Archives of Computational Methods in Engineering最新文献

{"title":"Evolution of Piled Raft Foundation at Static Loading Condition and Application of Numerical Modelling: A State-of-the-Art Review","authors":"Plaban Deb,&nbsp;Sujit Kumar Pal","doi":"10.1007/s11831-024-10172-w","DOIUrl":"10.1007/s11831-024-10172-w","url":null,"abstract":"<div><p>Piled raft foundation is a sustainable foundation approach in the current era due to the development of immense infrastructure. Initially, this foundation was primarily used for the construction of high-rise buildings in weaker soil bases, however, the application of piled raft systems is now extending to offshore and marine structures. This paper mainly concentrates on the review of available literature in the domain of the piled raft foundation and presents a critical review of the evolution of the piled raft from the past to till date. This state-of-the-art review starts from the early-age research and analytical works on the piled raft foundation. Various approaches suggested by several researchers are described here. The review also contains the experimental research conducted on piled rafts including the numerical analysis carried out on the existing case studies. The applications of different numerical software for the modelling of piled rafts are also described elaborately and the research related to the parametric analysis is accumulated together. The load sharing nature, interaction behaviour, and the influence of the loading system are reviewed separately depending upon the contribution of the research papers to the particular domain. There exist several design processes and selection criteria for the piled raft foundation and this is mainly due to the inadequate perception regarding the nature of the piled raft foundation during its overall applications. This literature review would be helpful for other researchers to acquire a clear idea of the behaviour of piled rafts and also would be useful to identify the future prospects of the piled raft foundation.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1097 - 1133"},"PeriodicalIF":9.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205804","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 Systematic Survey on Segmentation Algorithms for Musculoskeletal Tissues in Ultrasound Imaging","authors":"Ananth Hari Ramakrishnan, Muthaiah Rajappa, Kannan Kirthivasan, Nachiappan Chockalingam, Panagiotis E. Chatzistergos, Rengarajan Amirtharajan","doi":"10.1007/s11831-024-10171-x","DOIUrl":"https://doi.org/10.1007/s11831-024-10171-x","url":null,"abstract":"<p>Ultrasound imaging is widely used for the clinical assessment and study of musculoskeletal tissues because of its capacity for real-time imaging, low cost, high availability and portability. Objectively identifying and segmenting these tissues in ultrasound images can enhance disease diagnosis and biomechanical research. Manual segmentation is tedious, time-consuming and examiner-dependent. At the same time, ultrasound images suffer from poor image quality and low contrast between different regions in the image, making visual interpretation difficult. Hence, there is a need for reliable algorithms for computerised segmentation. This paper reviews the techniques developed for automated and semi-automated segmentation of vital musculoskeletal tissues (i.e. tendon, ligament, bone, muscle, plantar fascia and cartilage) from ultrasound images. This paper comprehensively explains each methodology and discusses distinguishing features, advantages and limitations to help the reader decide the most appropriate method on an application-specific basis.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"174 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205814","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":"Machine Learning Application in Prediction of Scour Around Bridge Piers: A Comprehensive Review","authors":"Farooque Rahman, Rutuja Chavan","doi":"10.1007/s11831-024-10167-7","DOIUrl":"https://doi.org/10.1007/s11831-024-10167-7","url":null,"abstract":"<p>Scour is one of the most difficult challenges faced by hydraulic engineers, which refers to the erosion of sediments that surrounds hydraulic structures. In the past, scour prediction has generally relied on physical models as well as empirical formulae. However, these methods may not satisfactorily account for the complex nature of scour processes. Hence, this paper aims to provide a concise overview of the latest advancements in the field of scour prediction, particularly focusing on the use of machine learning (ML) techniques. The review begins by examining the basic ideas and methodologies of various machine learning algorithms which are commonly employed, it then looks into the key factors that affect scour processes, including flow velocity, sediment characteristics, and bed morphology. The paper provides an in-depth assessment of the advantages and drawbacks of current machine learning models used for estimating scour, taking into account various issues such as the availability of data, models understandability, and their capacity to adapt in changing environmental conditions. This study will be a helpful resource for researchers, practitioners, and decision-makers in the field of hydraulic engineering. It provides insights into the evolving field of ML applications for predicting scour and sets the stage for the advancement of more precise and versatile scour prediction models.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205813","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 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,&nbsp;Rehan Shah,&nbsp;Parin Shah,&nbsp;Manan Shah,&nbsp;Rudraksh Nanavaty","doi":"10.1007/s11831-024-10156-w","DOIUrl":"10.1007/s11831-024-10156-w","url":null,"abstract":"<div><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></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"927 - 941"},"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 Rishabh,&nbsp;Kedar Nath Das","doi":"10.1007/s11831-024-10165-9","DOIUrl":"10.1007/s11831-024-10165-9","url":null,"abstract":"<div><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></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"963 - 993"},"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,&nbsp;Mohammad Mayouf,&nbsp;Edlira Vakaj,&nbsp;Mark Shelbourn","doi":"10.1007/s11831-024-10159-7","DOIUrl":"10.1007/s11831-024-10159-7","url":null,"abstract":"<div><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></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"899 - 926"},"PeriodicalIF":9.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10159-7.pdf","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":"OA","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,&nbsp;Zhen-Yu Yin","doi":"10.1007/s11831-024-10164-w","DOIUrl":"10.1007/s11831-024-10164-w","url":null,"abstract":"<div><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></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 2","pages":"1039 - 1096"},"PeriodicalIF":9.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10164-w.pdf","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}