基于车轮跟踪试验的遗传算法优化沥青混合料粘弹性参数研究

IF 2.7 Q2 CONSTRUCTION & BUILDING TECHNOLOGY Infrastructures Pub Date : 2023-11-28 DOI:10.3390/infrastructures8120169
Jinxi Zhang, Weiqi Zhou, Dandan Cao, Jia Zhang
{"title":"基于车轮跟踪试验的遗传算法优化沥青混合料粘弹性参数研究","authors":"Jinxi Zhang, Weiqi Zhou, Dandan Cao, Jia Zhang","doi":"10.3390/infrastructures8120169","DOIUrl":null,"url":null,"abstract":"The generalized Maxwell (GM) constitutive model has been widely applied to characterize the viscoelastic properties of asphalt mixtures. The parameters (Prony series) of the GM are usually obtained via interconversion between a dynamic modulus and relaxation modulus, and they are then input to a finite element model (FEM) as viscoelastic parameters. However, the dynamic modulus obtained with the common loading mode only provides the compressive and tensile properties of materials. Whether the compression or tensile modulus can represent the shear properties of materials related to flow rutting is still open to discussion. Therefore, this study introduced a novel method that integrates the Kriging model into the genetic algorithm as a surrogate model to determine the viscoelastic parameters of an asphalt mixture in rutting research. Firstly, a wheel tracking test (WTT) for AC-13 was conducted to clarify the flow rutting development mechanism. Secondly, two sets of the AC-13 viscoelastic parameters obtained through the optimization method and the dynamic modulus were used as inputs into the FEM simulation of the WTT to compare the simulation results. Finally, a sensitivity analysis of viscoelastic parameters was performed to improve the efficiency of parameter optimization. The results indicating the viscoelastic parameters obtained by this method could precisely characterize the development law of flow rutting in asphalt mixtures.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Study on the Genetic Algorithm Optimization of an Asphalt Mixture’s Viscoelastic Parameters Based on a Wheel Tracking Test\",\"authors\":\"Jinxi Zhang, Weiqi Zhou, Dandan Cao, Jia Zhang\",\"doi\":\"10.3390/infrastructures8120169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The generalized Maxwell (GM) constitutive model has been widely applied to characterize the viscoelastic properties of asphalt mixtures. The parameters (Prony series) of the GM are usually obtained via interconversion between a dynamic modulus and relaxation modulus, and they are then input to a finite element model (FEM) as viscoelastic parameters. However, the dynamic modulus obtained with the common loading mode only provides the compressive and tensile properties of materials. Whether the compression or tensile modulus can represent the shear properties of materials related to flow rutting is still open to discussion. Therefore, this study introduced a novel method that integrates the Kriging model into the genetic algorithm as a surrogate model to determine the viscoelastic parameters of an asphalt mixture in rutting research. Firstly, a wheel tracking test (WTT) for AC-13 was conducted to clarify the flow rutting development mechanism. Secondly, two sets of the AC-13 viscoelastic parameters obtained through the optimization method and the dynamic modulus were used as inputs into the FEM simulation of the WTT to compare the simulation results. Finally, a sensitivity analysis of viscoelastic parameters was performed to improve the efficiency of parameter optimization. The results indicating the viscoelastic parameters obtained by this method could precisely characterize the development law of flow rutting in asphalt mixtures.\",\"PeriodicalId\":13601,\"journal\":{\"name\":\"Infrastructures\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infrastructures\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/infrastructures8120169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infrastructures","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/infrastructures8120169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

广义麦克斯韦(GM)构成模型已被广泛应用于表征沥青混合料的粘弹特性。GM 的参数(Prony 系列)通常通过动态模量和松弛模量之间的相互转换获得,然后将其作为粘弹性参数输入到有限元模型(FEM)中。然而,通过普通加载模式获得的动态模量只能提供材料的压缩和拉伸特性。压缩模量或拉伸模量能否代表与流动车辙相关的材料剪切特性仍有待讨论。因此,本研究引入了一种新方法,将克里金模型集成到遗传算法中,作为车辙研究中确定沥青混合料粘弹性参数的代用模型。首先,对 AC-13 进行了轮迹试验(WTT),以明确流动车辙的发展机理。其次,将通过优化方法获得的两组 AC-13 粘弹性参数和动态模量作为 WTT 有限元模拟的输入,以比较模拟结果。最后,对粘弹性参数进行了敏感性分析,以提高参数优化的效率。结果表明,通过该方法获得的粘弹性参数可以精确表征沥青混合料流动车辙的发展规律。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Study on the Genetic Algorithm Optimization of an Asphalt Mixture’s Viscoelastic Parameters Based on a Wheel Tracking Test
The generalized Maxwell (GM) constitutive model has been widely applied to characterize the viscoelastic properties of asphalt mixtures. The parameters (Prony series) of the GM are usually obtained via interconversion between a dynamic modulus and relaxation modulus, and they are then input to a finite element model (FEM) as viscoelastic parameters. However, the dynamic modulus obtained with the common loading mode only provides the compressive and tensile properties of materials. Whether the compression or tensile modulus can represent the shear properties of materials related to flow rutting is still open to discussion. Therefore, this study introduced a novel method that integrates the Kriging model into the genetic algorithm as a surrogate model to determine the viscoelastic parameters of an asphalt mixture in rutting research. Firstly, a wheel tracking test (WTT) for AC-13 was conducted to clarify the flow rutting development mechanism. Secondly, two sets of the AC-13 viscoelastic parameters obtained through the optimization method and the dynamic modulus were used as inputs into the FEM simulation of the WTT to compare the simulation results. Finally, a sensitivity analysis of viscoelastic parameters was performed to improve the efficiency of parameter optimization. The results indicating the viscoelastic parameters obtained by this method could precisely characterize the development law of flow rutting in asphalt mixtures.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Infrastructures
Infrastructures Engineering-Building and Construction
CiteScore
5.20
自引率
7.70%
发文量
145
审稿时长
11 weeks
期刊最新文献
A Comprehensive Study on Unsupervised Transfer Learning for Structural Health Monitoring of Bridges Using Joint Distribution Adaptation Improving Lightweight Structural Tuff Concrete Composition Using Three-Factor Experimental Planning Buckling Instability of Monopiles in Liquefied Soil via Structural Reliability Assessment Framework Effect of Incorporating Cement and Olive Waste Ash on the Mechanical Properties of Rammed Earth Block Integrating Machine Learning in Geotechnical Engineering: A Novel Approach for Railway Track Layer Design Based on Cone Penetration Test Data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1