  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.
