Classification of Variable Foundation Properties Based on Vehicle–Pavement–Foundation Interaction Dynamics

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 21; p. 6263
• Main Author: Kim, Robin Eunju
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.11.2020; MDPI
• Subjects: Automation; machine learning-based classification; Noise; non-uniform foundation; Roads & highways; stochastic analysis; vehicle–pavement–foundation interaction; United States > US
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20216263

The dynamic interaction between vehicle, roughness, and foundation is a fundamental problem in road management and also a complex problem, with their coupled and nonlinear behavior. Thus, in this study, the vehicle–pavement–foundation interaction model was formulated to incorporate the mass inertia of the vehicle, stochastic roughness, and non-uniform and deformable foundation. Herein, a quarter-car model was considered, a filtered white noise model was formulated to represent the road roughness, and a two-layered foundation was employed to simulate the road structure. To represent the non-uniform foundation, stiffness and damping coefficients were assumed to vary either in a linear or in a quadratic manner. Subsequently, an augmented state-space representation was formulated for the entire system. The time-varying equation governing the covariance of the response was solved to examine the vehicle response, subject to various foundation properties. Finally, a linear discriminant analysis method was employed for classifying the foundation types. The performance of the classifier was validated by test sets, which contained 100 cases for each foundation type. The results showed an accuracy of over 90%, indicating that the machine learning-based classification of the foundation had the potential of using vehicle responses in road managements.