Showing papers in "Archives of Computational Methods in Engineering in 2021"

Multiscale modeling meets machine learning: What can we learn?

Abstract: Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem

Abstract: Meta-heuristics are problem-independent optimization techniques which provide an optimal solution by exploring and exploiting the entire search space iteratively. These techniques have been successfully engaged to solve distinct real-life and multidisciplinary problems. A good amount of literature has been already published on the design and role of various meta-heuristic algorithms and on their variants. The aim of this study is to present a comprehensive analysis of nature-inspired meta-heuristic utilized in the domain of feature selection. A systematic review methodology has been used for synthesis and analysis of one hundered and seventy six articles. It is one of the important multidisciplinary research areas that assist in finding an optimal set of features so that a better rate of classification can be achieved. The concept of feature selection process along with relevance and redundancy metric is briefly elucidated. A categorical list of nature-inspired meta-heuristic techniques has been presented. The major applications of these techniques are explored to highlight the least and most explored areas. The area of disease diagnosis has been extensively assessed. In addition, the special attention has been given on highlighting the role and performance of binary and chaotic variants of different nature-inspired meta-heuristic techniques. The summary of nature-inspired meta-heuristic methods and their variants along with datasets, performance (mean, best, worst, error rate and standard deviation) is also depicted. In addition, the detailed publication trend of meta-heuristic feature selection approaches has also been presented. The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem.

Applications of Generative Adversarial Networks (GANs): An Updated Review

Abstract: Generative adversarial networks (GANs) present a way to learn deep representations without extensively annotated training data. These networks achieve learning through deriving back propagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in several applications. GANs have made significant advancements and tremendous performance in numerous applications. The essential applications include semantic image editing, style transfer, image synthesis, image super-resolution and classification. This paper aims to present an overview of GANs, its different variants, and potential application in various domains. The paper attempts to identify GANs’ advantages, disadvantages and significant challenges to the successful implementation of GAN in different application areas. The main intention of this paper is to explore and present a comprehensive review of the crucial applications of GANs covering a variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.

Computer Vision Techniques in Construction: A Critical Review

Abstract: Computer vision has been gaining interest in a wide range of research areas in recent years, from medical to industrial robotics. The architecture, engineering and construction and facility management sector ranks as one of the most intensive fields where vision-based systems/methods are used to facilitate decision making processes during the construction phase. Construction sites make efficient monitoring extremely tedious and difficult due to clutter and disorder. Extensive research has been carried out to investigate the potential to utilise computer vision for assisting on-site managerial tasks. This paper reviews studies on computer vision in the past decade, with a focus on state-of-the-art methods in a typical vision-based scheme, and discusses challenges associated with their application. This research aims to guide practitioners to successfully find suitable approaches for a particular project.

Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review

Abstract: Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

A Survey of Learning-Based Intelligent Optimization Algorithms

Abstract: A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.

A Review and State of Art of Internet of Things (IoT)

Abstract: The Internet of Things (IoT) is basically like a system for connecting computer devices, mechanical and digital machines, objects, or individuals provided with the unique system (UIDs) and without transfer to transmit data over an ability human-to-human or computer-to-human relation. Another thing on the internet is that the items in the IoT are like a connected manner with humans and computers to which internet protocol addresses can be assigned and which can transfer data over the network or another man-made object. In this paper, we describe the utilization of IoT in the cloud, fog, IoT technologies with applications and security. Specifically, we provide IoT architecture for design and development with sensors in 6G. Finally, we discuss the current research, solutions, and present open issues of future research in IoT.

Ant Lion Optimizer: A Comprehensive Survey of Its Variants and Applications

Abstract: This paper introduces a comprehensive overview of the Ant Lion Optimizer (ALO). ALO is a novel metaheuristic swarm-based approach introduced by Mirjalili in 2015 to emulate the hunting behavior of ant lions in nature life. The review is highlighted the applications that are utilized ALO algorithm to solve various optimization problems. In ALO, the best solution is determined to enhance the performance of the functional and efficient during the optimization process by finding the minimum or maximum values to solve a certain problem. Metaheuristic algorithms have become the focus of research due to introduce of decision-making and asses the benefits in solving various optimization problems. Also, a review of ALO variants is presented in this paper such as binary, modification, hybridization, enhanced, and others. The classifications of the ALO’s applications include the benchmark functions, machine learning applications, networks applications, engineering applications, software engineering, and Image processing. Finally, According to the reviewed papers published in the literature, the ALO algorithm is mostly utilized in solving various optimization problems. Presenting an overview and reviewing the ALO applications are the main aims of this review paper.

