Archives of Computational Methods in Engineering最新文献

{"title":"A Comprehensive Review of Machine Learning Algorithms and Its Application in Groundwater Quality Prediction","authors":"Harsh Pandya, Khushi Jaiswal, Manan Shah","doi":"10.1007/s11831-024-10126-2","DOIUrl":"10.1007/s11831-024-10126-2","url":null,"abstract":"<div><p>Groundwater is among the utmost essential renewable resources for every organism existing on Earth. Assessing water quality is critical for the ecosystem’s stability and conservation. The overall water quality possesses a significant effect on human being wellness and environmental preservation. Numerous applications of water exist, including those related to industries, agriculture, and consumption. The water quality index (WQI) is an essential metric for assessing water management effectiveness. By its biological, physical, and physiological features, water quality assesses whether water is suitable for a specific application or not. Water quality analysis has become a big concern in today’s world because of industrialization, industry, farming techniques, and people’s behavior. Quality of water has traditionally been examined using expensive testing facilities and numerical procedures, enabling monitoring in real-time obsolete. Improper quality of groundwater necessitates an additional feasible and affordable remedy. The algorithmic learning-based categorization technique looks to be promising for quick identification and estimation of water quality. Predicting the quality of water has been done effectively using machine learning algorithms. The technological investigation of computer algorithms as well as mathematical models that networks of computers employ to complete a certain task without having to be explicitly programmed is referred to as machine learning (ML). The major benefit associated with algorithmic machine learning models is that as an algorithm knows how to utilize data, it can perform its function independently. This work comprehensively examines three major machine learning techniques: Decision Tree, Regression Model, and Support Vector Machine. Features including total coliform, electric conductivity, biological oxygen demand, pH, dissolved oxygen, and nitrate determine the water quality. In this paper, many prior research that employed machine learning techniques for determining water quality in diverse regions were examined. A comparison of past research involving these algorithms, assessment methodologies, and acquired outcomes is offered. We performed a thorough analysis of the cutting-edge ML algorithms used to predict groundwater quality. As part of our methodology, we analysed a wide range of research, looked into the use of conventional and cutting-edge ML techniques, pre-processing techniques, feature selection techniques, and data augmentation methods. The findings of this study will help with groundwater development planning and will enhance the Machine learning applications in improving the quality of groundwater. Our analysis demonstrates the adaptability of ML techniques in predicting groundwater quality. We discovered that ML models, such as deep learning, ensemble approaches, neural networks, support vector machines, and linear regression, have been successfully used to predict the quality of groundwa","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4633 - 4654"},"PeriodicalIF":9.7,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525018","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":"Various Methods for Computing Risk Factors of Down Syndrome in Fetus","authors":"Sushil Kumar,&nbsp;K. Selvakumar","doi":"10.1007/s11831-024-10158-8","DOIUrl":"10.1007/s11831-024-10158-8","url":null,"abstract":"<div><p>There is a chromosomal defect that significantly affects an individual’s life is Down syndrome. Early identification of Down syndrome is crucial for an accurate assessment of the fetus. The process of assess the fetus includes measurement of the crown rump length, fetal heart rate, short arm or thighs bones length, nasal bone present or absent and the thickness of fluid behind neck. And the process are done during first and second trimester of pregnancy. Various invasive and noninvasive screenings are used for Down syndrome diagnosis. Research on diagnosing Down syndrome has been extensively documented. Additionally, this survey includes various techniques using deep learning for detecting the availability of Down Syndrome and does analysis of image processing methods and formulas for its diagnosis.