The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R = 0.841, MAE = 0.592, and RMSE = 0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

代表路面结构状况的最常见指标是结构编号。当前确定结构编号的程序包括利用落锤挠度计和穿透地面的雷达测试，记录人行道表面挠度以及通过反算方式分析记录的挠度。该程序有两个缺点：重量下降式偏转仪和探地雷达是昂贵的测试；与精确方法相比，反向计算方法具有固有的缺点，因为它们采用了试错法。在这项研究中，三种机器学习方法分别称为高斯过程回归，M5P模型树和随机森林，用于预测柔性路面的结构数。本文的数据集与伊朗Semnan和Khuzestan省的759个柔性路面部分有关，并包括“结构数”作为输出和“表面挠度和表面温度”作为输入。根据R的三个标准检查了结果的准确性，MAE和RMSE。在本文采用的方法中，随机林是最准确的，因为它可以为上述标准（R = 0.841，MAE = 0.592和RMSE = 0.760）提供最佳值。所提出的方法不需要使用探地雷达测试，从而降低了成本和工作难度。使用机器学习方法代替反向计算可提高计算过程的质量和准确性。