이동차량의 동적 확률모델을 통한 도로 함몰예상지역식별 알고리즘의 개발

Abstract

PURPOSES: This paper develops a new stochastic approach to analyze the pavement-vehicle interaction model with a certain roughness and elasticity for the pavement foundation, thereby accommodating the deflection of the pavement, and to identify the road subsidence zone represented with a sudden changes in the elasticity of the foundation. METHODS: In the proposed model, a quarter-car model was combined with a filtered white noise model of road roughness and a two-layer foundation (Euler-Bernoulli beam for the top surface and Winkler foundation to represent the sub-structure soil). An augmented state-space model for the subsystems was formulated. Then, because the input is White noise and the system is represented as a single system, the Lyapunov equation governing the covariance of the system’s response was solved to obtain a structurally weak zone index (WZI). RESULTS: The results showed that the WZI from the pavement-vehicle interaction model is sensitive enough to identify road subsidence. In particular, the WZI rapidly changed with a small change in foundation elasticity, indicating that the model has the potential to detect road subsidence in the early stage. CONCLUSIONS: Beacause of the simplicity of the calculation, the proposed approach has potential applications in managing road conditions while a vehicle travels along the road and detecting road subsidence using a device with an on-board computational capability, such as a smart phone.