可采用国际粗糙度指数(IRI)来评价路面的平整度。先前提出的机械经验路面设计指南(MEPDG),用于模拟接缝素混凝土路面(JPCP)的IRI,考虑到其预测精度低的缺点,本研究对此进行了修改。为了提高JPCPIRI预测效果的可靠性,本研究通过使用支持向量机(SVM)的机器学习方法,比较了JPCP的IRI预测精度,决策树(DT),和随机森林(RF),通过甲虫天线搜索(BAS)算法的超参数优化。机器学习过程的结果表明,BAS算法能有效提高超参数整定的有效性,提高优化的速度和准确性。RF模型被证明是上述三种模型中预测精度最高的模型。最后,本研究分析了输入变量对IRI的重要性得分,结果表明,IRI与本研究中的所有输入变量成正比,JPCP的IRI的初始平滑度(IRII)和每km累积的总节理断层(TFAULT)的重要性得分最高。
The international roughness index (IRI) can be employed to evaluate the smoothness of pavement. The previously proposed mechanical-empirical pavement design guide (MEPDG), which is used to model the IRI of joint plain concrete pavement (JPCP), has been modified in this
study considering its disadvantage of low prediction accuracy. To improve the reliability of the prediction effect of the IRI for JPCP, this study compares the prediction accuracy of the IRI of JPCP by using the machine-learning methods of support vector machine (SVM), decision tree (DT), and random forest (RF), optimized by the
hyperparameter of the beetle antennae search (BAS) algorithm. The results from the machine-learning process show that the BAS algorithm can effectively improve the effectiveness of
hyperparameter tuning, and then improve the speed and accuracy of optimization. The RF model proved to be the one with the highest prediction accuracy among the above three models. Finally, this
study analyzes the importance score of input variables to the IRI, and the results show that the IRI was proportional to all the input variables in this
study, and the importance score of initial smoothness (IRII) and total joint faulting cumulated per km (TFAULT) were the highest for the IRI of JPCP.