以物理学为指导的神经网络,以平衡的准确性,稳定性和理性来预测青混合物的路线
Yong Deng1, Haifeng Wang1, Xianming Shi1
1Department of Civil & Environmental Engineering, Washington State University, Pullman, WA, 99164-2910, USA.
概括
预测青路径是非常具有挑战性的. 这项研究引入了一种新的机器学习框架,该框架使用物理信息来提高青混合物的预测准确性,稳定性和可解释性.
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
- 计算科学 计算科学
背景情况:
- 由于复杂的影响因素,在青材料中预测滑性能是复杂的.
- 当前的机器学习模型往往将准确性优先于稳定性和可解释性,导致过度拟合和应用性降低.
- 需要先进的建模框架,以平衡预测能力与物理理解.
研究的目的:
- 开发一种新的建模框架,用于预测青混合物路性能.
- 将物理信息集成到机器学习模型中,以提高稳定性和理性.
- 提高路线预测模型的可解释性和适用性.
主要方法:
- 利用自动编码器从青材料数据中有效选择特征.
- 使用feedforward神经网络来预测路径深度.
- 将选定变量的物理信息纳入神经网络架构中.
主要成果:
- 拟议的框架在预测青路径方面取得了很高的准确性.
- 运用物理信息显著提高了模型的稳定性和合理性.
- 与传统方法相比,该模型显示了更好的解释性和适用性.
结论:
- 这种新的框架有效地平衡了模型的准确性,稳定性和合理性,用于青路径预测.
- 将基于物理的知识与数据驱动的机器学习相结合,为路面工程提供了一个有前途的方法.
- 这种方法具有很大的潜力,可以准确可靠地预测路面困境.
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