A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems

Abstract: The ever-increasing demands for resource-saving, engineering technology progress, and environmental protection stimulate the progress of the progressive design method. As an excellent promising design method for dealing with the inevitable uncertainty factors, reliability-based design optimization (RBDO) is capable of offering reliable and robust results and minimizing the cost under the prescribed uncertainty level, which can provide a trade-off between economy and safety. However, the primary challenges, including global convergence capacity and complicated mixed design variable type, hinder the wider application of RBDO. This study presents a comprehensive work on the application of ten popular and recent metaheuristic algorithms of five engineering problems. Furthermore, we focus on the RBDO equip with metaheuristic algorithms about its global convergence, robustness, accuracy, and computational speed. This paper also presents the major difference of convergence property between metaheuristic algorithms and gradient algorithms. The detailed statement of this study presents the state-of-the-art in RBDO to demonstrate its crucial technologies and great challenges, as well as the beneficial future development direction.

A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges

Abstract: Internet of Things (IoT) is widely accepted technology in both industrial as well as academic field. The objective of IoT is to combine the physical environment with the cyber world and create one big intelligent network. This technology has been applied to various application domains such as developing smart home, smart cities, healthcare applications, wireless sensor networks, cloud environment, enterprise network, web applications, and smart grid technologies. These wide emerging applications in variety of domains raise many security issues such as protecting devices and network, attacks in IoT networks, and managing resource-constrained IoT networks. To address the scalability and resource-constrained security issues, many security solutions have been proposed for IoT such as web application firewalls and intrusion detection systems. In this paper, a comprehensive survey on Intrusion Detection System (IDS) for IoT is presented for years 2015–2019. We have discussed various IDS placement strategies and IDS analysis strategies in IoT architecture. The paper discusses various intrusions in IoT, along with Machine Learning (ML) and Deep Learning (DL) techniques for detecting attacks in IoT networks. The paper also discusses security issues and challenges in IoT.

The Revolution of Blockchain: State-of-the-Art and Research Challenges

Abstract: With the rapid development of Information Technology (IT) industries, data or information security has become one of the critical issues. Nowadays, Blockchain technology is widely using for improving data security. It is a tool for the individual and organization to interchange the digital asset without the intervention of a trusted third party i.e. a central administrator. This technology has given the ability to create digital tokens for representing assets, innovation and likely reshaping the scenery of entrepreneurship. Blockchain has several key properties, such as decentralization, immutability and transparency without using a trusted third party. It can be used in several fields, such as healthcare, digital voting, Internet of Things (IoT) and many more. This study aims to discuss the fundamentals of Blockchain. In this paper, the technology or working procedure of Blockchain including many applications in several fields are discussed. Finally, future work directions and open research challenges in the domain of Blockchain have been also discussed in detail.

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Abstract: Metamodels aim to approximate characteristics of functions or systems from the knowledge extracted on only a finite number of samples. In recent years kriging has emerged as a widely applied metamodeling technique for resource-intensive computational experiments. However its prediction quality is highly dependent on the size and distribution of the given training points. Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention. These techniques aim to find pertinent points in an iterative manner based on information extracted from the current metamodel. A review of adaptive schemes for kriging proposed in the literature is presented in this article. The objective is to provide the reader with an overview of the main principles of adaptive techniques, and insightful details to pertinently employ available tools depending on the application at hand. In this context commonly applied strategies are compared with regards to their characteristics and approximation capabilities. In light of these experiments, it is found that the success of a scheme depends on the features of a specific problem and the goal of the analysis. In order to facilitate the entry into adaptive sampling a guide is provided. All experiments described herein are replicable using a provided open source toolbox.

