Archives of Computational Methods in Engineering最新文献

In the realm of modern cryptography, error-correcting codes emerge as a robust approach to secure communication. Polar codes, harnessing the power of channel polarization, offer near-linear encoding and decoding complexity while achieving Shannon capacity. Thus, polar codes are useful for Code-based Secret Sharing (CSS), Quantum Key Distribution (QKD), and Code-Based Cryptography (CBC). Therefore, the analysis of polar codes’ potential is imperative for several reasons: polar codes garner substantial attention due to error-correction capability, cryptography plays a pivotal role in securing communications and data, making it crucial to assess the efficacy of polar codes in encryption, secret sharing, and QKD, and polar codes help to identify vulnerabilities and weaknesses when incorporated into cryptographic schemes. Our survey represents the first comprehensive analysis of polar codes in CBC, CSS, and QKD. We delve into the practical implementation of polar codes within McEliece’s system and key reconciliation protocols, conducting detailed simulations to assess their strengths and weaknesses. We consider decoding attack cost, maximum Frame Error Rate (FER), and the efficiency of the key reconciliation protocol. Moreover, we offer insights into designing efficient polar code-based cryptographic algorithms for post-quantum security, making this survey a valuable resource for academia and industries alike.

The study of punching shear has gained prominence in structural engineering, particularly over the past decade, due to the abrupt nature of this failure mode and its potentially severe consequences. While certain aspects of punching shear have been extensively investigated, notable gaps persist in the literature. These include the analysis of punching under specific boundary conditions, slabs constructed with alternative materials to conventional reinforced concrete, and solutions such as shear reinforcements and structural strengthening techniques. Although experimental studies dominate the field, many numerical investigations using the finite element method have emerged worldwide, offering valuable insights into this complex phenomenon. This review aims to identify and highlight these research gaps by examining published studies on the numerical simulation of punching shear in slabs. Key aspects of numerical modeling are presented alongside the main conclusions achieved so far while outlining promising future research directions. Potential future research avenues include the numerical simulation of punching shear under dynamic loading conditions, such as seismic actions; the analysis of slabs incorporating novel construction materials, such as lightweight concrete or fiber-reinforced composites; the development and assessment of advanced shear reinforcement systems; and the exploration of punching in complex slab systems, including ribbed slabs, sandwich panels, and biaxial hollow-core slabs. This review seeks to contribute to advancing numerical modeling techniques and understanding punching shear behavior in contemporary structural applications, by addressing these topics.

The Aquila Optimizer (AO) algorithm is a well-known Swarm-based nature-inspired optimization algorithm inspired by Aquila’s behavior in hunting and catching prey. Since its development by Abualigah et al. (Comput Methods Appl Mech Eng 376:113609, 2021), AO has gained significant interest among researchers. It has been widely applied across various fields to solve optimization problems, owing to its simplicity, ease of implementation, and reasonable execution time. The main purpose of this paper is to provide a comprehensive survey of the AO algorithm and its improved variants (multi-objective, modified, and hybridized). It also illustrates the various applications of the AO algorithm in several domains of problems such as image processing, feature selection, economic load dispatch, wireless sensor networks, photovoltaic power systems, Unmanned Aerial Vehicles (UAVs) path planning, optimal parameter control, and vehicle routing problems. Furthermore, the results of the AO algorithm are compared with some well-known optimization meta-heuristics published in the literature, such as Differential Evolution (DF), Firefly Algorithm (FA), Bat Algorithm (BA), Grey Wolf Optimization (GWO), Moth Flame Optimization (MFO), and Multi-Verse Optimizer (MVO). Finally, the paper concludes with some future research directions for the AO algorithm.

