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Archives of Computational Methods in Engineering
Journal Prestige (SJR): 1.41  Citation Impact (citeScore): 5 Number of Followers: 5  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1886-1784 - ISSN (Online) 1134-3060 Published by Springer-Verlag  [2484 journals]
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- Investigation and Implementation of Model Order Reduction Technique for
Large Scale Dynamical Systems-
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Abstract: Model Order Reduction (MOR) has demonstrated its robustness and wide use in engineering and science for the simulation of large-scale mathematical systems over the last few decades. MOR is currently being intensively optimized for dynamic systems that are becoming increasingly complex. MOR broad applications have been identified not only in the modeling but also for optimization and control engineering applications. In the present article, various methods related to MOR for large-scale linear and nonlinear dynamic systems have been analyzed, mainly pertaining to electrical power systems, control engineering, computational system theory, and design. The paper focuses on a detailed theoretic Perspective for MOR of the large-scale dynamical system to address the key challenges to the approximation along with their application. Firstly, a complete description of the literature search for various approximation techniques has been presented and the various inferences have been mentioned as the outcome. One of the drawbacks that we have found out of the investigation, has been taken as a sample problem. The key demerit of the balanced truncation approach is that the ROM steady-state values do not correspond with the higher-order system (HOS). This drawback has been eliminated in the proposed approach, which leads to the hybridization of balanced truncation and singular perturbation approximation (SPA) into a novel reduction method without the loss of retaining its dynamic behavior. The reduced system has been so designed to preserve the complete parameters of the original system with reasonable accuracy. The approach is based on the retention of the dominant states or modes of the system and is comparatively less important once. The reduced system evolves from the preservation of the dominant states or modes of the original system and thus retains stability intact. The methodology presented has been tested on two typical numerical examples taken from the literature review, to examine the performance, precision, and comparison with other available order reduction methods.
PubDate: 2022-01-14
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- A Comprehensive Review on High-Fidelity and Metamodel-Based Optimization
of Composite Laminates-
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Abstract: Composite laminates have found wide-ranging applications in various areas of structural, marine and aerospace industries. Their design and optimization is a challenging task due to involvement of a large number of design variables. Because of high accuracy of the laminate modeling theories and presence of numerous design variables, laminate design and optimization is primarily carried out in silico. Integratin the high accuracy of these laminate modeling theories using numerical solvers, like finite element method, boundary element method etc. with the iterative improvement capability of different optimization algorithms is a well-established approach and can be broadly referred to as high-fidelity optimization. However, in recent times with the advent of machine learning and statistical approaches, metamodel-based optimization has gained significant prominence, primarily due to its less computational time and effort. In this review paper, the essence of nearly 300 research articles (about 26% and 50% of them are from last 5 and 10 years respectively.) on high-fidelity and metamodel-based optimization of composite laminates is comprehensively assessed and presented. Special emphasis is provided on the discussion of various metamodels. The methodology and key outputs of each research article are concisely presented in this paper, which would make it an asset for the future researchers and design engineers.
PubDate: 2022-01-13
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- A Comprehensive Survey on the Recent Variants and Applications of
Membrane-Inspired Evolutionary Algorithms-
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Abstract: In the last decade, the application of membrane-inspired evolutionary algorithms in real-life problems has attracted much attention due to their flexibility and parallelizability. Almost seven years have passed since the first membrane algorithms survey paper was published in 2014. Considering the importance and ongoing research on such algorithms and their applications in various disciplines, this paper presents a comprehensive review of the published literature and suggests future directions. This review aims to summarize and analyze membrane algorithms based on the used nature-inspired algorithm, membrane structure, membrane rules, and their merits and demerits. Furthermore, an extensive bibliography about their real-world applications is presented.