Sign Language Recognition Systems: A Decade Systematic Literature Review

Abstract: Despite the importance of sign language recognition systems, there is a lack of a Systematic Literature Review and a classification scheme for it. This is the first identifiable academic literature review of sign language recognition systems. It provides an academic database of literature between the duration of 2007–2017 and proposes a classification scheme to classify the research articles. Three hundred and ninety six research articles were identified and reviewed for their direct relevance to sign language recognition systems. One hundred and seventeen research articles were subsequently selected, reviewed and classified. Each of 117 selected papers was categorized on the basis of twenty five sign languages and were further compared on the basis of six dimensions (data acquisition techniques, static/dynamic signs, signing mode, single/double handed signs, classification technique and recognition rate). The Systematic Literature Review and classification process was verified independently. Literature findings of this paper indicate that the major research on sign language recognition has been performed on static, isolated and single handed signs using camera. Overall, it will be hoped that the study may provide readers and researchers a roadmap to guide future research and facilitate knowledge accumulation and creation into the field of sign language recognition.

A Systematic Review of Hidden Markov Models and Their Applications

Abstract: The hidden Markov models are statistical models used in many real-world applications and communities. The use of hidden Markov models has become predominant in the last decades, as evidenced by a large number of published papers. In this survey, 146 papers (101 from Journals and 45 from Conferences/Workshops) from 93 Journals and 44 Conferences/Workshops are considered. The authors evaluate the literature based on hidden Markov model variants that have been applied to various application fields. The paper represents a short but comprehensive description of research on hidden Markov model and its variants for various applications. The paper shows the significant trends in the research on hidden Markov model variants and their applications.

UAV Communication Networks Issues: A Review

Abstract: The unmanned aerial vehicle communication networks (UAVCN) comprises of a collection of unmanned aerial vehicles (UAVs) to build a network that can be used for many applications. These nodes autonomously fly in free space in ad-hoc mode to carry out the mission. However, the UAVs face some challenging issues during collaboration and communication. These nodes have high speed, hence the communication links fail to route the traffic that affects the routing mechanism. Therefore, UAVCN communication affecting the quality of service and facing the performance issue. Power is another major problem to limit and optimize the use of power, the energy-efficient mechanism is needed. In this paper, an attempt is made to explore the issues of unmanned aerial vehicle communication networks: UAVCN characteristics, UAVCN design issues, UAVCN applications, routing protocols, quality of service, power issue and identify the future open research areas which could be considered for further research to explore the UAVCN technology.

Image Fusion Techniques: A Survey

Abstract: The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

Performance Assessment of Concrete and Steel Material Models in LS-DYNA for Enhanced Numerical Simulation, A State of the Art Review

Abstract: One of the biggest challenges associated with modelling the behaviour of reinforced concrete is the difficulty of incorporating realistic material models that can represent the observable behaviour of the physical system. Experiments for relevant loading rates and pressures reveal that steel and concrete exhibits a complicated nonlinear behavior that is difficult to capture in a single constitutive model. LS-DYNA provides several material models to simulate the structural behaviour of reinforced concrete. To provide some guidance in selecting the proper one for users who have limited experience on numerical simulation of steel and concrete, this paper reviews the background theory and evaluates performance of different material models to predicting the response of reinforced concrete structures to dynamic loads as well as advantageous and disadvantageous of models. Comparisons of several widely available concrete constitutive models are presented pertaining to their ability to reproduce basic laboratory data for concrete and steel as well as predict the response of structures subjected to shock and impact loadings. The performance of these models was assessed by comparison of finite element analysis model and experimental results of reinforced concrete structures to insure that the overall behaviour prediction is qualitatively acceptable, even if the exact parameter fit or material characterization is not available. The authors concluded that the accuracy of the finite element results relied on the selection of material model as well as the input parameter values. The material model assessment presented in this study can be used in the numerical simulation to generate appropriate models for concrete and steel.

Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

Abstract: Fault detection prior to their occurrence or complete shut-down in induction motor is essential for the industries. The fault detection based on condition monitoring techniques and application of machine learning have tremendous potential. The power of machine learning can be harnessed and optimally used for fault detection. The faults especially in induction motor needs to be addressed at a proper time for avoiding losses. Machine learning algorithm applications in the domain of fault detection provides a reliable and effective solution for preventive maintenance. This paper presents a review of the machine learning algorithm applications in fault detection in induction motors. This paper also presents the future prospects and challenges for an efficient machine learning based fault detection systems.

