Abstract: A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified/distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.

Abstract: An advance in the art is made according to aspects of the present disclosure directed to distributed fiber optic sensing systems (DFOS), methods, and structures that advantageously monitor and identify—in real-time—roadway traffic and patterns across a multiple-lane highway by employing a multiple-transverse fiber optic cable arrangement of optical fiber cable positioned under the highway/roadway to detect, monitor, and/or identify traffic.

Abstract: An advance in the art is made according to aspects of the present disclosure directed to distributed fiber optic sensing systems (DFOS), methods, and structures that advantageously monitor and identify—in real-time—roadway traffic and patterns across a multiple-lane highway by employing a multiple-transverse fiber optic cable arrangement of optical fiber cable positioned under the highway/roadway to detect, monitor, and/or identify traffic.