Archives of Computational Methods in Engineering最新文献

{"title":"Artificial Intelligence Applications in Composites: A Survey","authors":"Ercüment Öztürk, Ayfer Dönmez Çavdar, Tuğrul Çavdar","doi":"10.1007/s11831-024-10169-5","DOIUrl":"https://doi.org/10.1007/s11831-024-10169-5","url":null,"abstract":"<p>It is known that raw material resources have reached the point of depletion. Therefore, the search for alternative sources is becoming more and more common. The only product that can be considered as an alternative to raw material sources is composites. With the increase in its use in the industrial fields, studies in relation to increasing the quality of composites and reducing the production cost have recently gained attention. Experimental studies based on personal experience have now left their place to Information Technologies. Because IT is a good approach that can provide a solution to the improvement of low quality, long timeframes, and high cost in the experimental studies process. In this context, Artificial Intelligence technologies have the potential to provide better solutions and results. In this survey, a literature review on composites using AI technology was conducted. We have mainly focused on the foundations of the AI technology and its advantages in the field of composites. Consequently, it has been seen that the production of composites via IT approaches increases the quality, reduces the production costs, and abridges the experimental production process.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205815","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":"Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and Challenges","authors":"Essam H. Houssein,&nbsp;Mahmoud Khalaf Saeed,&nbsp;Gang Hu,&nbsp;Mustafa M. Al-Sayed","doi":"10.1007/s11831-024-10168-6","DOIUrl":"10.1007/s11831-024-10168-6","url":null,"abstract":"<div><p>The greatest and fastest advances in the computing world today require researchers to develop new problem-solving techniques capable of providing an optimal global solution considering a set of aspects and restrictions. Due to the superiority of the metaheuristic Algorithms (MAs) in solving different classes of problems and providing promising results, MAs need to be studied. Numerous studies of MAs algorithms in different fields exist, but in this study, a comprehensive review of MAs, its nature, types, applications, and open issues are introduced in detail. Specifically, we introduce the metaheuristics' advantages over other techniques. To obtain an entire view about MAs, different classifications based on different aspects (i.e., inspiration source, number of search agents, the updating mechanisms followed by search agents in updating their positions, and the number of primary parameters of the algorithms) are presented in detail, along with the optimization problems including both structure and different types. The application area occupies a lot of research, so in this study, the most widely used applications of MAs are presented. Finally, a great effort of this research is directed to discuss the different open issues and challenges of MAs, which help upcoming researchers to know the future directions of this active field. Overall, this study helps existing researchers understand the basic information of the metaheuristic field in addition to directing newcomers to the active areas and problems that need to be addressed in the future.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4485 - 4519"},"PeriodicalIF":9.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10168-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205816","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":"The Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction","authors":"Athira Rajeev, Rehan Shah, Parin Shah, Manan Shah, Rudraksh Nanavaty","doi":"10.1007/s11831-024-10156-w","DOIUrl":"https://doi.org/10.1007/s11831-024-10156-w","url":null,"abstract":"<p>Water, a priceless gift from nature, acts as Earth's matrix, medium, and life-sustaining substance. While the planet is predominantly covered by water, only 3% is available as freshwater, with 99% of that sourced underground. This groundwater supplies nearly half of the global population. Unfortunately, many areas have experienced recent pollution and overexploitation of this precious resource, adversely affecting the development, sustainability, and economy of people and the planet. Therefore, the evaluation and prediction of Groundwater Quality become indispensable for effective water resource management. Nevertheless, with the continuous advancement of technology, the sheer magnitude of data in Groundwater Science surpasses the capabilities of traditional methods to store, process, and analyse it accurately, leading to erroneous assessments and predictions. Machine Learning is among the promising advanced techniques for processing and extracting new insights from such “Big Data”. This paper explores the scope of Big Data and Machine Learning algorithms for Ground Water Quality Assessment and Prediction (GWQAP). The primary objective of this paper is to identify the impact of Big Data and the effectiveness of Machine learning models in GWQAP. This paper discusses the significance of different Big Data techniques and Machine Learning algorithms for GWQAP. It includes a systematic review of various recently deployed Big Data and Machine Learning applications for Groundwater Quality Management. It also highlights the challenges and future scope of Big Data and Machine Learning in Groundwater Quality Management. Ultimately, this paper is the first step towards enhancing our understanding towards Ground Water Resource Management through Big Data and Machine Learning applications. According to the study, Big Data and Machine Learning can substantially impact water resource management and analysis. Big Data ensures new possibilities for data-driven discovery and decision-making if correctly assessed and managed.