State-of-the-Art Review on Responses of RC Structures Subjected to Lateral Impact Loads

Abstract: Reinforced concrete structures and structural members used in strategic infrastructures such as highway bridges, high-rise buildings, etc. are inherently subjected to lateral impact loads arising from the collision of vehicles, vessels, falling rocks, and rigid objects having different impact geometries, weights, and velocities. Due to the brittle nature of concrete materials, both localized and overall failure modes are very likely to occur in concrete structures under dynamic and impulsive loads. Hence, many attempts have been carried out in the literature to recognize the failure behaviors and to assess the vulnerability of concrete structure under lateral impact loads. This paper presents a comprehensive state-of-the-art review on the responses and failure behaviors of various types of concrete structures and structural members subjected to lateral impact loads based on analytical, numerical, and experimental studies carried out by the previous research works. In addition, the influences of various structural- and load-related parameters on the impact resistance and failure behaviors of different concrete structures under lateral impact loads are reviewed.

Applications of Artificial Intelligence in Oil and Gas Development

Abstract: Artificial intelligence has been back on the stage of research works in all the walks in recently years, the sharply increase of AI-based work have shown its potential to be a future direction for almost all disciplines. In oil and gas industry, AI technology is also doubtlessly a new shining star that draws attention from researchers devoted themselves into it. In order to dig up more about the applications of artificial intelligence in oilfield development for a hint of the future trend of this exciting technology in oil and gas industry, literature investigation of a large amount of AI-based work reported has been conducted in this work. Based on the investigation, the application of AI in important issues in oilfield development including oilfield production dynamic prediction, developing plan optimization, residual oil identification, fracture identification, and enhanced oil recovery are specifically investigated and summarized, the backs and cons of existing AI algorithms has been compared. Based on the analysis and discussion, current situation of the application of AI in oilfield development is concluded, and suggestions and potential directions of future work AI application in oil and gas developing are provided.

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges.

Abstract: Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

Methodological-Technological Framework for Construction 4.0

Abstract: The construction industry has traditionally been characterised by the high diversity of its agents and processes, high resistance to change and low incorporation of technology compared to manufacturing industries However, the construction sector is experiencing now a strong renovation process in methodology and tools due to the incorporation of the Building Information Modelling, Lean Construction and Integrated Project Delivery Meanwhile, in production systems, “Industry 40” is a new paradigm that proposes automation, monitoring, sensorisation, robotisation, and digitalisation to improve production and distribution processes In this context, some authors have proposed the concept of “Construction 40” as the counterpart of Industry 40 for the construction sector, although the methodological-technological implications are not clear This research shows a methodological-technological framework adapted to the Architecture, Engineering, Construction, and Operations industry This papers includes a detailed proposal for a reference frameworks and related technologies that could impact on this sector, responding to its complexities and specific challenges, such as the unique spaces for each work, which are difficult to standardise, arbitrary cost overruns and a productivity far below the average for other industries, increasing competitiveness and globalisation, as opposed to its traditionally local deployment, and an increasing demand to reduce the carbon footprint for all its activities

Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review

Abstract: Intelligence technologies play an important role in increasing product quality and production efficiency in digital aquaculture. Automatic fish detection will contribute to achieving intelligent production and scientific management in precision farming. Due to the availability and ubiquity of modern information technology, such as the internet of things, big data, and camera devices, computer vision techniques, as an essential branch of artificial intelligence, have emerged as a powerful tool for achieving automatic fish detection. At present, it has been widely used in fish species identification, counting, and behavior analysis. Nevertheless, computer vision modeling used for fish detection is riddled with many challenges, such as varies in illumination, low contrast, high noise, fish deformation, frequent occlusion, and dynamic background. Hence, this paper provides a comprehensive review of the computer vision model for fish detection under unique application scenarios. Firstly, the image acquisition system based on 2D and 3D is discussed. Further, many fish detection techniques are categorized as appearance-based, motion-based, and deep learning. In addition, applications of fish detection and public open-source datasets are also presented in the literature. Finally, the prominent findings and the directions of future research are addressed toward the advancement in the aquaculture field throughout the discussion and conclusion section.

