Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Korea.
Sensors (Basel). 2020 Nov 3;20(21):6263. 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.
车辆、粗糙度和基础之间的动态相互作用是道路管理中的一个基本问题，也是一个复杂的问题，具有耦合和非线性行为。因此，在本研究中，提出了一个车辆-路面-基础相互作用模型，以纳入车辆的质量惯性、随机粗糙度以及非均匀和可变形的基础。这里考虑了一个四分之一车辆模型，建立了一个滤波白噪声模型来表示道路粗糙度，并采用两层基础来模拟道路结构。为了表示非均匀基础，假设刚度和阻尼系数以线性或二次方式变化。随后，为整个系统建立了增广状态空间表示。求解了响应协方差的时变方程，以研究在各种基础特性下车辆的响应。最后，采用线性判别分析方法对基础类型进行分类。通过测试集验证了分类器的性能，每个基础类型包含 100 个案例。结果表明，分类器的准确率超过 90%，这表明基于机器学习的基础分类可以利用车辆响应进行道路管理。