PubDate: 2022-01-12
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- A Review of Recent Progress in Molecular Dynamics and Coarse-Grain
Simulations Assisted Understanding of Wettability-
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Abstract: Wetting phenomena are ubiquitous in nature and technology. An accurate control in designing novel self-clean coating material requires a profound understanding of importance of low surface energy materials and surface topography. Computational modelling and simulations play a significant role to gain an in-depth knowledge of wettability phenomena. In this review, the previously attempted theoretical and computational studies employing different polymeric materials are revealed. In particular, authors focus on literature wherein wetting by water droplets, parameters that influence the wetting characteristics, different contact angle (CA) measurement techniques, and wetting of different polymeric materials have been addressed. Recent advancements in construction of different water models, their meticulous simulation, and the application of advanced computational tools have increasingly prompted to realize more realistic substrate-fluid models (polymer-water droplet) deeply. Finally, perspectives on theoretical modelling and simulations in the fields of wettability estimation are presented in the section of concluding remarks and outlook. Overall, this review brings together and highlights the significant advancements that aid in an improved understanding of wettability to enable early-stage researchers to prudently plan, simulate and design novel self-clean coating materials by overcoming limitations related to atomistic/mesoscopic simulations. This improved understanding shall ensure to eliminate the demerits associated with existing manufacturing technologies. Graphical abstract
It demonstrates the growing demand for the use of MD simulation for the fundamental understandings in the coating field. Source of data: Web of science.
PubDate: 2022-01-12
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- Advances in Tree Seed Algorithm: A Comprehensive Survey
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Abstract: In recent years, significant research has been done to solve optimization and complex problems by metaheuristic algorithms. Metaheuristic algorithms are inspired by the observation and behavior of natural phenomena, such as animals' and plants' lives. In this paper, a comprehensive survey of the tree seed algorithm (TSA) and its applications in a wide range of different fields is performed. TSA is a metaheuristic algorithm inspired by the relationships between trees and seeds in nature and how tree seeds grow and position. TSA has become a convenient algorithm for solving optimization problems in various fields with good exploration and exploitation capabilities. TSA has been used in many disciplines due to its capabilities and strengths. Since 2015, various TSA-based papers have been published in various international journals such as Elsevier, Springer, IEEE, and international conferences. This paper covers all the TSA empirical literature in hybridization, Improved, Variants and Optimization. According to studies, the use of TSA in the mentioned areas has been equal to 21, 23, 4 and 52%, respectively. Therefore, it is believed that this paper will be helpful and practical for students, academic researchers, and specialists and engineers.
PubDate: 2022-01-10
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- Meshfree Methods in Geohazards Prevention: A Survey
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Abstract: Geohazards, including the landslide, debris flow, rockfall, surface collapse, surface subsidence, and surface cracks, can bring great threats to human life and property. Generally, the defromation and failure of rock and soil masses can induce geohazards. To effectively predict and prevent geohazards, it is significant to capture the deformation and failure characteristics of rock and soil masses occurring in geohazards. Numerical modeling methods have been widely used to investigate the deformation and failure of rock and soil masses due to their low cost and convenience. Commonly, meshfree methods can be used to model the large deformation and failure characteristics of rock and soil masses without mesh-dependence. In this paper, we aim to provide a survey of related applications in modeling the deformation and failure of rock and soil masses occurring in common geohazards by using four frequently-used meshfree methods, including Smoothed Particle Hydrodynamics (SPH), Material Point Method (MPM), Discrete Element Method (DEM), and Discontinuous Deformation Analysis (DDA). Moreover, the capabilities, key issues, and outlook of the surveyed meshfree methods in the deformation and failure simulation of rock and soil masses are discussed. This paper provides a reference for geohazards prevention from the perspective of modeling the deformation and failure of rock and soil masses using meshfree methods.
PubDate: 2022-01-10
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- 3D Face Reconstruction in Deep Learning Era: A Survey
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Abstract: 3D face reconstruction is the most captivating topic in biometrics with the advent of deep learning and readily available graphical processing units. This paper explores the various aspects of 3D face reconstruction techniques. Five techniques have been discussed, namely, deep learning, epipolar geometry, one-shot learning, 3D morphable model, and shape from shading methods. This paper provides an in-depth analysis of 3D face reconstruction using deep learning techniques. The performance analysis of different face reconstruction techniques has been discussed in terms of software, hardware, pros and cons. The challenges and future scope of 3d face reconstruction techniques have also been discussed.
