Asphalt Pavement Roughness Prediction Based on Gray GM(1,1|sin) Model

DOI

10.2991/ijcis.d.190808.002How to use a DOI?

Keywords

Roughness; Prediction; Gray theory; Particle swarm optimization

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/ijcis.d.190808.002
ID  - Zhang2019
ER  -