The subgrade performance assessment and targeted maintenance of a highway during operation is very important and challenging. This paper focuses on the performance of the whole life-cycle of a highway subgrade during the operational period. Four roads with different traffic volume and geological conditions were selected; 20 test sections of these 4 roads were examined for a three-year distress survey, and 18 specific subgrade distresses of the 5 assessment objects were tracked and collected. First, based on the analytic hierarchy process (AHP), the subgrade performance of the selected section is evaluated, and the subgrade performance index (SPI) at different time periods is obtained. Then, based on the internal and external factors which affect the subgrade, three algorithms to determine the optimal support vector machine (SVM) model were proposed to train and predict the SPI. The results show that the SPI predicted results based on the data time series and particle swarm optimization–least squares SVM (PSO–LSSVM) model are better than those based on grid search (Grid-SVM) and genetic algorithm (GA-SVM) models. Finally, this paper provides a detailed idea for the rational layout of subgrade life-cycle assessment and decision-making by establishing a subgrade performance assessment–prediction–maintenance–management architecture system.

中文翻译：

---

基于层次分析法和PSO-LSSVM模型的路基生命周期性能评估与病险预测

在运营过程中，对高速公路的路基性能评估和有针对性的维护非常重要且充满挑战。本文着重于运营期间公路路基整个生命周期的性能。选择了四条交通量和地质条件不同的道路；对这4条道路的20个测试路段进行了为期三年的遇险调查，并对5个评估对象的18个特定路基遇险进行了跟踪和收集。首先，基于层次分析法（AHP），评估所选路段的路基性能，并获得不同时间段的路基性能指数（SPI）。然后，根据影响路基的内部和外部因素，提出了三种确定最优支持向量机（SVM）模型的算法来训练和预测SPI。结果表明，基于数据时间序列和粒子群优化-最小二乘支持向量机（PSO-LSSVM）模型的SPI预测结果优于基于网格搜索（Grid-SVM）和遗传算法（GA-SVM）模型的SPI预测结果。最后，本文通过建立路基绩效评估—预测—维护—管理体系结构体系，为路基生命周期评估和决策的合理布局提供了详细的思路。