The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has transformed machining processes, significantly boosting efficiency, accuracy, and sustainability. This systematic review analyzes 182 research articles, categorized into eight thematic clusters using VOSviewer software, based on author keywords from the Scopus database, following the PRISMA framework. These clusters comprise ‘advanced sensing and prognostics,’ ‘machine learning and optimization in manufacturing,’ sustainability group (‘energy efficiency and optimization techniques’, ‘smart and sustainable manufacturing’, ‘neural networks and energy management’), ‘intelligent machining processes,’ ‘advanced algorithms in machining,’ ‘lubrication and tool wear management,’ ‘CNC and deep learning applications,’ and ‘digital twins. A critical literature review of each cluster was conducted to identify key trends, challenges, and developments in AI, ML, and DL applied in machining operations. The vital results are presented in table format. The review reveals that AI-driven machining has significantly enhanced predictive maintenance, real-time process monitoring, and energy optimization, resulting in a reduction of machining energy consumption by up to 20%. ML and DL models have improved machining accuracy, tool wear prediction, and adaptive process control. While progress has been made, difficulties persist in merging AI models with industrial systems. This review also highlights significant research gaps in data quality, system adaptability, and the scalability of AI solutions when integrating AI and ML with practical machining applications. The review addresses these gaps by proposing techniques that improve model accuracy and reliability across various machining contexts and provides a roadmap for future advancements in intelligent manufacturing systems.

Engineering optimization provides efficient designs that balance performance with resource demand. Metaheuristic algorithms excel at this task, but their lack of adaptability across different problems limits their search capability. In this regard, integrating these methods with auxiliary decision-making mechanisms based on fuzzy logic can considerably improve their search ability. Fuzzy logic empowers these algorithms to adapt their search behavior dynamically based on specific problem characteristics. The current study assesses how this integration improves search efficiency and adaptability to complex and uncertain scenarios, ultimately leading to more effective solutions in engineering optimization. To this end, different fuzzy-reinforced metaheuristic approaches are evaluated, and their search capabilities are compared among themselves and against their standard versions. The selected methods were thoroughly assessed from diverse aspects, including search performance, behavioral process, computational cost, and stability across various problems (e.g., mathematical, mechanical, and structural problems). The acquired results are reported and discussed in detail. Consequently, the attained outcomes indicate that a proper fuzzy-based decision mechanism can considerably improve the search capability of metaheuristic algorithms.

The pivotal significance of lateral resisting elements comes to the fore in upholding the security and steadfastness of structures against lateral forces. However, predicting the lateral resistance of these elements is a complex and challenging task that requires considering various factors. Artificial intelligence (AI) techniques have emerged as a promising approach to predicting the lateral resistance of building elements. These techniques can analyze large amounts of data and extract patterns and relationships that are difficult to identify using traditional methods. Consequently, the present research augments the scholarly literature by conducting a methodical examination encompassing all principal facets concerning the lateral stabilizing components of edifices, employing principles derived from artificial intelligence paradigms during the most recent series of publication years. This research also presents an innovative lateral-resistant building taxonomy based on insightful ideas and explores work in various fields that contradict it. To achieve this, we reviewed the ScienceDirect, ASCE, Scopus, IEEE Xplore, and Web of Science databases to conduct this study. Between 2018 and 2024, 4039 papers were aggregated. The established inclusion criteria filtered the articles, resulting in 360 included articles. Six groups were categorized based on (1) moment-resisting frames, (2) braced frames, (3) shear walls, (4) hybrid systems, (5) control systems, and (6) failure mode and damage detection. This review, which adopts a systematic perspective, discusses the apparent benefits of the application of artificial intelligence and offers new research pathways for advancing multidisciplinary synergy. Well-structured, this study will be a handy and noteworthy read for structure researchers and engineers. Furthermore, it draws out issues within the peripheries of ongoing research, stating the value of AI and spotlighting a few recommendations for the adoption of such an approach in handling emerging research gaps significant for a future-proof multidisciplinary collaboration.

The Tunicate Swarm Algorithm (TSA) is a metaheuristic optimization method inspired by the navigation and feeding behaviors of marine tunicates, particularly their jet propulsion mechanics and swarm intelligence. TSA’s elegance lies in its core principles: collision avoidance through gravitational forces, optimal path identification via distance-based search, and swarm cohesion maintenance. Since its introduction in 2020, TSA has gained widespread attention for its simplicity, parameter efficiency, derivative-free operation, and robust convergence properties. This survey delves into TSA’s theoretical foundations and evolution, comprehensively reviewing its applications across diverse domains. A comparative study against six established algorithms on 23 benchmark functions highlights TSA’s superior performance. The algorithm has shown remarkable utility in fields such as computer science, engineering, and mathematics, experiencing exponential growth in adoption and citations. This review also explores TSA variants, including Chaotic TSA, Adaptive TSA, and hybrid approaches, analyzing their effectiveness across optimization challenges. Notable applications in power systems optimization, engineering design, medical image analysis, and network security are discussed with detailed insights into implementation strategies and performance metrics. Despite its strengths, TSA faces challenges in exploration and premature convergence on highly multimodal landscapes. The paper identifies promising research directions, such as quantum-inspired enhancements, distributed computing, and integration with Industry 4.0 technologies. This survey gives researchers and practitioners an in-depth understanding of TSA’s capabilities, limitations, and potential, positioning it as a transformative tool in computational intelligence and optimization.