PubDate: 2022-01-10
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- Review of Machine Learning Techniques for EEG Based Brain Computer
Interface-
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Abstract: A brain computer interface (BCI) framework uses computer algorithms to detect mental activity patterns and manipulate external devices. Because of its simplicity and non-invasiveness, one of the most commonly used imaging technologies is electroencephalography (EEG). The evaluative method used in assessing the output of an EEG-based BCI system is classifying EEG signals for particular applications. The growth of artificial intelligence technology inspired researchers to use machine learning (ML) techniques and deep learning (DL) approaches to classify EEG-based BCI. Machine learning techniques enable the brain computer interface to learn from the subject's brain with each new session, adapting the generated rules for classifying thoughts and thus improving the system's efficiency. The authors present a concentrated survey on the use of various ML/DL techniques in EEG-based BCI. Three EEG paradigms for classification are used: motor imagery, p300, and steady state evoked potential. In addition, the challenges that recent EEG-based BCI systems face are addressed based on ideal signal processing methods, BCI functioning, performance assessment and commercialization. The authors hope that the information gathered would aid in application of suitable machine learning techniques, as well as provide a foundation for BCI researchers to enhance future BCI system.
PubDate: 2022-01-07
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- A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction
with Real-Time Data-
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Abstract: Wind speed and solar radiation are the fundamental inputs used as a renewable energy source. Both parameters are highly non-linear and environmental dependent. Hence accurate prediction of these parameters is necessary for the application to various applications in different agriculture, industrial, transport, and environmental sectors because they reduce greenhouse gases and are environmentally friendly. The study indicates that good quality prediction, accurate solar forecasting, and wind speed, using the machine learning methodology, can predict correct data such as temperature, relative moisture, solar radiations, rain, and wind speed. The best way to anticipate extremely nonlinear environmental data, such as wind speed and solar radiation is using the artificial neural system. In this study, the benefits and constraints of various neural artificial network algorithms (RP) are analyzed and compared. These algorithms include the RP, Levenberg Marquardt (LM), Polak-Ribiére update gradient (CGP), and OSS (one step secant) gradient to enhance the accuracy of the wind speed and the predicted solar radiation. All findings suggest that the Levenberg–Marquardt and Bayesian algorithms for regulation are more resilient and effective to anticipate highly non-linear characteristics, such as solar radiation and wind speed. With the aid of components like sensors, actuators, protocols, and gateways to increase energy efficiency, dependability, detection of failures, and production optimization, IoT is after collecting exact data the better platform utilized for real-time data analysis.
PubDate: 2022-01-06
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- Rainfall Induced Geotextile Reinforced Model Slope Embankment Subjected to
Surcharge Loading: A Review Study-
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Abstract: Geosynthetic-reinforced soil systems are commonly set to support roads on modern pile supports have been replaced by road approach, bridge abutments and technologies. Over soft foundation soils, geotextile is often utilized to enhance embankments performance. Therefore, the geotextile reinforced embankment is used with the benefits of geotextiles. Due to the major negative effects on social and economic factors, slope failures are often considered a high-risk geo-environmental threat. For the slope failures, rainfall is the most prevailing generating factor. The slope loss susceptibility is increased and the power of shear is reduced, soil matric suction is reduced by the rainfall infiltration. In this review, the researcher structured a brief explanation about the geotextiles with their uses in constructions, toxicity, performance, and their types. Subsequently, the embankment reinforcement slope model, the slope stability techniques and the model subjected with surcharge load are discussed related to different models, additionally, the numerical methods for the stabilization of embankment slope and soil structure which are also distinguished using experimental analysis. Large differential settlements, bearing capacity, and durability are the consideration of deformation and stability and these significant problems are overcome by increasingly using this technique during the building is taking place on very soft soil which will examine using an indoor artificial rainfall erosion model testing. From the art of study, motivation is defined, by the problem definition surveyed by the various studies. To overcome the problems identified, the review’s objective is mentioned to establish a stable embankment with the geotextile reinforced slope model subjected to surcharge load.