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"485 - 498"},"PeriodicalIF":9.7,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548692","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 Probabilistic Approaches for Assessing the Liquefaction Hazard in Urban Areas","authors":"Alejandro Cruz,&nbsp;Shaghayegh Karimzadeh,&nbsp;Nicola Chieffo,&nbsp;Eimar Sandoval,&nbsp;Paulo B. Lourenço","doi":"10.1007/s11831-024-10124-4","DOIUrl":"10.1007/s11831-024-10124-4","url":null,"abstract":"<div><p>Several probabilistic liquefaction triggering approaches, or liquefaction manifestation severity approaches, have been developed to consider the uncertainties related to liquefaction and its manifestations. Probabilistic approaches are essential for vulnerability and risk models that considers the consequences of liquefaction on building performance. They may be incorporated into a performance-based earthquake engineering framework through a fully probabilistic liquefaction hazard assessment. The objective is to effectively incorporate spatial interaction of two concurrent hazards, specifically earthquake-induced shaking, and liquefaction, and to develop a robust multi-hazard framework applicable to regions with limited input data. For this purpose, it is necessary to establish, according to the available probabilistic liquefaction triggering or manifestation severity assessment approaches, which set of approaches aligns optimally with vulnerability and risk models. Thus, this paper discusses the current methodologies on the ongoing probabilistic liquefaction hazard assessment approaches with the aim of defining a reliable model specific for areas with a non-liquefiable surface layer over a liquefiable layer.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4673 - 4708"},"PeriodicalIF":9.7,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10124-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552681","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 Effect of Loading Inclination and Eccentricity on the Bearing Capacity of Shallow Foundations: A Review","authors":"Lysandros Pantelidis,&nbsp;Abdelaziz Meddah","doi":"10.1007/s11831-024-10113-7","DOIUrl":"10.1007/s11831-024-10113-7","url":null,"abstract":"<div><p>This paper provides a comprehensive review on the effect of load inclination and eccentricity on the bearing capacity of shallow foundations. Regarding load eccentricity, Meyerhof’s intuitive formula <span>({B}{prime}=B-2{e}_{b})</span> aligns well with finite element analyses, though it is slightly conservative. Analysis using finite element results revealed the more accurate formula <span>(B-1.9{e}_{b})</span>. Concerning load inclination factors, numerous such factors exist in the literature. However, most are either intuitive or derived from small-scale experimental results, rendering them unreliable due to the significant impact of model scale on the bearing capacity of footings. Based on numerical results, it is proposed that all inclination factors (namely <span>({i}_{c})</span>, <span>({i}_{gamma })</span> and <span>({i}_{q})</span>) can be reliably expressed by a formula of the form <span>({left(1-{f}_{1}cdot {tan }left({f}_{3}delta right)right)}^{{f}_{2}})</span>, where <span>(delta)</span> is the inclination angle of the loading with respect to the vertical, <span>({f}_{1})</span> and <span>({f}_{3})</span> are coefficients and <span>({f}_{2}=3)</span>. The latter ensures smooth transition from the bearing capacity failure to the sliding failure as <span>(delta)</span> increases. It is also observed that many <span>(i-)</span> factors in the literature and various design standards employ an impermissible combination of sliding resistance at the footing-soil interface and Mohr–Coulomb bearing capacity failure under the footing. Moreover, it is shown that only the <span>({i}_{c})</span> factor depends on the angle of internal friction of soil. Finally, Vesic’s 1975 “<i>m</i>” interpolation formula largely falls short in accurately representing the effect of the direction of the horizontal loading.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 7","pages":"4189 - 4208"},"PeriodicalIF":9.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10113-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548693","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":"Demystifying ChatGPT: An In-depth Survey of OpenAI’s Robust Large Language Models","authors":"Pronaya Bhattacharya,&nbsp;Vivek Kumar Prasad,&nbsp;Ashwin Verma,&nbsp;Deepak Gupta,&nbsp;Assadaporn Sapsomboon,&nbsp;Wattana Viriyasitavat,&nbsp;Gaurav Dhiman","doi":"10.1007/s11831-024-10115-5","DOIUrl":"10.1007/s11831-024-10115-5","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Recent advancements in natural language processing (NLP) have catalyzed the development of models capable of generating coherent and contextually relevant responses. Such models are applied across a diverse array of applications, including but not limited to chatbots, expert systems, question-and-answer robots, and language translation systems. Large Language Models (LLMs), exemplified