The complex structure of bituminous mixtures ranging from nanoscale binder components to macroscale pavement performance requires a comprehensive approach to material characterization and performance prediction. This paper provides a critical analysis of advanced techniques in paving materials modeling. It focuses on four main approaches: finite element method (FEM), discrete element method (DEM), phase field method (PFM), and artificial neural networks (ANNs). The review highlights how these computational methods enable more accurate predictions of material behavior, from asphalt binder rheology to mixture performance, while reducing reliance on extensive empirical testing. Key advances, such as the smooth integration of information across multiple scales and the emergence of physics-informed neural networks (PINNs), are discussed as promising avenues for enhancing model accuracy and computational efficiency. This review not only provides a comprehensive overview of current methodologies but also outlines future research directions aimed at developing more sustainable, cost-effective, and durable paving solutions through advanced multiscale modeling techniques.

中文翻译：

---

探索数值模拟和机器学习在多尺度铺路材料分析中的作用：应用、挑战、最佳实践


从纳米级粘结剂成分到宏观路面性能，沥青混合物的复杂结构需要一种全面的材料表征和性能预测方法。本文对铺路材料建模中的先进技术进行了批判性分析。它侧重于四种主要方法：有限元法 （FEM）、离散元法 （DEM）、相场法 （PFM） 和人工神经网络 （ANN）。该综述强调了这些计算方法如何能够更准确地预测材料行为，从沥青粘结剂流变学到混合料性能，同时减少对大量实证测试的依赖。关键进展，例如跨多个尺度的平滑信息整合和物理信息神经网络 （PINN） 的出现，被讨论为提高模型准确性和计算效率的有前途的途径。这篇综述不仅全面概述了当前方法，还概述了未来的研究方向，旨在通过先进的多尺度建模技术开发更可持续、更具成本效益和更耐用的摊铺解决方案。