论文标题
高级机器学习的智能道路检查；智能移动性和运输维护系统的混合预测模型
Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems
论文作者
论文摘要
通过使用机器学习方法，已经大大改善了移动性和运输维护系统中的预测模型。本文提出了用于智能道路检查的新型机器学习模型。基于路面条件指数（PCI）的传统道路检查系统通常与关键的安全性，能源和成本问题有关。另外，提出的模型利用体重偏转计（FWD）测试的表面挠度数据来预测PCI。机器学习方法是单一多层感知器（MLP）和径向基础功能（RBF）神经网络及其混合动力，即Levenberg-Marquardt（MLP-LM），缩放的结合结合物梯度（MLP-SCG），帝国主义竞争（帝国主义竞争（RBF-ICA）和GENGICE ALGFAS（RBF-GA）（RBF-GA）（RBF-GA）（RB）。此外，采用了委员会机器智能系统（CMI）方法来结合结果并提高建模的准确性。通过使用四个平均相对误差百分比（APRE），平均绝对百分比相对误差（AAPRE），均方根误差（RMSE）和标准误差（SD）的四个标准，平均绝对百分比相对误差（APRE），平均绝对百分比（APRE）和标准。 CMIS模型的表现优于其他模型，其有希望的结果= 2.3303，AAPRE = 11.6768，RMSE = 12.0056，SD = 0.0210。
Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