Histogram Equalization Variants as Optimization Problems: A Review

Abstract: In the consumer electronics field, the main challenge in image processing is to preserve the original brightness. Histogram Equalization (HE) is one of the simplest and widely used methods for contrast enhancement. However, HE does not suit into the consumer electronics field as this procedure flattens the histogram by distributing the entire gray levels uniformly. Therefore, several HE variants have been proposed based on proper histogram segmentation, histogram weighting, and range optimization techniques to overcome this flattening effect. However, sometimes these modifications become complex and computationally expensive. Recently, researchers have formulated the HE variants for image enhancement as optimization problems and solved, using Nature-Inspired Optimization Algorithms (NIOA), which starts a new era in the image enhancement field. This study presents an up-to-date review over the application of NIOAs for HE variants in image enhancement domain. The main issues which are involved in the application of NIOAs with HE are also discussed here.

Stock Market Forecasting Using Computational Intelligence: A Survey

Abstract: Stock market plays a key role in economical and social organization of a country. Stock market forecasting is highly demanding and most challenging task for investors, professional analyst and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. Prediction of stock market is a crucial task and prominent research area in financial domain as investing in stock market involves higher risk. However with the development of computational intelligent methods it is possible to reduce most of the risk. In this survey paper, our focus is on application of computational intelligent approaches such as artificial neural network, fuzzy logic, genetic algorithms and other evolutionary techniques for stock market forecasting. This paper presents an up-to-date survey of existing literature on stock market forecasting based on computational intelligent methods. In this article, the selected papers are organized and discussed according to six main point of view: (1) the stock market analyzed and the related dataset, (2) the type of input variables investigated, (3) the pre-processing techniques used, (4) the feature selection techniques to choose effective variables, (5) the forecasting models to deal with the stock price forecasting problem and (6) performance metrics utilized to evaluate the models. The major contribution of this work is to provide the researcher and financial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques.

Advances in Spotted Hyena Optimizer: A Comprehensive Survey

Abstract: Metaheuristic algorithms are widely used in various fields of optimization engineering. These algorithms have become popular because of their ability to explore and exploit solutions in various problem areas. The Spotted Hyena Optimizer (SHO) algorithm is a metaheuristic algorithm inspired by the life of spotted hyenas, introduced by Dhiman and Kumar (2017) to solve continuous optimization problems. Various studies have been performed based on changes in the SHO algorithm to solve various problems due to its effectiveness and success in solving continuous problems. This paper aims to comprehensively survey the application of the SHO algorithm in solving various optimization problems. In this paper, SHO algorithms are categorized based on hybridization, improvement, SHO variants, and optimization problems. This study invites researchers and developers of meta-heuristic algorithms to employ the SHO algorithm for solving diverse problems since it is a simple and robust algorithm for solving intricate and NP-hard problems. Based on the studies, it was concluded that the SHO algorithm had been used more in optimization problems. The purpose of optimization problems is to find optimal solutions and finding global points in the problem environment. Also, the SHO algorithm establishes a good trade-off between the exploration and extraction stages. Based on the done studies and investigations, properties and factors of the SHO algorithm are better than another meta-heuristic algorithms, which has increased its adaptability and flexibility in different fields.

Layerwise Theories of Laminated Composite Structures and Their Applications: A Review

Abstract: It is very challenging to accurately analyze the displacement and stress fields in the laminated composite structures due to the transverse anisotropy and higher transverse flexibilities. The equivalent single-layer theory (EST) is first developed to simplify this complex 3D problem to a pure 2D problem based on a displacement assumption in the thickness direction. The EST gives good results for the global responses of the very thin laminates with minimum computation cost, but poor results for the local responses at the interfaces and can not presents the zig-zag distribution of in-plane displacements. The elasticity solutions based on the 3D displacement-based finite element method (FEM) can present the accurate displacement and stress fields, but requires huge computational cost. As a quasi 3D method, the layerwise theories (LWT) is more accurate than most of the ESTs, and its computational cost is less than that of the 3D-based displacement FEMs, so it is attracting more and more attention with the rapid development of computer technology. This paper presents an overall review for the LWTs of the laminated composite structures and their applications. In general, there are two basic schemes employed to establish a LWT according to the construction of the displacements fields in the thickness direction. In the first scheme, the laminated composite structures are described as an assembly of individual layers, and these individual layers are combined by the interlaminar relationships to keep the displacement continuity and stress equilibrium. All of the individual layers are simulated by using the EST. In the second scheme, the displacement and/or stress fields along the thickness direction are constructed by a 1D interpolation functions and the in-plane displacement and/or stress fields are discretized by the 2D finite elements. The review in this paper is organized by the this classification.