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205817","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 Critical Review on Metaheuristic Algorithms based Multi-Criteria Decision-Making Approaches and Applications","authors":"Rishabh, Kedar Nath Das","doi":"10.1007/s11831-024-10165-9","DOIUrl":"https://doi.org/10.1007/s11831-024-10165-9","url":null,"abstract":"<p>This study includes a panoramic view of various existing techniques and approaches of Metaheuristic Optimization Algorithms (MOAs), specifically applied in solving decision-making problems. The synergy of MOAs and Multi-Criteria Decision-Making (MCDM) methods has already established many milestones in the literature. However, the review papers existing in the literature mostly segregates MOAs and MCDM, lacking behind a comprehensive exploration of their integration. This paper bridges the aforesaid gap by providing the recent publications of these two intricate domains arranged and explored with respect to their key contributions. The paper emphasizes on four highly cited Evolutionary Algorithms (EAs) to reduce the information overload. It provides in-depth exploration of practical applications, highlighting instances where synthesis of past achievements and current trends lay the groundwork for future explorations. The study claims that more than 85% of this work has been performed in the last decade only with Genetic Algorithm (GA)-MCDM leading this realm. It offers valuable insights for scholars and practitioners seeking to navigate the intricate developments in this interdisciplinary field.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"49 2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881827","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":"Artificial Intelligence Based Methods for Retrofit Projects: A Review of Applications and Impacts","authors":"Nicoleta Bocaneala, Mohammad Mayouf, Edlira Vakaj, Mark Shelbourn","doi":"10.1007/s11831-024-10159-7","DOIUrl":"https://doi.org/10.1007/s11831-024-10159-7","url":null,"abstract":"<p>The Architecture, Engineering and Construction (AEC) sector faces severe sustainability and efficiency challenges. In recent years, various initiatives have demonstrated how artificial intelligence can effectively address these challenges and improve sustainability and efficiency in the sector. In the context of retrofit projects, there is a continual rising interest in the deployment of Artificial Intelligence (AI) techniques and applications, but the complex nature of such projects requires critical insight into data, processes, and applications so that value can be maximised. This study aims to review AI applications and techniques that have been used in the context of retrofit projects. A review of existing literature on the use of artificial intelligence in retrofit projects within the construction industry was carried out through a thematic analysis. The analysis revealed the potential advantages and difficulties associated with employing AI techniques in retrofit projects, and also identified the commonly utilised techniques, data sources, and processes involved. This study provides a pathway to realise the broad benefits of AI applications for retrofit projects. This study adds to the AI body of knowledge domain by synthesizing the state-of-the-art of AI applications for Retrofit and revealing future research opportunities in this field to enhance the sustainability and efficiency of the AEC sector.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"50 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866513","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":"Correction: Static Modal Analysis: A Review of Static Structural Analysis Methods Through a New Modal Paradigm","authors":"Jonas Feron,&nbsp;Pierre Latteur,&nbsp;João Pacheco de Almeida","doi":"10.1007/s11831-024-10166-8","DOIUrl":"10.1007/s11831-024-10166-8","url":null,"abstract":"","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 6","pages":"3441 - 3441"},"PeriodicalIF":9.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142414945","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":"State of the Art of Coupled Thermo–hydro-Mechanical–Chemical Modelling for Frozen Soils","authors":"Kai-Qi Li, Zhen-Yu Yin","doi":"10.1007/s11831-024-10164-w","DOIUrl":"https://doi.org/10.1007/s11831-024-10164-w","url":null,"abstract":"<p>Numerous studies have investigated the coupled multi-field processes in frozen soils, focusing on the variation in frozen soils and addressing the influences of climate change, hydrological processes, and ecosystems in cold regions. The investigation of coupled multi-physics field processes in frozen soils has emerged as a prominent research area, leading to significant advancements in coupling models and simulation solvers. However, substantial differences remain among various