysis substantiated by Lyapunov laws verifies the overall stability of the controller. From a control-theoretic perspective, benchmarks against PI, sliding mode, and integral Backstepping controllers demonstrate NCSOBN’s superior convergence speed (1.9 ms), zero steady-state error, and resilience to internal/external uncertainties. In the realm of PECS, benchmarking against existing MPPT methods, reveals surpassing efficiency exceeding 99.7 % with very rapid convergence. Embedded digital implementation and experimental verification using STM32 card confirms practical viability. This piece of work advances robust control paradigms for renewable energy systems, offering a computationally efficient, sensor-reduced solution for real-world PECS deployment.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 103967"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190857","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":"Synergistic effect of carbon fiber, fly ash, and M-sand on the performance of Engineered Cementitious Composites (ECC)","authors":"Asmitha Nazeema Raheem ,&nbsp;Rama Mohan Rao Pannem","doi":"10.1016/j.asej.2026.104023","DOIUrl":"10.1016/j.asej.2026.104023","url":null,"abstract":"<div><div>Concrete is known for its strength and other properties. But it lacks tensile capacity, due to which reinforcements are used in concrete. Engineered Cementitious Composites (ECC) are a ductile material that is known for its strain hardening with multiple cracking behaviour. This paper focuses on the development of ECC using carbon fiber and M−Sand. M−Sand was chosen as fine aggregate due to its easy availability, quality, and increasing scarcity of natural river sand. The effect of different dosages of carbon fiber on workability was investigated, with M−Sand providing sustainable and practical alternative to river sand. FE-SEM and Micro CT scan was conducted to analyse the microstructure of ECC. A high strength of 73 MPa was achieved with 0.25% carbon fiber and low w/b, while exhibiting good workability. Replacing sand with fly ash improved workability, reduced porosity, and improved strength.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104023"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190862","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":"An iterative confidence-driven discrepancy-aware federated weakly supervised model for spine segmentation in 3D medical images","authors":"Pei Wang ,&nbsp;Jiansong Fan ,&nbsp;Xiang Pan ,&nbsp;Wei Chu","doi":"10.1016/j.asej.2026.104015","DOIUrl":"10.1016/j.asej.2026.104015","url":null,"abstract":"<div><div>Spinal disorders can cause neurological dysfunction and disability, with diagnosis relying on medical image segmentation. Traditional deep learning requires costly pixel-level annotations. Weakly supervised semantic segmentation with image-level labels reduces this cost, but CNNs struggle with long-range dependencies, leading to under-activated class activation maps (CAMs). Vision Transformers (ViTs) partly address this but cause over-activation. Data privacy constraints hinder large-scale dataset construction, and Federated Learning (FL) introduces Non-IID challenges. We propose ICDE, a federated weakly supervised framework addressing over-activation and Non-IID issues. A Progressive Reliable Mask Refinement (PRMR) module expands pseudo-labels from high-confidence regions to alleviate over-activation. A Class Prototype Optimization (CPO) module enhances PRMR via prototype–patch similarity. For Non-IID FL, a Confidence Discrepancy-aware Pseudo-label Correction (CDPC) mechanism balances global–local models for reliable pseudo-labels. Our framework achieves state-of-the-art performance on CTSpine1K, MRSpineSeg2021, and SPIDER2023, demonstrating strong robustness in addressing the complex and nonlinear characteristics of pathological spinal anatomies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104015"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190856","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":"Intelligent loading and unloading stages of loader based on data-driven control","authors":"Bingwei Cao ,&nbsp;Guangliang Tian ,&nbsp;Changhao Mu ,&nbsp;Junda Li","doi":"10.1016/j.asej.2026.104020","DOIUrl":"10.1016/j.asej.2026.104020","url":null,"abstract":"<div><div>Dynamically changing state variables increase the complexity of the loader during the Loading and Unloading Stages (LUSs). These variables include the height of the truck, the position of the stockpile, and the attitude of the working device. This paper proposes a novel data-driven control strategy to automate the complex, dynamic LUSs of loaders. Specifically, data-driven control strategies automatically lift, flip, and lower the bucket. These strategies ensure that the task requirements of different operation stages are correctly completed. Experimental validation revealed a control deviation in the attitude of the bucket. Based on the analysis, it was clarified that the lag of the control signal was the main factor causing the deviation. Subsequently, this paper proposes a control strategy based on Machine Learning (ML) that maintains error within 0.4% when the engine speed is relatively stable. This paper provides a clear, intelligent operation realization scheme for earthmoving machines.