ABSTRACT

The routine maintenance and rehabilitation of road pavement is vital to keep up with the service level and bearing capacity. However, the current problem is that, with the continuous increase of the scale of maintenance data, the traditional decision-making cannot satisfy the requirements in terms of accuracy and efficiency. The objectives of this paper were to improve the accuracy and efficiency of the maintenance decisions, overcome the decision error caused by insufficient human experience, and develop the mapping process for decision plans. This paper presented a decision-making method for asphalt pavement maintenance using improved weight random forest algorithm (IWRF) based on the correlation analysis (CA) and the analytic hierarchy process (AHP). Firstly, appropriate features were selected through CA of road detection data, and then decision trees were constructed based on Bootstrapping. Finally, qualified decision trees were chosen and weighted by AHP to form a random forest. To examine the feasibility, the algorithm was applied in a maintenance decision of the 80 km highway in Jiangsu province. The results showed that IWRF had a decision-making accuracy of up to 90%. Comparing with the traditional random forest algorithm, the IWRF algorithm had a 4.35% higher accuracy and saved 75% computation time.

摘要

道路路面的日常维护和修复对于跟上服务水平和承载能力至关重要。然而，目前的问题是，随着维修数据规模的不断增加，传统的决策在准确性和效率上已经不能满足要求。本文的目标是提高维护决策的准确性和效率，克服因人为经验不足而导致的决策错误，并开发决策计划的映射过程。本文提出了一种基于相关分析（CA）和层次分析法（AHP）的改进权重随机森林算法（IWRF）的沥青路面养护决策方法。首先，通过道路检测数据的CA选择合适的特征，然后基于Bootstrapping构建决策树。最后，通过层次分析法选择合格的决策树并加权，形成随机森林。为了检验可行性，将该算法应用于江苏省80公里高速公路的维护决策中。结果表明，IWRF的决策准确率高达90%。与传统的随机森林算法相比，IWRF算法的准确率提高了4.35%，计算时间节省了75%。