This contribution is about predicting maintenance alerts in roads and selecting the most appropriate type of interventions recommended for preventing the occurrence of future failures. The objective is aligned with that covered by pavement maintenance decision support systems (PMDSS), though the methodology presented can be applied to other non-pavement road linear assets. The purpose is to summarise the main findings in the development of an approach based on testing the four most extended machine learning techniques (ML), namely Decision Trees (DT), K-Nearest Neighbourhood (KNN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN), using data from the historical inventory of inspections and maintenance interventions of a case study to illustrate the potential that such approach can offer to road maintenance managers. The correlation process embodies supervised and unsupervised training of models. The maintenance predictions are presented and compared over various segments corresponding to the real maintenance interventions conducted on an existing road network of a geographical zone.

这项贡献是关于预测道路维护警报并选择最合适的干预措施类型，以防止未来发生故障。该目标与路面维护决策支持系统 (PMDSS) 所涵盖的目标一致，尽管所提出的方法可以应用于其他非路面道路线性资产。目的是总结基于测试四种最扩展的机器学习技术 (ML)，即决策树 (DT)、K-最近邻域 (KNN)、支持向量机 (SVM) 和人工神经网络 (ANN)，使用案例研究的检查和维护干预历史清单中的数据来说明这种方法可以为道路维护管理人员提供的潜力。相关过程体现了模型的有监督和无监督训练。在与在地理区域的现有道路网络上进行的实际维护干预相对应的各个路段上呈现和比较维护预测。