Archives of Computational Methods in Engineering最新文献

{"title":"State-of-the-Art Constitutive Modelling of Frozen Soils","authors":"Kai-Qi Li,&nbsp;Zhen-Yu Yin,&nbsp;Ji-Lin Qi,&nbsp;Yong Liu","doi":"10.1007/s11831-024-10102-w","DOIUrl":"10.1007/s11831-024-10102-w","url":null,"abstract":"<div><p>In recent decades, the constitutive modelling for frozen soils has attracted remarkable attention from scholars and engineers due to the continuously growing constructions in cold regions. Frozen soils exhibit substantial differences in mechanical behaviours compared to unfrozen soils, due to the presence of ice and the complexity of phase changes. Accordingly, it is more difficult to establish constitutive models to reasonably capture the mechanical behaviours of frozen soils than unfrozen soils. This study attempts to present a comprehensive review of the state of the art of constitutive models for frozen soils, which is a focal topic in geotechnical engineering. Various constitutive models of frozen soils under static and dynamic loads are summarised based on their underlying theories. The advantages and limitations of the models are thoroughly discussed. On this basis, the challenges and potential future research possibilities in frozen soil modelling are outlined, including the development of open databases and unified constitutive models with the aid of advanced techniques. It is hoped that the review could facilitate research on describing the mechanical behaviours of frozen soils, and promote a deeper understanding of the thermo-hydro-mechanical (THM) coupled process occurring in cold regions.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3801 - 3842"},"PeriodicalIF":9.7,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10102-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659519","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":"Revolutionizing Structural Engineering: Applications of Machine Learning for Enhanced Performance and Safety","authors":"Anup Chitkeshwar","doi":"10.1007/s11831-024-10117-3","DOIUrl":"10.1007/s11831-024-10117-3","url":null,"abstract":"<div><p>This study delves into the transformative influence of Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) within the realm of Structural Engineering, emphasizing their profound implications for Information, Process, and Design Engineering. Through a meticulous analysis of existing literature, the study highlights the vast potential of ML, DL, and AI across diverse construction domains, particularly within structural engineering, including healthcare, performance evaluation, monitoring, and optimization. Notably, the integration of ML with the Internet of Things (IoT) for real-time structural health monitoring emerges as a pivotal advancement, promising enhanced durability and performance models. Moreover, the application of ML-supported multi-objective optimization in design processes showcases promising strides, effectively balancing factors such as cost and durability to bolster structural integrity. By leveraging these technologies to process data, identify patterns, and predict behaviour, structural health is significantly bolstered. Moving forward, the study advocates for continued exploration of ML and IoT integration for real-time monitoring, refinement of learning algorithms for process control, and the utilization of ML-assisted multi-objective optimization in design. Crucially, it underscores the imperative of addressing challenges such as data availability and algorithm robustness to fully harness the potential of ML, DL, and AI in revolutionizing structural engineering design. This research thus serves as a clarion call for further investigation and training to facilitate the widespread adoption of these transformative technologies in structural engineering practices.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4617 - 4632"},"PeriodicalIF":9.7,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659931","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":"The Applications of 3D Input Data and Scalability Element by Transformer Based Methods: A Review","authors":"Abubakar Sulaiman Gezawa,&nbsp;Chibiao Liu,&nbsp;Naveed Ur Rehman Junejo,&nbsp;Haruna Chiroma","doi":"10.1007/s11831-024-10108-4","DOIUrl":"10.1007/s11831-024-10108-4","url":null,"abstract":"<div><p>Outstanding effectiveness of transformers in visual tasks has resulted in its fast growth and adoption in three dimensions (3D) vision tasks. Vision transformers have shown numerous advantages over earlier convolutional neural network (CNN) architectures including broad modelling abilities, more substantial modelling capabilities, convolution complementarity, scalability to model data size, and better connection for enhancing the performance records of many visual tasks. We present thorough review that classifies and summarizes the popular transformer-based approaches based on key features for transformer integration such as the input data, scalability element that enables transformer processing, architectural design, and context level through which the transformer functions as well as a highlight of the primary contributions of each transformer approach. Furthermore, we compare the results of these techniques with commonly employed non-transformer techniques in 3D object classification, segmentation, and object detection using standard 3D datasets including ModelNet, SUN RGB-D, ScanNet, nuScenes, Waymo, ShapeNet, S3DIS, and KITTI. This study also includes the discussion of numerous potential future options and limitation for 3D vision transformers.