创新的机器学习方法用于预测在马歇尔设计青混合物的青含量
Mutahar Al-Ammari1,2, Ruikun Dong1, Mohammed Nasser3
1School of Civil Engineering, Chongqing University, Chongqing 400045, China.
Materials (Basel, Switzerland)
|April 24, 2025
概括
机器学习模型,特别是神经网络,可以准确地预测青混合物特性,以便在路面设计中获得最佳的青含量. 与传统的马歇尔混合设计方法相比,这种方法提高了准确性和可靠性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 灵活的路面性能依赖于适当的马歇尔混合设计.
- 传统的马歇尔混合设计是劳动密集型,昂贵和耗时的.
- 准确预测青混合物特性对于路面的寿命和安全至关重要.
研究的目的:
- 将传统的马歇尔混合设计与机器学习 (ML) 方法进行比较,以确定最佳的青含量.
- 研究基于ML的回归技术,用于预测关键的青混合物特性.
- 评估ML模型在捕捉青混合物内的复杂关系方面的有效性.
主要方法:
- 60个数据集的数据库被策划用于ML模型开发.
- 在第一阶段使用传统方法来确定理想的青含量.
- 在第二阶段,应用了各种ML回归算法并进行了比较,以预测青混合物特性 (马歇尔稳定性,流量,VMA,VFA,单位重量).
主要成果:
- 神经网络模型在其他ML算法上表现出卓越的性能.
- 神经网络实现了最高的准确性,用R2和MSE值来表示.
- 机器学习模型,特别是神经网络,在预测青混合物特性方面显示出重大潜力.
结论:
- 神经网络模型有效地捕捉了青混合物的复杂性.
- 与传统方法相比,机器学习为青混合物分析提供了更高的准确性和可靠性.
- 这项研究支持使用ML来优化青路面设计和性能预测.
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