Graph Neural Networks (GNNs) have emerged as a powerful tool for traffic flow prediction, demonstrating significant advancements in modelling complex spatial-temporal dependencies in traffic networks. This survey presents a comprehensive review of GNN applications in traffic flow prediction from 2020 to 2024, offering unique insights through an extensive quantitative analysis. Unlike previous reviews, our work provides an end-to-end examination of the entire prediction pipeline, from data processing to model deployment, with a particular focus on recent advancements in graph construction methods, feature engineering and network architectures. The key contributions of this survey are threefold: (1) We present a comparative analysis of model performance across multiple datasets and prediction horizons, evaluating around 40 state-of-the-art models on five major public datasets, spanning short-term (15 and 30-min) and long-term (60-min) prediction horizons. (2) We systematically organize and summarize different graph construction methods, feature selection and fusion techniques, and various structural designs in GNN-based traffic prediction. This includes a comprehensive examination of static, adaptive, and dynamic graph constructions, multi-view and hypergraph approaches, as well as emerging trends such as physics-informed GNNs and hybrid architectures. (3) We offer a critical analysis of real-world implementation challenges, including scalability, computational efficiency, and strategies for handling data quality issues, alongside identifying promising future research directions. By providing this comprehensive, quantitative evaluation alongside a thorough review of recent advancements, our survey offers researchers and practitioners a clear understanding of the current state-of-the-art in GNN-based traffic prediction.

Thin-walled tubes play a crucial role in frame structure design and are essential for achieving automotive lightweighting. In recent years, the integration of thin-walled tube structures with multi-material layouts has garnered significant attention due to its potential to further reduce structural weight and enhance static load-bearing capacity. This paper provides a comprehensive review of the current status of multi-material structure topology optimization design, highlighting significant advancements made over the past decades. Given that multi-material structure topology optimization methods are primarily developed based on single-material approaches, four commonly used single-material topology optimization methods are first briefly introduced. Subsequently, the key numerical implementation challenges in multi-material structure topology optimization are emphasized, including material description, finite element analysis techniques, and the selection of optimization solvers. Additionally, the performance improvement strategies for multi-material structure topology optimization are discussed, such as structural topology expression, methods for describing thin-walled tube features, and issues related to optimization accuracy and efficiency. To systematically elucidate the application of multi-material structural topology optimization, the primary applications at both macro-scale and multi-scale levels are also summarized. Finally, the future research directions in multi-material structural topology optimization are forecasted. It is evident that despite extensive studies by scholars using various topology optimization methods, multi-material structural topology optimization remains a novel, dynamic, and challenging research area. This paper provides comprehensive guidance for initial investigations into multi-material structural topology optimization of thin-walled tubes used in vehicles under static loads and offers valuable insights for further research.

The increasing use of concrete along with the emergence of innovative types of concrete necessitates in-depth knowledge regarding their performance. Given the inherent uncertainties, time constraints, and costs associated with traditional laboratory tests, the application of machine learning (ML) as a powerful technique of artificial intelligence (AI) has recently gained particular interest for predicting properties of concrete and optimizing concrete mixtures. Keeping that in mind, this review paper explores the application of ML models within the field of concrete technology, investigating various aspects. It includes the prediction of concrete properties, addressing classification challenges, and exploring advanced ML methodologies such as Automated ML, explainable AI, generative models, and counterfactual analysis. Furthermore, the paper emphasizes the critical importance of data preprocessing for optimizing the performance of these methods. In this regard, this paper serves as a comprehensive resource, providing researchers with a profound understanding of ML model development in the context of concrete technology.