PubDate: 2022-01-06
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- Blast Protection Techniques: A Review
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Abstract: In the past few years, many countries have suffered severely from the attacks in form of blasts and other terrorist activities. These attacks have caused a major loss to infrastructure, human lives as well as economic losses. When the explosive occurs intentionally, it is called blast and if the explosive explodes accidentally then it an explosion. In India, the blast resistant design of structures is categorized as explosion above ground, IS 4991 (Indian standard criteria for blast resistant design of structures for explosions above ground, New Delhi, India, 1969) and underground blasts, IS 6922 (Indian Standard criteria for safety and design of structures subject to underground blast, New Delhi, India, 1973). In addition, different international codes and regulations namely NATO (Department of Defense in Structures to resist the effects of accidental explosions, UFC 3-340-02, DC, United States of America, Washington, 2008), FEMA (NATO in Manual of NATO safety principles for the storage of NATO ammunition and explosives, AC/258-D/258, Bonn, Germany, 1993) and DOD (FEMA 428 in Primer to design safe school projects in case of terrorist attacks, Federal Emergency Management Agency, United States of America, 2003) provide guidelines to mitigate blast induced effects on structures. A brief sketch on the development of blast pressure time history generated from detonation of manmade explosions is discussed. Factors affecting the intensity of blast load and its effects on various structural components both experimentally and analytically are also summarized. Theoretical and empirical approaches proposed by various researchers for the prediction of realistic blast load are also studied. The review also includes blast mitigation strategies proposed by various investigations for an improved structural performance against blast. The studies presented in the literature review are broadly categorized under two sections namely studies on air and surface blast loading and studies on underground mine blast loading.
PubDate: 2022-01-04
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- Correction to: Big Data Analytics in Weather Forecasting: A Systematic
Review-
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PubDate: 2022-01-01
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- Correction to: Big Data Processing and Analysis in Internet of Vehicles:
Architecture, Taxonomy, and Open Research Challenges-
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Abstract: A correction to this paper has been published: https://doi.org/10.1007/s11831-021-09607-5
PubDate: 2022-01-01
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- Beyond the Tuned Mass Damper: a Comparative Study of Passive Approaches to
Vibration Absorption Through Antiresonance Assignment-
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Abstract: Vibration absorption is a core research area in the design and control of structures and machines, and exploiting antiresonances is an effective approach for systems under harmonic excitation. This paper proposes a comparative study and a review of the main passive techniques to antiresonance assignment proposed in the recent literature, by discussing them through some numerical examples too. The techniques discussed include the well-known Tuned Mass Damper, which has been widely developed in the literature. However, as the title reveals, great attention is paid to the methods inherited from the field of dynamic structural modification that assign antiresonances without modifying the number of degrees of freedom, by exploiting a proper modification of the system inertial and stiffness parameters. Due to higher mathematical complexity, these approaches have been less investigated in the literature although they are an effective and less invasive approach to antiresonance assignment, especially for machines. To puzzle out the complicated subject matter of antiresonances, their background and their key features are also discussed by reviewing the main theoretical results and their relationship with the assignment techniques. The paper is also enriched with several numerical examples to compare different methods and investigate the features of antiresonances. The concluding remarks of the paper bring together some open issues in this field of research and outlines some possible research directions.
PubDate: 2022-01-01
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- Emerging Trends in Machine Learning to Predict Crop Yield and Study Its
Influential Factors: A Survey-
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Abstract: Agriculture is one of the most crucial field contributing to the development of any nation. It not only affects the economy of nation but also has impact on the world food grain statistics. For agriculturist obtaining sustainable production of crop is always a challenge. Achieving optimum crop yield has always been a challenge for the farmer due to ever changing environmental conditions. The major reasons for unpredictability of crop yield are: land types, availability of resources, and changing nature of weather. Thus, the scientists all over the world are trying to discover techniques which can efficiently and accurately estimate the crop yield in much advance so that the farmers can take suitable actions to meet the future challenges. The main objectives of the study include: (a) Exploration of various machine learning techniques used in crop yield prediction; (b) Assessment of advanced techniques like deep learning in yield estimations; and (c) To explore the efficiency of hybridized models formed by the combination of more than one technique. The reviews done have shown good inclination towards hybrid models and deep learning techniques as means of crop yield prediction. The study also reviewed the works done by researchers in assessing the influence of various factors on crop yields and temperature and precipitation have been found to have maximum influence on the yields of different crops. Apart from climatic factors, agronomic practices adopted by farmers at various stages of growth of a plant also have considerable influence of the final yield of crop.