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104020"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190855","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":"Stochastic resonant NLSE with cubic-quintic-septic-nonlocal nonlinearity: Soliton dynamics and noise-induced phenomena","authors":"Nafissa T. Trouba ,&nbsp;Huiying Xu ,&nbsp;Reham M.A. Shohib ,&nbsp;Mohamed E.M. Alngar ,&nbsp;Walaa Mahfouz ,&nbsp;Xinzhong Zhu ,&nbsp;Mahmoud Mohamed Alderiny","doi":"10.1016/j.asej.2025.103961","DOIUrl":"10.1016/j.asej.2025.103961","url":null,"abstract":"<div><div>This research examines the stochastic resonant nonlinear Schrödinger equation (NLSE) with cubic-quintic-septic nonlocal nonlinearity in the It ô framework, incorporating spatio-temporal dispersion (STD), inter-modal dispersion (IMD), resonant nonlinearities, and multiplicative white noise. Using Kudryashov’s method and the <span><math><msup><mi>P</mi><mrow><mn>6</mn></mrow></msup><mo>−</mo></math></span>model expansion approach, novel families of bright, dark, singular, Jacobi-elliptic, and periodic stochastic solitons are derived, with exact constraint conditions ensuring physical validity. Numerical simulations reveal noise-induced amplitude modulation, phase jitter, rogue-wave-like spikes, and stochastic resonance phenomena that transition periodic backgrounds into emergent soliton structures. These findings demonstrate multiplicative noise’s dual role in destabilizing conventional solitons while generating resonance-enhanced coherent waveforms, filling the gap in unified models combining higher-order nonlinearities, nonlocality, and stochasticity. The numerical results were obtained using the split-step Fourier method to validate the analytical findings. The results advance soliton theory for realistic optical fibers, photonic lattices, metamaterials, and plasma systems, enabling noise-robust signal transmission and nonlinear wave control.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 103961"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190842","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":"GreenMix-pareto: Uncertainty-aware, physics-guided multi-objective optimization of low-carbon concrete mix designs","authors":"Tarek Salem Abdennaji ,&nbsp;Amel Ksibi ,&nbsp;Aymen Flah ,&nbsp;Vandna Batra ,&nbsp;Rupesh Kumar Tipu","doi":"10.1016/j.asej.2026.104009","DOIUrl":"10.1016/j.asej.2026.104009","url":null,"abstract":"<div><div>Concrete mix design increasingly requires balancing mechanical performance with climate and resource impacts. Most existing data-driven studies either optimize a single target (often strength) or report point predictions without calibrated uncertainty, which limits reliability-aware and constructible Pareto decision-making. <em>GreenMix-Pareto</em> addresses this gap through an end-to-end workflow that combines (i) physics-guided multi-task learning with monotonicity constraints, (ii) distribution-free conformal calibration for decision-grade uncertainty, (iii) data-driven feasibility screening, and (iv) hybrid multi-objective optimization to produce robust Pareto sets. Using a dataset of <span><math><mi>N</mi><mo>=</mo><mn>1000</mn></math></span> mixes (cement, water, superplasticizer, coarse/fine aggregates, age), the method derives domain ratios (e.g., water–cement ratio, sand ratio, paste fraction) and trains a monotone-constrained, multi-output gradient-boosting model to jointly predict compressive strength, embodied carbon dioxide (CO₂), energy, and resource use. Conformalized quantile regression provides calibrated prediction intervals that support chance constraints on strength using robust lower bounds. A learned feasibility filter (one-class support vector machine (SVM) + kernel density estimation (KDE)), together with broad domain bounds, restricts the search to plausible and constructible mixtures. The optimization stage combines Expected Hypervolume Improvement (EHVI) Bayesian optimization with Non-dominated Sorting Genetic Algorithm II (NSGA-II) refinement to discover dense, uncertainty-aware Pareto fronts. On held-out tests, the physics-guided model achieves strong accuracy for strength (RMSE <span><math><mo>=</mo><mn>0.58</mn></math></span> MPa, <span><math><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.997</mn></math></span>) while preserving physically credible gradients. For strength, conformal prediction reaches near-nominal marginal coverage (e.g., 0.92 at 90%). The resulting Pareto fronts expose actionable eco–strength trade-offs; a representative knee solution achieves <span><math><mo>∼</mo></math></span>50 MPa strength with <span><math><mo>∼</mo></math></span>220 kg CO₂ and <span><math><mo>∼</mo></math></span>1159 MJ per m³ inside the learned feasible region. Evaluation uses five independent stratified 80/20 splits with age-bin control; reported test metrics are averaged across splits. SHAP and partial-dependence analyses align with mixture science (impacts driven by cement; strength governed by <span><math><mi>w</mi><mrow><mo>/</mo></mrow><mi>c</mi></math></span> and age). The workflow supports reliability-aware strength targeting while reducing embodied impacts in low-carbon concrete design.