Archives of Computational Methods in Engineering最新文献

{"title":"Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review","authors":"Alvin Wei Ze Chew,&nbsp;Renfei He,&nbsp;Limao Zhang","doi":"10.1007/s11831-024-10145-z","DOIUrl":"10.1007/s11831-024-10145-z","url":null,"abstract":"<div><p>Building resilient and sustainable urban infrastructures is imperative to prepare future generations against new pandemics and climate change uncertainties. In general, modelling of urban infrastructures requires modelers to carefully consider their initial design phase, subsequent life-span management, and long-term resilience development. With the continual development of machine learning (ML) and artificial intelligence (AI) approaches, significant opportunities are available to civil engineers to improve the existing computing systems of urban infrastructures to contribute to their overall design, management, and resilience-development. Often, an important requirement for the successful adoption of ML/AI techniques is to ensure sufficient field data for training effective predictive models for the above objectives. However, this requirement may be difficult to achieve for all infrastructure engineering applications in the practical field context due to sensor constraints (e.g., limited sensor deployment), coupled with other computational challenges. To address the multiple challenges, this review paper evaluates the important and relevant physics informed machine learning (PIML) publications from 1992 to 2022 for various critical infrastructure engineering applications, namely: (1) PIML for Infrastructures Design and Analysis, (2) PIML for Infrastructure Built-Environment Modelling, (3) PIML for Infrastructures Health Monitoring, and (4) PIML for Infrastructures Resilience Management/Development. In each application, we discuss on the key modelling objectives involved for the specific infrastructure systems, and their associated advantages and/or likely limitations obtained from the PIML implementation. Finally, we then summarize the key research trends and their associated challenges to continue leveraging on PIML techniques to better benefit the overall design, management, and resilience-development of urban infrastructures.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"399 - 439"},"PeriodicalIF":9.7,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Linear and Nonlinear Free Vibration Analyses of Micro/Nanoscale Composite Plate-Type Structures With/Without Considering Size Dependency Effect: A Comprehensive Review","authors":"Zummurd Al Mahmoud,&nbsp;Babak Safaei,&nbsp;Saeid Sahmani,&nbsp;Mohammed Asmael,&nbsp;AliReza Setoodeh","doi":"10.1007/s11831-024-10132-4","DOIUrl":"10.1007/s11831-024-10132-4","url":null,"abstract":"<div><p> Recently, the mechanical performance of various mechanical, electrical, and civil structures, including static and dynamic analysis, has been widely studied. Due to the neuroma's advanced technology in various engineering fields and applications, developing small-size structures has become highly demanded for several structural geometries. One of the most important is the nano/micro-plate structure. However, the essential nature of highly lightweight material with extraordinary mechanical, electrical, physical, and material characterizations makes researchers more interested in developing composite/laminated-composite-plate structures. To comprehend the dynamical behavior, precisely the linear/nonlinear-free vibrational responses, and to represent the enhancement of several parameters such as nonlocal, geometry, boundary condition parameters, etc., on the free vibrational performance at nano/micro scale size, it is revealed that to employ all various parameters into various mathematical equations and to solve the defined governing equations by analytical, numerical, high order, and mixed solutions. Thus, the presented literature review is considered the first work focused on investigating the linear/nonlinear free vibrational behavior of plates on a small scale and the impact of various parameters on both dimensional/dimensionless natural/fundamental frequency and Eigen-value. The literature is classified based on solution type and with/without considering the size dependency effect. As a key finding, most research in the literature implemented analytical or numerical solutions. The drawback of classical plate theory can be overcome by utilizing and developing the elasticity theories. The nonlocality, weight fraction of porosity, or the reinforcements, and its distribution type of elastic foundation significantly influence the frequencies.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"113 - 232"},"PeriodicalIF":9.7,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10132-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives of Peridynamic Theory in Wind Turbines Computational Modeling","authors":"Mesfin Belayneh Ageze,&nbsp;Migbar Assefa Zeleke,&nbsp;Temesgen Abriham Miliket,&nbsp;Malebogo Ngoepe","doi":"10.1007/s11831-024-10129-z","DOIUrl":"10.1007/s11831-024-10129-z","url":null,"abstract":"<div><p>The applications of wind turbines are consistently increasing across the globe. Competent and sustainable wind energy harnessing inherently requires the implementation of optimal design and advanced materials. To minimize all the risks associated with severe environmental loadings, reduced cost, and improved performance, advanced computational methodologies should be utilized as a part of the analysis process. The recently introduced non-local theory called Peridynamic (PD) theory crafted by Silling has interesting advantages over the conventional computational method such as the finite element method (FEM) and finite volume method (FVM). PD theory is a computational and theoretical framework where partial differential equations (PDEs) of classic continuum theory are replaced by integral equations. Unlike the local continuum theory, the integro-differential equations of PD theory are without derivatives of displacement function, hence suitable to capture discontinuities. Therefore, the present paper reviews the structural and aerodynamics of wind turbines, the existing computational challenges that are related to the modeling and analysis of wind turbines, and finally examines the potential use of Peridynamic theory concerning wind turbines.