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4129 - 4147"},"PeriodicalIF":9.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670761","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":"Active Learning and Bayesian Optimization: A Unified Perspective to Learn with a Goal","authors":"Francesco Di Fiore,&nbsp;Michela Nardelli,&nbsp;Laura Mainini","doi":"10.1007/s11831-024-10064-z","DOIUrl":"10.1007/s11831-024-10064-z","url":null,"abstract":"<div><p>Science and Engineering applications are typically associated with expensive optimization problem to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through efficient adaptive sampling schemes to assist and accelerate this search task toward a given optimization goal. Both those methodologies are driven by specific infill/learning criteria which quantify the utility with respect to the set goal of evaluating the objective function for unknown combinations of optimization variables. While the two fields have seen an exponential growth in popularity in the past decades, their dualism and synergy have received relatively little attention to date. This paper discusses and formalizes the synergy between Bayesian optimization and active learning as symbiotic adaptive sampling methodologies driven by common principles. In particular, we demonstrate this unified perspective through the formalization of the analogy between the Bayesian infill criteria and active learning criteria as driving principles of both the goal-driven procedures. To support our original perspective, we propose a general classification of adaptive sampling techniques to highlight similarities and differences between the vast families of adaptive sampling, active learning, and Bayesian optimization. Accordingly, the synergy is demonstrated mapping the Bayesian infill criteria with the active learning criteria, and is formalized for searches informed by both a single information source and multiple levels of fidelity. In addition, we provide guidelines to apply those learning criteria investigating the performance of different Bayesian schemes for a variety of benchmark problems to highlight benefits and limitations over mathematical properties that characterize real-world applications.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 5","pages":"2985 - 3013"},"PeriodicalIF":9.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10064-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805055","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":"Revolutionizing Dermatology: A Comprehensive Survey of AI-Enhanced Early Skin Cancer Diagnosis","authors":"Zinal M. Gohil,&nbsp;Madhavi B. Desai","doi":"10.1007/s11831-024-10121-7","DOIUrl":"10.1007/s11831-024-10121-7","url":null,"abstract":"<div><p>Skin cancer is a significant global health concern, with its early detection and diagnosis playing a pivotal role in improving patient health outcomes. In recent years, artificial intelligence (AI) has emerged as a transformative force in the field of dermatology, revolutionizing the way skin cancer is detected and diagnosed. This comprehensive survey paper delves into the realm of AI-enhanced early skin cancer diagnosis, offering a thorough examination of the state-of-the-art techniques, methodologies, and advancements in this critical domain. Our survey begins by providing a comprehensive overview of the different types of skin cancer, emphasizing the importance of early detection in preventing disease progression. It then explores the pivotal role that AI and machine learning algorithms play in automating the detection and classification of skin lesions, making dermatology more accessible and accurate. A critical analysis of various AI-driven approaches, including image-based classification, feature extraction, and deep learning models, is presented to elucidate their strengths and limitations. Furthermore, this survey examines the integration of AI into clinical practice, discussing real-world applications, challenges, and ethical considerations. It explores the potential of AI to assist dermatologists in making faster and more accurate diagnoses, ultimately enhancing patient care. The paper also addresses the need for large, diverse datasets and standardization in the development and validation of AI models for skin cancer diagnosis. In conclusion, “Revolutionizing Dermatology” presents a comprehensive synthesis of the current landscape of AI-enhanced early skin cancer diagnosis, offering insights into its transformative potential, challenges, and future directions. By bridging the gap between dermatology and cutting-edge AI technologies, this survey aims to facilitate informed decision-making among researchers, clinicians, and stakeholders in the pursuit of more effective skin cancer detection and treatment strategies.