Archives of Computational Methods in Engineering最新文献

The construction industry is on the brink of a transformative shift with the integration of Building Information Modelling (BIM) and Augmented Reality (AR) to enhance project efficiency and accuracy. This study presents a comprehensive analysis and model that outlines the potential of BIM-AR integration in optimizing design processes and minimizing reworks in the construction industry. The study applied a systematic literature review methodology to highlight the potential of this integration in revolutionising construction practices. Key findings reveal that this integration facilitates a robust digital-physical bridge, ensures real-time data accessibility, and extends across the project’s lifecycle. The model underscores the pivotal role of AR technologies and BIM authoring tools in realizing this potential, while also recognizing hardware constraints, software compatibility, and scalability as primary limitations. Remarkable challenges such as technology integration, data management, and user adoption are discussed, highlighting the need for industry-wide education and a cultural shift towards new technological practices. The study charts a future research trajectory focusing on standardization, affordable solutions, AI advancements, user experience, and sustainability investigations. By enabling superior visualization, communication, and collaboration, the BIM-AR convergence is set to revolutionize construction practices, driving the industry towards more sustainable, efficient, and error-minimized operations. This integration model serves as a roadmap for researchers and practitioners to outline the current state and future directions for BIM-AR in construction.

In the field of optimization problems, the optimization of energy systems problems is of significant importance, mainly due to their dramatic role in achieving sustainability. The complexity of energy systems optimization problems, intense constraints, and various decision variables have led many researchers to utilize meta-heuristics optimization algorithms to optimize such issues and improve energy systems. Meta-heuristic algorithms that can find global solutions and prevent trapping in local optima can efficiently solve energy systems problems. Grey Wolf Optimizer (GWO), one of the well-known meta-heuristic optimizers inspired by the grouped hunting process of wolves, has been employed in different studies to deal with energy systems optimization problems. GWO has received much attention in the literature due to its proper exploratory and exploitative features, rapid and mature convergence rate, and simplicity in design and coding. This paper reviews various GWO applications for tackling optimization problems related to production, conversion, transmission and distribution, storage, and energy consumption. It is highly believed that this paper can be a practical and innovative reference for researchers, professionals, and engineers.

Quaternions are extensively used in several fields including physics, applied mathematics, computer graphics, and control systems because of their notable and unique characteristics. Embedding quaternions into deep neural networks has attracted significant attention to neurocomputing researchers in recent years. Quaternion’s algebra helps to reconstruct neural networks in the quaternionic domain. This paper comprehensively reviewed and analyzed the recent advancements in quaternion deep neural networks (QDNNs) and their practical applications. Several architectures integrating quaternions in deep neural networks such as quaternion convolutional neural networks, quaternion recurrent neural networks, quaternion self-attention networks, hypercomplex convolutional neural networks, quaternion long-short term memory networks, quaternion residual networks, and quaternion variational autoencoders are thoroughly examined and reviewed with applications. It is observed that they have outperformed conventional real-valued neural networks. This study also discusses the main discoveries and possible advanced mechanisms of QDNN for future research. The open challenges and future scopes of QDNNs are also addressed, which provides the right direction of work in this field. This review may help researchers interested in architectural advancements and their practical applications.

The article provides a detailed review of the utilisation of machine learning (ML) in various domains of additive manufacturing (AM) and highlights its potential to address key challenges in the industry. The article acknowledges the hurdles to widespread adoption of AM, including barriers in design for AM (DfAM), limited materials selection, processing defects, and inconsistent product quality. ML is increasingly being integrated into AM workflows, offering significant potential for classification, regression, and clustering to address the AM challenges. It can be used to generate new high-performance metamaterials and optimize topological designs, improving the efficacy and usefulness of the design process. It also optimizes process parameters, monitors powder spreading, and detects in-process defects, enhancing the overall quality and reliability of the manufacturing process. ML aids in streamlining the production processes and ensuring consistent product quality. There's recognition of the importance of data security in AM, with ML techniques potentially posing risks of data breaches if not properly managed. Therefore, a synergistic approach where ML assists in identifying critical conditions and human operators take action is likely the most effective way to ensure both efficiency and accuracy in AM processes. The paper summarises the key results from the literature and discusses some significant applications of machine learning in AM. It emphasizes the potential of ML to drive innovation and address critical challenges in the AM industry. Overall, the article underscores the significance of ML in advancing AM technology and its potential to overcome existing barriers to adoption, making way for broader implementation of AM in various industries.

In recent years, the quest for optimizing metaheuristic algorithms has led to a surge in research efforts aimed at enhancing their performance. While existing reviews have diligently summarized these endeavors, they primarily focus on presenting the collective body of work undertaken to augment standard algorithms. In contrast, this paper takes a unique perspective by concentrating on the myriad methodologies employed by authors to improve one such algorithm, the Sine Cosine Algorithm (SCA). Our comprehensive review dissects the various strategies used to elevate the effectiveness of SCA variants, meticulously scrutinizing their advantages and disadvantages. This in-depth analysis extends beyond the confines of SCA and provides valuable insights into the broader landscape of metaheuristic optimization algorithms. By evaluating the pros and cons of these enhancement methods, our work forms a foundational review that can be applied to other optimization algorithms. Through this broader lens, we offer readers a comprehensive overview of the strategies adopted by researchers in recent years to enhance optimization algorithms, facilitating a deeper understanding of the advancement of this vital field. Our paper thus serves as a guidepost for researchers and practitioners navigating the ever-evolving terrain of metaheuristic optimization, shedding light on the strengths and potential pitfalls of enhancement methodologies. It provides a holistic perspective that empowers the community to make informed choices when selecting or devising strategies to optimize algorithms for diverse problem domains.