coupled models due to the insufficient observations and in-depth understanding of multi-field coupling processes. Therefore, this study comprehensively reviews the latest research process on multi-field models and numerical simulation methods, including thermo-hydraulic (TH) coupling, thermo-mechanical (TM) coupling, hydro-mechanical (HM) coupling, thermo–hydro-mechanical (THM) coupling, thermo–hydro-chemical (THC) coupling and thermo–hydro-mechanical–chemical (THMC) coupling. Furthermore, the primary simulation methods are summarised, including the continuum mechanics method, discrete or discontinuous mechanics method, and simulators specifically designed for heat and mass transfer modelling. Finally, this study outlines critical findings and proposes future research directions on multi-physical field modelling of frozen soils. This study provides the theoretical basis for in-depth mechanism analyses and practical engineering applications, contributing to the advancement of understanding and management of frozen soils.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"76 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866418","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":"Application of Data-Driven Surrogate Models in Structural Engineering: A Literature Review","authors":"Delbaz Samadian, Imrose B. Muhit, Nashwan Dawood","doi":"10.1007/s11831-024-10152-0","DOIUrl":"https://doi.org/10.1007/s11831-024-10152-0","url":null,"abstract":"<p>In recent times, there has been an increasing prevalence of surrogate models and metamodeling techniques in approximating the responses of complex systems. These surrogate models have proven to be effective in various engineering and scientific disciplines due to their ability to handle demanding computational requirements. The utilisation of surrogates can significantly reduce the time and resources required for calculations. However, practitioners and researchers in structural engineering face challenges in selecting the appropriate surrogate model due to the multitude of approaches available in surrogate modelling development. Despite the numerous advantages of surrogate models, their application in civil engineering has only been explored in the past few years. Consequently, there is a need for recommendations to guide practitioners in the proper utilisation of surrogate models. Additionally, comprehensive review studies are necessary to examine the current state-of-the-art in this area. Currently, there is a lack of research that investigates the implementation of surrogate models specifically in the context of structural engineering. Therefore, this article aims to address this gap by reviewing notable papers that have employed data-driven surrogate modelling in calculations within the field of structural engineering. To achieve this, a thorough analysis is conducted, encompassing a review of 91 journal articles published from 2003 onwards. The primary purpose of this analysis is to describe the various surrogate models employed, and to highlight the domains in which surrogates have been utilised so far. The study demonstrates that the utilisation of data-driven surrogate models in the field of structural engineering provides significant benefits owing to their flexible computational methods that produce accurate outcomes. However, there exist certain significant research gaps in the existing literature that need to be addressed in future studies.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"53 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611021","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":"Correction: Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review","authors":"Alvin Wei Ze Chew, Renfei He, Limao Zhang","doi":"10.1007/s11831-024-10162-y","DOIUrl":"https://doi.org/10.1007/s11831-024-10162-y","url":null,"abstract":"","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"67 9","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653148","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 Materials Improvement Through Artificial Intelligence Approaches: A Systematic Literature Review","authors":"José G. B. A. Lima, Anderson S. L. Gomes, Adiel T. de Almeida-Filho","doi":"10.1007/s11831-024-10163-x","DOIUrl":"https://doi.org/10.1007/s11831-024-10163-x","url":null,"abstract":"<p>Artificial intelligence applications to enhance materials science have reduced the efforts and costs of developing new materials. Although it is still a recent research field, some promising results, and techniques have successfully been deployed for intelligent material discovery. This paper presents a systematic literature review considering applications of Artificial Intelligence (AI) approaches within the Materials Science context, presenting the literature and trends on intelligent materials through Artificial Intelligence. For this literature review, 527 articles and reviews were retrieved from Web of Science and Scopus databases from 1995 to 2022. The results showed that the number of AI applications in Materials Science has grown as well as the number of publications citing AI applications. Among the results, the most popular and relevant algorithms used in materials science are identified with a wide diversity of application possibilities with future directions.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614787","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}