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4521 - 4531"},"PeriodicalIF":9.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140671440","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 Review of Strategies to Detect Fatigue and Sleep Problems in Aviation: Insights from Artificial Intelligence","authors":"Yan Li,&nbsp;Jibo He","doi":"10.1007/s11831-024-10123-5","DOIUrl":"10.1007/s11831-024-10123-5","url":null,"abstract":"<div><p>Over the past few years, the increasing occurrence of catastrophic accidents in aviation owing to human factors has raised several devastating threats to mankind. Recent progress in fatigue recognition among pilots made by Artificial intelligence (AI) has intensely begun to enhance the safety of the aviation sector by identifying and warning the potential catastrophic incidents caused by the impaired cognitive condition of aviation professionals. In this review, we have thoroughly investigated the implementation of AI-based approaches in the domain of aviation for fatigue detection. To the extent of our knowledge, it is clear that this review article is a new paper extremely devoted for investigating the advancements and challenges rendered by the AI-based approaches for addressing sleep and fatigue issues in aviation. Initially, we provided the basic definition of fatigue, various aspects provoking these problems among aviation professionals, and its effects in compromising aviation safety. Secondly, we illustrated a review of AI-based approaches developed for assessing fatigue and sleep problems in the context of aviation. Thirdly, the comparisons of various approaches are provided to summarize the efficiency of the existing works. Finally, we talked about the challenges encountered by the state-of-the-art approaches for identifying future research direction, and our suggested solutions are well presented for improving the efficiency of the fatigue detection approaches. This comprehensive research clearly depicts that the advancement of fatigue recognition approaches based on AI has a wider scope for mitigating pilot’s fatigue by identifying the mental state of the pilot earlier and providing adequate interventions.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4655 - 4672"},"PeriodicalIF":9.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627256","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 Artificial Intelligence in Aerospace Engineering and Its Future Directions: A Systematic Quantitative Literature Review","authors":"Kamal Hassan,&nbsp;Amit Kumar Thakur,&nbsp;Gurraj Singh,&nbsp;Jaspreet Singh,&nbsp;Lovi Raj Gupta,&nbsp;Rajesh Singh","doi":"10.1007/s11831-024-10105-7","DOIUrl":"10.1007/s11831-024-10105-7","url":null,"abstract":"<div><p>This research aims to comprehensively analyze the most essential uses of artificial intelligence in Aerospace Engineering. We obtained papers initially published in academic journals using a Systematic Quantitative Literature Review (SQLR) methodology. We then used bibliometric methods to examine these articles, including keyword co-occurrences and bibliographic coupling. The findings enable us to provide an up-to-date sketch of the available literature, which is then incorporated into an interpretive framework that enables AI's significant antecedents and effects to be disentangled within the context of innovation. We highlight technological, security, and economic factors as antecedents prompting companies to adopt AI to innovate. As essential outcomes of the deployment of AI, in addition to identifying the disciplinary focuses, we also identify business organizations' product innovation, process innovation, aerospace business model innovation, and national security issues. We provide research recommendations for additional examination in connection to various forms of innovation, drawing on the most critical findings from this study.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4031 - 4086"},"PeriodicalIF":9.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611259","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 Comparative Study and Systematic Analysis of XAI Models and their Applications in Healthcare","authors":"Jyoti Gupta,&nbsp;K. R. Seeja","doi":"10.1007/s11831-024-10103-9","DOIUrl":"10.1007/s11831-024-10103-9","url":null,"abstract":"<div><p>Artificial intelligence technologies such as machine learning and deep learning employ techniques to anticipate results more effectively without human involvement. Since AI models are viewed as opaque models, their application in healthcare is still restricted. Explainable artificial intelligence (XAI) has been designed to increase the use of artificial intelligence (AI) algorithms in the healthcare sector by increasing trust in the model's predictions and explaining how they are developed. The aim of this article is to critically review, compare, and summarize existing research and to find new research possibilities of XAI for applications in healthcare. This study is conducted by finding articles related to XAI in biological and healthcare domains from the PubMed, Science Direct, and Web of Science databases using the PRISMA method. A comparative study of the state-of-the-art XAI techniques to evaluate its applications in healthcare has also been done using an experimental demonstration on the Diabetes dataset. XAI techniques, namely LIME, SHAP, PDP, and decision tree, were used to explain how various input attributes contributed to the outcome of the model. This study found that the explanations provided by these models are not easily understandable for different users of the model, like doctors and patients, and need expertise. This study found that the potential of XAI in the medical domain is high as it increases trust in the AI model. This survey will motivate the researchers to build more XAI techniques that provide user-friendly explanations, especially for the less explored areas of medical data, such as biomedical signals and biomedical text.