Wang Qiang, Zhou Mengmeng, Sabri Mohanad Muayad Sabri, Huang Jiandong
School of Mines, China University of Mining and Technology, Xuzhou 221116, China.
Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.
Materials (Basel). 2022 Aug 15;15(16):5605. doi: 10.3390/ma15165605.
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 (IRI) and total joint faulting cumulated per km (TFAULT) were the highest for the IRI of JPCP.
国际平整度指数（IRI）可用于评估路面的平整度。先前提出的用于模拟普通混凝土接缝路面（JPCP）IRI的力学经验路面设计指南（MEPDG），本研究考虑到其预测精度低的缺点对其进行了修改。为提高JPCP的IRI预测效果的可靠性，本研究采用支持向量机（SVM）、决策树（DT）和随机森林（RF）等机器学习方法，通过甲虫触角搜索（BAS）算法的超参数进行优化，比较了JPCP的IRI预测精度。机器学习过程的结果表明，BAS算法可以有效地提高超参数调整的有效性，进而提高优化的速度和精度。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，在本研究中IRI与所有输入变量成正比，对于JPCP的IRI，初始平整度（IRI）和每公里累计的总接缝错台（TFAULT）的重要性得分最高。