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104009"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191330","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":"Experimental visualization of soil arching evolution and collapse in pile-supported embankments under cyclic loading","authors":"Qizhi Chen ,&nbsp;Hongruyi Wei ,&nbsp;Xinpeng Li ,&nbsp;Baoping Zou ,&nbsp;Yanshen Deng ,&nbsp;Bowen Kong","doi":"10.1016/j.asej.2026.104059","DOIUrl":"10.1016/j.asej.2026.104059","url":null,"abstract":"<div><div>Soil arching is a fundamental mechanism governing the stability of pile-supported embankments, a widely adopted solution for constructing transportation infrastructure on soft soils. However, the internal evolution and degradation of soil arching under long-term traffic-induced cyclic loading remain inadequately understood, limiting the ability to assess long-term performance. This study presents an innovative experimental framework combining transparent soil and Particle Image Velocimetry (PIV) to directly visualize and quantify the internal deformation and stress redistribution within embankment fills subjected to cyclic loading. The results, obtained from model tests with a normalized embankment height (<span><math><mi>H</mi><mrow><mo>/</mo></mrow><mi>B</mi></math></span>) ranging from 1.5 to 2.5, reveal that the stability and failure mode of soil arching are predominantly controlled by <span><math><mi>H</mi><mrow><mo>/</mo></mrow><mi>B</mi></math></span>. Two distinct collapse mechanisms are identified: (i) an immediate global collapse for low embankments (<span><math><mi>H</mi><mrow><mo>/</mo></mrow><mi>B</mi><mo>=</mo><mn>1.5</mn></math></span>) attributed to insufficient confining stress, and (ii) a progressive failure for higher embankments (<span><math><mi>H</mi><mrow><mo>/</mo></mrow><mi>B</mi><mo>≥</mo><mn>2.0</mn></math></span>), characterized by the cumulative upward migration of a shear plane. The visualized particle-scale kinematics provide some new insights into the mechanics of arching degradation under cyclic conditions. These findings advance the fundamental understanding of soil arching evolution and offer qualitative references for the performance assessment of transportation embankments within the investigated parameter range.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104059"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422174","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":"Improved prediction of geopolymer concrete compressive strength using metaheuristic-enhanced extreme learning models with combined SHAP and LIME for sustainable construction","authors":"Meysam Alizamir ,&nbsp;Aliakbar Gholampour ,&nbsp;Sungwon Kim ,&nbsp;Salim Heddam ,&nbsp;Gum-Sung Ryu","doi":"10.1016/j.asej.2026.104043","DOIUrl":"10.1016/j.asej.2026.104043","url":null,"abstract":"<div><div>The building sector is a major contributor to global carbon dioxide emissions, underscoring the need for environmentally sustainable construction materials. Geopolymer concrete offers an eco-friendly alternative to conventional cement-based concrete. Because experimental determination of its mechanical properties is resource‑intensive, this study develops seven innovative hybrid extreme learning machine (ELM) models, each enhanced with a different metaheuristic optimization algorithm, to improve prediction of compressive strength in geopolymer concrete. These models are benchmarked against a standard ELM and multiple linear regression (MLR) baseline. To support interpretability, SHapley additive exPlanations (SHAP), Local Interpretable Model Agnostic Explanation (LIME), and SelectKBest are applied to rank feature importance, demonstrate variable interactions, and quantify relationships. Model performance is evaluated using mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), Nash-Sutcliffe efficiency coefficient (NSE), Kling-Gupta efficiency (KGE), and Willmott’s Index (WI). The results reveal that the ELM combined with invasive weed optimization (ELM-IWO) demonstrates the highest accuracy among all models with RMSE = 2.928 MPa, R = 0.964, and MAE = 2.261 MPa. Strong performance is also obtained by ELM‑GWO (RMSE = 3.031 MPa, R = 0.963, MAE = 2.260 MPa) and ELM‑MFO (RMSE = 3.049 MPa, R = 0.963, MAE = 2.433 MPa), while the MLR baseline shows the weakest performance (RMSE = 3.974 MPa, R = 0.926, MAE = 3.105 MPa). The findings confirm the effectiveness of metaheuristic-enhanced ELM models for accurate compressive strength forecasting when appropriate variables are incorporated. The outcomes offer practical value for reducing laboratory workload and supporting the development of eco‑friendly construction materials through more efficient prediction processes.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104043"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147422246","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":"A complete dynamical analysis of a discontinuously driven resonance circuit","authors":"Barka Infal ,&nbsp;Muhammad Muddassar ,&nbsp;Adil Jhangeer","doi":"10.1016/j.asej.2026.104005","DOIUrl":"10.1016/j.asej.2026.104005","url":null,"abstract":"<div><div>Multistable systems with non-smooth dynamics provide serious challenges to the engineering design. Using a suite of standard numerical tools, our analysis of a canonical non-smooth system: a resonant circuit with an asymmetric cubic restoring force fed by a discontinuous square wave voltage, offers two important contributions to the study of such systems. First, the discontinuous forcing itself causes the basins of attraction with riddled boundaries to be created, which is a fundamental sensitivity that cannot be predicted by local analysis. Second, our Lyapunov landscape analysis finds the coefficient of quadratic nonlinearity, <span><math><msub><mi>l</mi><mn>2</mn></msub></math></span>, to be a significant critical stability switch, which can be directly used to control the predictability of the system. Lastly, we prove this control by achieving chaos synchronization and determining the critical coupling necessary to stabilize a problem stabilize synchronously as the required. This description of the global dynamics of the system is crucial for achieving robust performance in systems that incorporate both electronic and mechanical components.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"17 3","pages":"Article 104005"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057588","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}