PubDate: 2022-01-01
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- Machine Learning and Deep Learning Based Computational Techniques in
Automatic Agricultural Diseases Detection: Methodologies, Applications,
and Challenges-
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Abstract: Plant disease detection is a critical issue that needs to be focused on for productive agriculture and economy. Detecting plant disease using traditional methods is a tedious job as it requires a tremendous amount of work, time, and expertise. Automatic plant disease detection is an important research area that has recently gained a lot of attention among the academicians, researchers, and practitioners. Machine Learning and Deep Learning can help identify the plant disease at the initial stage as soon as it appears on plant leaves. In this state-of-an-art review, a thorough investigation has been performed to evaluate the possibility of using Machine Learning models to identify plant diseases. In this study, diseases and infections of four types of crops, i.e., Tomato, Rice, Potato, and Apple, are considered. Initially, numerous possible infections and diseases on these four kinds of crops are studied along with their reason for the occurrence and possible symptoms for their detections. An in-depth study of the different steps involved in plant disease detection and classification using Machine Learning and Deep Learning is provided. Various datasets available online for plant disease detection have also been presented. Along with this, a detailed study on various existing Machine Learning and Deep Learning-based classification models proposed by different researchers across the world for four considered crops in terms of their performance evaluations, the dataset used, and the feature extraction method is discussed. At last, various challenges in the use of machine learning and deep learning for plant disease detection and future research directions are enumerated and presented.
PubDate: 2022-01-01
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- Knowledge Extraction and Discovery Based on BIM: A Critical Review and
Future Directions-
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Abstract: In the past, knowledge in the fields of Architecture, Engineering and Construction (AEC) industries mainly come from experiences and are documented in hard copies or specific electronic databases. In order to make use of this knowledge, a lot of studies have focused on retrieving and storing this knowledge in a systematic and accessible way. The Building Information Modeling (BIM) technology proves to be a valuable media in extracting data because it provides physical and functional digital models for all the facilities within the life-cycle of the project. Therefore, the combination of the knowledge science with BIM shows great potential in constructing the knowledge map in the field of the AEC industry. Based on literature reviews, this article summarizes the latest achievements in the fields of knowledge science and BIM, in the aspects of (1) knowledge description, (2) knowledge discovery, (3) knowledge storage and management, (4) knowledge inference and (5) knowledge application, to show the state-of-arts and suggests the future directions in the application of knowledge science and BIM technology in the fields of AEC industries. The review indicates that BIM is capable of providing information for knowledge extraction and discovery, by adopting semantic network, knowledge graph and some other related methods. It also illustrates that the knowledge is helpful in the design, construction, operation and maintenance periods of the AEC industry, but now it is only at the beginning stage.
PubDate: 2022-01-01
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- A Review on the Size-Dependent Models of Micro-beam and Micro-plate Based
on the Modified Couple Stress Theory-
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Abstract: Micro scale experiments demonstrated that the material deformation behaviors of some metals and polymers deformed elasticity are size dependence, and the modified couple stress theory (MCST), which introduce an additional material length scale parameter, could capture the size dependences. The article reviews the available size-dependent models of micro-beam and micro-plate based on the MCST. For the representative micro-beams and micro-plates, the relevant mechanical models of micro-structures for static bending, vibration, buckling and pull-in instability, have been established based on the MCST, and the analytical and numerical solutions have been obtained. The results show that the size effects on the mechanical properties of the micro-structures are obvious and significant. Further possible researches on the size-dependent behaviors of the micro-structures are proposed.
PubDate: 2022-01-01
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- Review of Vehicle Routing Problems: Models, Classification and Solving
Algorithms-
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Abstract: With the rapid development of logistics industry, vehicle scheduling is the key to the optimization of transportation links. Vehicle transportation route planning is becoming more and more important to reduce logistics costs. In recent decades, the research on VRP and related derivative problems has become more and more extensive. Based on the basic VRP, this paper classifies VRP according to its characteristics and practical application. It focuses on the analysis of VRP with capacity constraint, VRP with time window, VRP with demand splitting and dynamic VRP, and gives the unified description and mathematical model of each type of problem, and then analyzes the solution methods of each type of VRP Finally, combined with other types of VRP, the future research and development trend of VRP are given.
PubDate: 2022-01-01
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- Correction to: Risk Assessment of Infection by Airborne Droplets and
Aerosols at Different Levels of Cardiovascular Activity-
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PubDate: 2021-12-15
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