by OpenAI’s Generative Pretrained Transformer (GPT), have significantly transformed the NLP landscape. They have introduced unparalleled abilities in generating text that is not only contextually appropriate but also semantically rich. This evolution underscores a pivotal shift towards more sophisticated and intuitive language understanding and generation capabilities within the field. Models based on GPT are developed through extensive training on vast datasets, enabling them to grasp patterns akin to human writing styles and deliver insightful responses to intricate questions. These models excel in condensing text, extending incomplete passages, crafting imaginative narratives, and emulating conversational exchanges. However, GPT LLMs are not without their challenges, including ethical dilemmas and the propensity for disseminating misinformation. Additionally, the deployment of these models at a practical scale necessitates a substantial investment in training and computational resources, leading to concerns regarding their sustainability. ChatGPT, a variant rooted in transformer-based architectures, leverages a self-attention mechanism for data sequences and a reinforcement learning-based human feedback (RLHF) system. This enables the model to grasp long-range dependencies, facilitating the generation of contextually appropriate outputs. Despite ChatGPT marking a significant leap forward in NLP technology, there remains a lack of comprehensive discourse on its architecture, efficacy, and inherent constraints. Therefore, this survey aims to elucidate the ChatGPT model, offering an in-depth exploration of its foundational structure and operational efficacy. We meticulously examine Chat-GPT’s architecture and training methodology, alongside a critical analysis of its capabilities in language generation. Our investigation reveals ChatGPT’s remarkable aptitude for producing text indistinguishable from human writing, whilst also acknowledging its limitations and susceptibilities to bias. This analysis is intended to provide a clearer understanding of ChatGPT, fostering a nuanced appreciation of its contributions and challenges within the broader NLP field. We also explore the ethical and societal implications of this technology, and discuss the future of NLP and AI. Our study provides valuable insights into the inner workings of ChatGPT, and helps to shed light on the potential of LLMs for shaping the future of technology and society. The approach used as Eco-GPT, with a three-level cascade (GPT-J, J1-G, GPT-4), achieves 73% and 60% cost savings in CaseHold an","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4557 - 4600"},"PeriodicalIF":9.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525020","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 Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers","authors":"Farzin Kazemi,&nbsp;Neda Asgarkhani,&nbsp;Torkan Shafighfard,&nbsp;Robert Jankowski,&nbsp;Doo-Yeol Yoo","doi":"10.1007/s11831-024-10143-1","DOIUrl":"10.1007/s11831-024-10143-1","url":null,"abstract":"<div><p>In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"571 - 603"},"PeriodicalIF":9.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11831-024-10143-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339827","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":"Computational ElectroHydroDynamics in microsystems: A Review of Challenges and Applications","authors":"Christian Narváez-Muñoz,&nbsp;Ali Reza Hashemi,&nbsp;Mohammad Reza Hashemi,&nbsp;Luis Javier Segura,&nbsp;Pavel B. Ryzhakov","doi":"10.1007/s11831-024-10147-x","DOIUrl":"10.1007/s11831-024-10147-x","url":null,"abstract":"<div><p>The principle of electrohydrodynamics (EHD) processes relies on manipulating fluids using electric forces. The advantage of EHD over other fluid manipulation approaches, such as thermal or acoustic-based processes, consists in its excellent controllability, versatility (in terms of the range of suitable fluids), and relatively low cost. The importance of modeling and simulation of EHD processes, particularly for the microsystems, has been growing over the past decade, replacing on many occasions trial-and-error approaches. The present paper is devoted to the advances in the numerical modeling and simulation of electrohydrodynamic (EHD) problems. Physical phenomena playing an essential role in EHD are explained and governing equations are formulated. Major challenges faced when modeling EHD problems are highlighted and different classes of numerical approaches used for handling them are outlined. Benefits and disadvantages as well as open issues in different numerical approaches are also discussed. Finally, the paper provides an overview of numerical studies of EHD in multi-phase micro-systems emphasizing some key findings for three classes of problems, namely droplets, jets and planar films exposed to external electric fields.