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"3977 - 4002"},"PeriodicalIF":9.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611178","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":"Advances in Discrete Element Modeling of Asphalt Mixture: A Literature Review","authors":"Xinman Ai,&nbsp;Junyan Yi,&nbsp;Zhongshi Pei,&nbsp;Wenyi Zhou,&nbsp;Decheng Feng","doi":"10.1007/s11831-024-10104-8","DOIUrl":"10.1007/s11831-024-10104-8","url":null,"abstract":"<div><p>The complicated composition structure of the asphalt mixture makes it difficult to determine its multi-scale performance. It is impossible to understand the internal interaction mechanism of asphalt mixture only through laboratory tests, especially under complex conditions at the microscale, which can be effectively solved by the discrete element method (DEM). This paper summarized the progress and advances in DEM modeling of asphalt mixture mainly consisting of the principle of DEM, DEM simulation for asphalt mixture, asphalt mixture compaction and mechanical behavior based on the DEM. The basis of DEM modeling is the well-known Newton's second law, through which the discrete elements are determined primarily according to the motion equation and force–displacement law. The DEM modeling of asphalt mixture often includes the simulation of coarse aggregates, asphalt mortar and air voids regardless of two-dimensional (2D) and three-dimensional (3D) DEM. The morphological characteristics of coarse aggregates and spatial distribution of air voids are essential to the simulation results of asphalt mixtures. In addition, the commonly used DEM contact models in asphalt mixture, including the <i>linear model</i>, <i>Burgers model</i> and <i>linear parallel bond model</i>, are introduced and analyzed. The main micro-parameters of various contact models are usually obtained through laboratory test results by trial and error. And the selection of contact modeling and determination of macro-parameters are discussed. Then, asphalt mixture compaction based on DEM, mainly containing Superpave gyratory compaction (SGC) and Marshall impact compaction (MIC), is estimated in this paper. It is concluded that displacement, rotation and contact stress of aggregate particles can be accurately captured similar to SmartRock. Moreover, varying mechanical behavior and the significant influencing factors based on DEM are evaluated comprehensively. Finally, further prospects in DEM modeling of asphalt mixture are proposed to promote the development and application of numerical simulation in pavement engineering.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4003 - 4029"},"PeriodicalIF":9.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560681","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 Computational Framework for Precise Aerial Agricultural Spray Delivery Processes","authors":"J. O. Betancourt,&nbsp;I. Li,&nbsp;E. Mengi,&nbsp;L. Corrales,&nbsp;T. I. Zohdi","doi":"10.1007/s11831-024-10106-6","DOIUrl":"10.1007/s11831-024-10106-6","url":null,"abstract":"<div><p>As the world’s population is expected to increase, so is the global demand for food. Sustainable intensification via precision agriculture of existing farms can increase crop production. Agricultural spray drones have recently taken a physical role within precision agriculture, such as aerial application of fluids, solids, and biological control agents but have difficulties spraying in uncontrolled environments caused by wind shifting spray material away from intended target areas. This work proposes an efficient physics-based framework to provide drone operators with trajectory and spray nozzle configuration for optimal target crop-dusting to mitigate spray drifts while providing quantitative approximations of spray particle trajectory and ground concentration. The framework is coupled with a machine-learning algorithm (MLA) to aid users in their search for optimal results and includes two decoupled models that simulate wind and spray particle trajectories. In the model problem, a genetic algorithm (GA) is used to optimize the system where the optimal trajectory and spray nozzle configuration resulted in 64% of crop targets hit while only losing minimal spray material from spray drifts.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4149 - 4162"},"PeriodicalIF":9.7,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560510","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}