Asphalt Pavement Roughness Prediction Based on Gray GM(1,1|sin) Model
- https://doi.org/10.2991/ijcis.d.190808.002How to use a DOI?
- Roughness; Prediction; Gray theory; Particle swarm optimization
Roughness is a comprehensive assessment indicator of pavement performance. Prediction of pavement roughness exhibits great difficulties by using traditional methods such as mechanistic-empirical method and regression method. Considering the fact that the value of international roughness index (IRI) varies in a fluctuant manner, in this paper, a new gray model based method is proposed to predict the roughness of pavement. The proposed method adopts GM(1,1|sin) model as the prediction model. In GM(1,1|sin) model, a sinusoidal term is added into GM(1,1) model, making it can fit fluctuant data more precisely than GM(1,1) model. A particle swarm optimization (PSO) algorithm is used to select the optimal parameter of GM(1,1|sin) model. Experimental results demonstrate its effectiveness of the proposed method. Furthermore, the proposed method only uses the history IRI data in prediction and leads to a large savings of collecting pavement condition data.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Xiuli Zhang AU - Chunming Ji PY - 2019 DA - 2019/08/19 TI - Asphalt Pavement Roughness Prediction Based on Gray GM(1,1|sin) Model JO - International Journal of Computational Intelligence Systems SP - 897 EP - 902 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190808.002 DO - https://doi.org/10.2991/ijcis.d.190808.002 ID - Zhang2019 ER -