Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models
Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models
复制标题
开发路面性能预测模型的机器学习技术综述
DOI：
10.3390/su13095248
复制
发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
G. Flintsch
中科院分区：
文献类型：
--
作者：
Rita Justo;Adelino Ferreira;G. Flintsch
公路运输一直是发展中社会固有的问题，影响国内生产总值（GDP）的10-20%。它负责个人的流动性（获得服务，商品和休闲），这就是为什么世界经济依赖于交通设施的高效和安全运行。道路维护至关重要，因为随着道路基础设施老化，维护需求增加，而且基于可持续性，这意味着现在花钱，将来可以节省更多。此外，道路养护在道路安全方面发挥着重要作用。然而，路面管理是一项具有挑战性的任务，因为可用的预算有限。公路机构需要制定短期和长期方案规划，以选择和安排维护和修复作业。路面性能预测模型（PPPMs）是路面管理系统（PMSs）中的一个关键要素，它可以预测路面的损坏情况，从而实现积极有效的管理。这项工作的目的是审查的建模技术，这些模型的发展中常用的。路面劣化过程本质上是随机的。它需要复杂的确定性或概率性建模技术，这将在这里介绍，以及每一个的优点和缺点。最后，将得出结论，并提出一些支持PPPM发展的指导方针。
Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.