{"title":"Optimizing asphalt foaming using neural network","authors":"Ali Saleh, László Gáspár","doi":"10.1556/606.2023.00896","DOIUrl":null,"url":null,"abstract":"Abstract This study uses a three-layer backpropagation neural network combined with particle swarm optimization to control the foamed bitumen in cold recycling technology. The foaming process of bitumen is non-linear and depends on dynamic temperature. By developing a neural network model, this study effectively captures the complex relationships between temperature, water content, air pressure, and the expansion ratio and half-life of foamed bitumen. The integration of particle swarm optimization enhances the accuracy and convergence of the neural network model by optimizing the initial weights. This optimization process improves the model's ability to predict and control the quality of foamed bitumen accurately. It serves as a valuable tool for the rapid development of high-quality cold asphalt design.","PeriodicalId":35003,"journal":{"name":"Pollack Periodica","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pollack Periodica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1556/606.2023.00896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract This study uses a three-layer backpropagation neural network combined with particle swarm optimization to control the foamed bitumen in cold recycling technology. The foaming process of bitumen is non-linear and depends on dynamic temperature. By developing a neural network model, this study effectively captures the complex relationships between temperature, water content, air pressure, and the expansion ratio and half-life of foamed bitumen. The integration of particle swarm optimization enhances the accuracy and convergence of the neural network model by optimizing the initial weights. This optimization process improves the model's ability to predict and control the quality of foamed bitumen accurately. It serves as a valuable tool for the rapid development of high-quality cold asphalt design.
期刊介绍:
Pollack Periodica is an interdisciplinary, peer-reviewed journal that provides an international forum for the presentation, discussion and dissemination of the latest advances and developments in engineering and informatics. Pollack Periodica invites papers reporting new research and applications from a wide range of discipline, including civil, mechanical, electrical, environmental, earthquake, material and information engineering. The journal aims at reaching a wider audience, not only researchers, but also those likely to be most affected by research results, for example designers, fabricators, specialists, developers, computer scientists managers in academic, governmental and industrial communities.