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"535 - 569"},"PeriodicalIF":9.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339502","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":"AI Techniques in Detection of NTLs: A Comprehensive Review","authors":"Rakhi Yadav,&nbsp;Mainejar Yadav,&nbsp; Ranvijay,&nbsp;Yashwant Sawle,&nbsp;Wattana Viriyasitavat,&nbsp;Achyut Shankar","doi":"10.1007/s11831-024-10137-z","DOIUrl":"10.1007/s11831-024-10137-z","url":null,"abstract":"<div><p>In the operation of power grid, worldwide, non-technical losses (NTLs) occur in a massive amount of proportion which is observed up to 40% of the total electric transmission and distribution losses. These dominant losses severely affect to adverse the performance of all the private and public distribution sectors. By rectifying these NTLs, the necessity of establishing new power plants will automatically be cut down. Hence, NTLs have become a critical challenge to do research in this emerging area for researchers of power systems due to the limitations of the current methodologies to detect and fix up these prominent type of losses. The existing survey so for basically contains the detail of identification of non-technical losses by machine and deep learning methods while this paper is a complete trouble shooting to resolve this issue by systematic approach. To address this, causes of NTLs along with its impact on economies and types of NTLs are elaborated in various countries. In addition, we have also prepared a comparative analysis based on several essential parameters. Further, implementation process of detection of NTLs or electricity theft based on Machine Learning or Deep Learning has also been demonstrated. Moreover, major challenges of detection of NTLs or electricity theft based on ML and Deep Learning, and its possible solutions are also described. Hence, definitely this comprehensive survey will help to the leading researchers to reach a new height in this thrust area.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"31 8","pages":"4879 - 4892"},"PeriodicalIF":9.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141345073","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-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review","authors":"Chetan Pawar,&nbsp;B. Shreeprakash,&nbsp;Beekanahalli Mokshanatha,&nbsp;Keval Chandrakant Nikam,&nbsp;Nitin Motgi,&nbsp;Laxmikant D. Jathar,&nbsp;Sagar D. Shelare,&nbsp;Shubham Sharma,&nbsp;Shashi Prakash Dwivedi,&nbsp;Pardeep Singh Bains,&nbsp;Abhinav Kumar,&nbsp;Mohamed Abbas","doi":"10.1007/s11831-024-10144-0","DOIUrl":"10.1007/s11831-024-10144-0","url":null,"abstract":"<div><p>As researchers sought for new methods to decrease noxious emissions and improve engine performance, they discovered biodiesel as a promising biofuel. However, traditional study methodologies were deemed inadequate, prompting the need for computational methods to offer numerical solutions. This approach was seen as a creative and practical solution to the problem at hand. In response to the limitations of conventional modeling approaches, researchers turned towards the innovative solution of using machine-learning techniques as data processing systems. This creative approach has proven effective in addressing a broad variety of technical and scientific concerns, particularly in fields where traditional modeling approaches have fallen short of expectations. This review discusses using machine learning algorithms for predicting biodiesel performance and emissions with nanoparticles. Researchers have solved these problems with the application of machine learning to anticipate engine efficiency and emissions. The machine-learning algorithm predicts engine performance very precisely, proving its efficacy. Nanotechnology and biodiesel engine technologies are quickly advancing, making this review vital. Previous studies have examined nanoparticles' influence on engine performance and emissions. This review uniquely focuses on the application of machine learning techniques. Through the utilization of machine-learning algorithms, it is possible for gaining deeper understanding of intricate connections existing between the properties of nanoparticles and the behavior of engines. This methodology provides extensive comprehension of an impact of nanoparticles upon performance and emissions of biodiesel engines, hence enabling a development of more effectual and sustainable engine designs.</p></div>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":"32 1","pages":"499 - 533"},"PeriodicalIF":9.7,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347055","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":"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}