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"1 - 33"},"PeriodicalIF":9.7,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer-Aided Classification of Melanoma: A Comprehensive Survey","authors":"Uma Sharma,&nbsp;Preeti Aggarwal,&nbsp;Ajay Mittal","doi":"10.1007/s11831-024-10138-y","DOIUrl":"10.1007/s11831-024-10138-y","url":null,"abstract":"<div><p>The prevalence of skin cancer has been increasing for the last few decades. Abnormal growth of cells forms skin lesions, which if not treated at the earliest, may turn into cancer. With the advancement in technology, computer-aided or remote diagnosis is possible, but a lot of efforts are required. An exclusive survey of the work done is required to consolidate the information regarding the various methods adopted to date and to ascertain future opportunities. In this paper, we have reviewed major works that have been proposed to automate the diagnosis of melanoma using dermoscopic images.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4893 - 4927"},"PeriodicalIF":9.7,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Computational Methods for Damage Detection of Laminated Composite Structures for Mobility Applications: A Comprehensive Review","authors":"Muhammad Muzammil Azad,&nbsp;Yubin Cheon,&nbsp;Izaz Raouf,&nbsp;Salman Khalid,&nbsp;Heung Soo Kim","doi":"10.1007/s11831-024-10146-y","DOIUrl":"10.1007/s11831-024-10146-y","url":null,"abstract":"<div><p>The mobility applications of laminated composites are constantly expanding due to their improved mechanical properties and superior strength-to-weight ratio. Such advancements directly contribute to a significant reduction in energy consumption in mobile applications. However, the orthotropic nature of these materials results in complex failure modes that require advanced damage detection techniques to prevent catastrophic failures. Therefore, various non-destructive evaluation techniques for structural health monitoring (SHM) of laminated composites are constantly being developed. Moreover, due to the latest advancements in intelligent computational methods, such as machine learning and deep learning, more reliable inspections can be performed. This review discusses current advances in SHM of composite laminates for safety–critical mobility applications such as aerospace, automobile, and marine. A comprehensive overview of the steps involved in SHM of mobility composite structures, such as sensing systems and intelligent computational methods, is presented. Additionally, the review discusses the procedure for developing these intelligent computational methods. The article also describes various public-domain datasets that readers can utilize to create novel, intelligent computational methods. Finally, potential research directions are highlighted that will enable researchers and practitioners to develop more accurate and efficient damage monitoring systems for mobility composite structures.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"441 - 469"},"PeriodicalIF":9.7,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Study on Deep Learning Models for the Detection of Ovarian Cancer and Glomerular Kidney Disease using Histopathological Images","authors":"S J K Jagadeesh Kumar,&nbsp;G. Prabu Kanna,&nbsp;D. Prem Raja,&nbsp;Yogesh Kumar","doi":"10.1007/s11831-024-10130-6","DOIUrl":"10.1007/s11831-024-10130-6","url":null,"abstract":"<div><p>Ovarian cancer is a significant health concern because of its high mortality rates and potential to cause glomerular injury, which can obstruct the urinary tract. It is very crucial to diagnose and treat these diseases accurately as well as timely. In the era of artificial intelligence, deep learning models have emerged as powerful tools in analysing medical images as they showcase exceptional capabilities to detect diseases. In this study, an innovative approach has been proposed that uses deep transfer learning classifiers for the detection as well as classification of ovarian cancer, sclerosed glomeruli, and normal glomeruli in histopathological images. To gather relevant data, two different repositories have been explored which contain images of ovarian cancer, sclerosed glomeruli, and normal glomeruli. These images are thoroughly pre-processed by converting them into grayscale. Afterwards, advanced segmentation techniques are applied such as image equalization, thresholding, image inversion, and morphological opening which effectively highlight the affected areas using contour features, and various measurements such as area, mean intensity, height, width, and epsilon are calculated. Our study employed a range of deep learning techniques such as AlexNet2, InceptionV3, EfficientNetB0, EfficientNetB5, DenseNet121, Xception, MobileNetV2, and InceptionResNetV2 along with the two optimization techniques: Adam and RMSprop optimizer. Remarkably, during experimentation, AlexNet2 demonstrated exceptional accuracy by achieving 99.74%, with a low loss of 0.0018 and a root mean square error of 0.042426 when incorporating the Adam optimizer. Similarly, using the RMSprop optimizer, Xception delivered outstanding results with an accuracy of 99.74%, a minimal loss of 0.0027, and a root mean square error of 0.051962. This pioneering research significantly contributes to the field of medical diagnostics by harnessing deep learning technology to enhance the precision and efficiency of ovarian cancer and sclerosed glomeruli detection.