From Photonic Crystals to Seismic Metamaterials: A Review via Phononic Crystals and Acoustic Metamaterials

Abstract: The advancement in electromagnetic metamaterials, which commenced three decades ago, experienced a rapid transformation into acoustic and elastic systems in the forms of phononic crystals and acoustic/elastic metamaterials. Since its early discovery, numerous wave phenomena alongside the possible engineering applications have been highlighted. The existing and emerging fields of metamaterials are far more extensive, ranging from optics to acoustic, and all the way to the elastic systems. Numerous fantastic dynamic properties in optics and acoustic/elastic systems have been reported to date, which cannot be found in naturally occurring materials. The present review tends to discuss the historical context, current progresses and possible future outcomes of metamaterials. The fascinating phenomena observed in optics/electromagnetic metamaterials have been explained and linked with acoustic and elastic counterparts. The idea of perfect lens that is governed by negative permittivity and negative permeability via left-handed materials with negative refractive index properties and the transformation optics for invisibility cloaks and optical rainbow effect alongside the hyperbolic metamaterials are reviewed and discussed. Furthermore, the associated transformation into acoustic and elastic focusing effects via graded index metamaterials, acoustic/elastic invisibility cloaks, transformational acoustics, and seismology and metawedges resembling optical rainbow effects and the likes are explained. The present state of the art has been examined and the physics involved in the governing of those peculiar wave mechanisms has been highlighted. Starting from photonic crystals, phononic crystals and acoustic metamaterials, the present state of the art research in some subfields of acoustic metamaterials has been outlined, such as metasurfaces, topological phononic crystals and seismic metamaterials, the three exciting and emerging research topics. The substantial challenges involved in these realms are characterised and the possible future outcome is further evaluated. This review article may assist researchers and engineers to grasp the idea of metamaterials in not only photonic and phononic crystal systems, but also the counterpart subfields.

Latest Advancements in Heat Transfer Enhancement in the Micro-channel Heat Sinks: A Review

Abstract: Miniaturization of the energy systems and high powered electronic devices necessitates the high capacity compact heat exchangers to dissipate the heat generated. Microchannel heatsinks (MCHS) are modern heat exchangers with the fluid flowing channels of size in microscale. These are very compact heat exchangers with higher ratios of heat transfer area to the volume. Huge research work has been going on to improve the hydraulic and thermal performance of the MCHS. This article provides the information about experimental and numerical studies that has been done on the heat transfer and its enhancement in micro-scale cooling devices. This review mainly concentrate on the heat transfer enhancement techniques in microchannel, numerical methods that has been implemented for the study of micro-channels and the parameters which effects the heat transfer rate. The recent studies on microchannel heat sink to improve its performance by geometry modifications, jet impingement, using Nano fluids, flow boiling and Magneto-hydrodynamics are thoroughly discussed in this article.

Recent Trends in Prediction of Concrete Elements Behavior Using Soft Computing (2010–2020)

Abstract: Soft computing (SC), due to its high abilities to solve the complex problems with uncertainty and multiple parameters, has been widely investigated and used, especially in structural engineering. They have successfully estimated the capacity of structural reinforced concrete (RC) members and determined the properties of concrete. There are so many articles in literature that applied SC methods for the above goals. However, there is no work to present the capability of such approaches by providing an overview on the available and existing studies. The lack of state-of-the-art review on the subject is the main motivation to present a comprehensive review on the latest trends between 2010 and 2020 in predicting the behavior of concrete elements using soft computing methods. The considered RC structural elements are beams, columns, joints, slabs, frames, concrete filled tube sections and strengthened elements with fibre reinforced polymer. The purpose of the investigated works was predicting the concrete characteristics such as crack, bond, shrinkage, or the strength of the elements. The review showed that SC methods are powerful tools which could provide flexible computational techniques with high level of accuracy for civil engineering problems. However, most of the published works neglected to present the required details and mathematical framework.