이동차량의 동적 확률모델을 통한 도로 함몰예상지역식별 알고리즘의 개발The Identification of Road Subsidence Zone from Stochastic Modelling of Dynamic Characteristics of a Moving Vehicle
- Other Titles
- The Identification of Road Subsidence Zone from Stochastic Modelling of Dynamic Characteristics of a Moving Vehicle
- Authors
- 김은주; 전성일
- Issue Date
- Dec-2018
- Publisher
- 한국도로학회
- Keywords
- Elastic foundation; Lyapunov solution; Weak foundation identification; Stochastic model
- Citation
- 한국도로학회논문집, v.20, no.6, pp.31 - 39
- Indexed
- KCI
- Journal Title
- 한국도로학회논문집
- Volume
- 20
- Number
- 6
- Start Page
- 31
- End Page
- 39
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/15201
- DOI
- 10.7855/IJHE.2018.20.6.031
- ISSN
- 1738-7159
- 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.
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