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"35 - 61"},"PeriodicalIF":9.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing","authors":"Mehdi Hosseinzadeh,&nbsp;Amir Masoud Rahmani,&nbsp;Fatimatelbatoul Mahmoud Husari,&nbsp;Omar Mutab Alsalami,&nbsp;Mehrez Marzougui,&nbsp;Gia Nhu Nguyen,&nbsp;Sang-Woong Lee","doi":"10.1007/s11831-024-10135-1","DOIUrl":"10.1007/s11831-024-10135-1","url":null,"abstract":"<div><p>In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"269 - 310"},"PeriodicalIF":9.7,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particle Swarm Optimization in 3D Medical Image Registration: A Systematic Review","authors":"Lucia Ballerini","doi":"10.1007/s11831-024-10139-x","DOIUrl":"10.1007/s11831-024-10139-x","url":null,"abstract":"<div><p>Medical image registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. These problems usually require the optimization of a similarity metric. Swarm Intelligence techniques are very effective and efficient optimization methods. This systematic review focuses on 3D medical image registration using Particle Swarm Optimization.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"311 - 318"},"PeriodicalIF":9.7,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141149718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential Evolution: A Survey on Their Operators and Variants","authors":"Elivier Reyes-Davila,&nbsp;Eduardo H. Haro,&nbsp;Angel Casas-Ordaz,&nbsp;Diego Oliva,&nbsp;Omar Avalos","doi":"10.1007/s11831-024-10136-0","DOIUrl":"10.1007/s11831-024-10136-0","url":null,"abstract":"<div><p>The Differential Evolution (DE) algorithm is one of the most popular and studied approaches in Evolutionary Computation (EC). Its simple but efficient design, such as its competitive performance for many real-world optimization problems, has positioned it as the standard comparison scheme for any proposal in the field. Precisely, its simplicity has allowed the publication of a great number of variants and improvements since its inception in 1997. Moreover, several DE variants are recognized as well-founded and highly competitive algorithms in the literature. In addition, the multiple DE applications and their proposed modifications in the state-of-the-art have propitiated the drafting of many review and survey works. However, none of the DE compilation work has studied the different variants of DE operators exclusively, which would benefit future DE enhancements and other topics. Therefore, in this work, a survey analysis of the variants of DE operators is presented. This study focuses on the proposed DE operators and their impact on the EC literature over the years. The analysis allows understanding of each year’s trends, the improvements that marked a milestone in the DE research, and the feasible future directions of the algorithm. Finally, the results show a downward trend for mutation or crossover variants while readers are increasingly interested in initialization and selection enhancements.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"83 - 112"},"PeriodicalIF":9.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Swarm Intelligent Metaheuristic Optimization Algorithms-Based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and Future Research Directions","authors":"K. Veeranjaneyulu,&nbsp;M. Lakshmi,&nbsp;Sengathir Janakiraman","doi":"10.1007/s11831-024-10142-2","DOIUrl":"10.1007/s11831-024-10142-2","url":null,"abstract":"<div><p>Breast Cancer Disease is identified as one of the prime causes of death in women around the globe standing next to lung cancer. Breast cancer represents the development of malignant neoplasm from the breast cells. This breast cancer can be treated when it is identified at an early stage. Several researchers have contributed different machine learning approaches for maximizing the accuracy during the process of predicting breast cancer. Optimization of selected features is another important step essential for attaining maximized accuracy during the process of detection during the use of Artificial Neural Network. The utilization of optimization algorithm also helps in fine-tuning the hyperparameters of ANN such that the process of classification can be achieved with better precision and less computational time. In this paper, a Review on Swarm Intelligent metaheuristic optimization algorithms-based Artificial Neural Network-based Breast Cancer Diagnosis Schemes is presented for comparing different approaches depending on their efficacy in achieving the classification process. It presents the potentiality of wrapper and filter methods generally used for classifying cancer cells from normal cells. This review specifically concentrates on highlighting the significance of the swarm intelligent algorithms-based optimized ANN models which are contributed with its limitations. This review also demonstrates the future scope of research which could be concentrated from the identified extract of the literature. This review also highlighted the different kinds of evaluation metrics considered for assessing the potentiality of the existing ANN-based Breast Cancer Diagnosis Schemes with its need in utilization during evaluation.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"381 - 398"},"PeriodicalIF":9.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}