Data on Machine Learning Reported by Researchers at University of Georgia (Accel eration of Superpave Mix Design: Solving Multiobjective Optimization Problems U sing Machine Learning and the Non-dominated Sorting Genetic Algorithm-ii)

佐治亚大学研究人员报告的机器学习数据（加速Superpave混合设计：使用机器学习和非支配排序遗传算法解决多目标优化问题-ii）

Robotics & Machine Learning Daily News2024，Issue(Jun.21) ：6-7.

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Data on Machine Learning Reported by Researchers at University of Georgia (Accel eration of Superpave Mix Design: Solving Multiobjective Optimization Problems U sing Machine Learning and the Non-dominated Sorting Genetic Algorithm-ii)

佐治亚大学研究人员报告的机器学习数据（加速Superpave混合设计：使用机器学习和非支配排序遗传算法解决多目标优化问题-ii）

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摘要

由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-关于机器学习的最新研究结果已经发表。据NewsRx Journa Lists在佐治亚州雅典的新闻报道，研究表明：“传统的沥青混合料设计需要准备大量的样品进行试验，这耗费大量的时间和劳力。此外，根据个人经验选择集料级配和沥青含量，以确定混合料的性能符合规范是一个反复试验的过程。”本研究的财政支持来自美国运输大学运输中心的美国分部多式联运基础设施系统综合资产管理中心(CIAMTIS)。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news reporting from Athens, Georgia, by NewsRx journa lists, research stated, "The traditional asphalt mix design requires the prepara tion of many samples to test, which consumes much time and labor. Moreover, sele cting aggregate gradation and asphalt content based on individual experience unt il a mixture's properties meet a specification is a trial-and-error procedure." Financial support for this research came from Center for Integrated Asset Manage ment for Multimodal Transportation Infrastructure Systems (CIAMTIS), a US Depart ment of Transportation University Transportation Center, United States.

Key words

Athens/Georgia/United States/North an d Central America/Algorithms/Cyborgs/Emerging Technologies/Genetic Algorithm s/Genetics/Machine Learning/University of Georgia

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出版年

2024

Robotics & Machine Learning Daily News

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