Scaffold represents important components of tissue engineering. Scaffold for bone tissue engineering needs to mimic bone structures that are heterogeneous and anisotropic. When using Triply Periodic Minimal Surfaces (TPMS) based unit cells to simulate bone structure for the additive manufacture of bone scaffolds, researchers frequently find a vast array of options for structural heterogeneity but not enough for material heterogeneity. The utilization of TPMS has led to a surge in the production of tissue engineering scaffolds by increasing the surface area to volume ratio, a crucial factor in vascularization and cell proliferation. Pore interconnectivity can be achieved more smoothly by using the TPMS unit cell for the making of scaffolds. This paper presents a comprehensive overview of TPMS-based (P-Primitive, Gyroid, and Double Diamond) bone scaffolds having both structural and material heterogeneity using composite material made of polymer Poly Lactic Acid (PLA) and ceramic Hydroxyapatite (HA) for 3D printing. As scaffolds should be biodegradable so polymer composites (PLA and Hydroxyapatite) have been studied to focus on their biodegradability and bioactivity. Material heterogeneity can be achieved by varying the composition of hydroxyapatite in PLA. Here, the hybridization of TPMS (P-Primitive, Gyroid, and Double Diamond) structures has been analyzed for making scaffolds that mimic human bone structures, and the best combination has been proposed.

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Ballast made up of discrete granular particles of rock is a principal component of railway tracks. This review paper focuses on using Discrete Element Modelling (DEM) for modelling ballast in railway track systems. It provides a comprehensive overview of past, present, and future challenges and developments in this area of research. The review discusses the various analysis principles used in DEM, including contact mechanics, representation of particle geometry, breakage and abrasion, and inclusions such as geosynthetics, fibres and rubber elements. It also describes the numerical interfaces between DEM and other analysis types (e.g., finite element modelling, multi-body dynamic, computational fluid dynamics, smoothed-particle hydrodynamics) that have been implemented to simulate ballasted railway-related problems, such as the subgrade modelled as a continuum and sleepers, and fluid and mechanical interactions, such as water washout, ballast flight, and track maintenance machines. Finally, the review outlines future challenges and directions for numerical analyses of ballasted railways.

Globally, the amount of wind turbines used to produce sustainable, renewable power is always increasing. Achieving dependable and easily accessible performance requires integrating innovative real-time condition monitoring technology. Ensuring the efficacy of wind power generation while maintaining its ability to generate revenue is fundamental. Machine learning (ML) has emerged as a crucial method for monitoring the condition of wind power systems in the past several years. This research study offers a comprehensive and current overview of contemporary condition monitoring technology employed in wind turbines for the purpose of detecting and predicting failures. Emphasizing machine learning algorithms for identifying significant faults and failure modes, preprocessing methods, and evaluation metrics, the review evaluates several references to determine past, present, and future developments in this field of study. Most of the analyzed references come from recent papers, reports, and journal articles that are freely available online.

This study provides an in-depth review and analysis of the Artificial Rabbit Optimization (ARO) algorithm inspired by the survival strategies of rabbits. The ARO tries to find the global solution in the search space according to the rabbits’ detour foraging strategy and searches locally according to their random hiding structure. This algorithm has various advantages such as a simple structure, fast running model, easy adaptation feature, few parameters, independent mechanism in exploration and exploitation phases, transitions between phases with a specific mechanism, reasonable convergence rate, and property of escaping local optima. Therefore, it has been preferred by many researchers to solve various complex optimization problems. ARO-based studies have been published at prestigious international publishers such as Elsevier, Springer, MDPI, and IEEE since its launch in July 2022. The rates of studies in these publishers are 34%, 19%, 18%, and 15%, respectively. The remaining 14% includes papers published by other publishers. Besides, the cited studies on this algorithm are examined in four categories: Improved, hybrid, variants, and adapted. Research trends demonstrate that 27%, 31%, 9%, and 33% of ARO-based studies fall into these categories.

The present work instigates a systematic literature review (SLR) methodology to highlight the most important studies and research progress on the vibration-induced sound radiation responses of laminated, sandwich composite, and functionally graded material (FGM) structures. It appraises the primary advances in computational methodologies, emphasizing the various mid-plane kinematics adopted and diverse schemes implemented for acquiring the vibroacoustic responses with and without considering environmental effects. The significant observations and research gaps where further research is needed for a more accurate estimation of the sound radiation characteristics of these advanced structures are outlined. The present review aims to put forward a broad perspective of the state-of-the-art related to structural–acoustic characteristics of composite and FGM plates and shells, specifically in hostile environments, to draw future research aspects.