Development of estimated models of the number of potholes with the statistical optimization method
- 80 Downloads
The objective of this paper is to determine a predictive model that uses the harmony search algorithm (HSA) based on available the multi-regression equation. The model employs the least squares method to predict the number of potholes in the Seoul metropolitan area. Independent variables were determined, based on traffic and weather data for each month in Seoul. Prior to the development of predictive models, empirical and stochastic factors that affect the occurrence of potholes were determined, resulting in a standardized regression coefficient from multi-linear regression analysis. A best-fit equation was derived from experiments using independent variables obtained from empirical and analytical approaches. The empirically and analytically filtered factors for each road management area in Seoul were used to develop the predictive models for the multiple regression analysis and the HSA. Fourteen predictive models were determined in this study. A performance comparison between these predictive models was conducted using the P-value, the root mean squared error, and the coefficient of determination.
Keywordsharmony search algorithm pothole pavement multiple regression analysis
Unable to display preview. Download preview PDF.
- Alia, O. M., Mandava, R., and Ramachandram, D. (2009) “Harmony search-based cluster initialization for fuzzy c-means segmentation of MR images.” TENCOM 2009 Conference Proceedings, DOI: 10.1109/TENCON.2009.5396049.Google Scholar
- Jog, G., Koch, C., Golparvar-Fard, M., and Brilakis, I. (2012). “Pothole properties measurement through visual 2D recognition and 3D reconstruction.” Computing in Civil Engineering, Proceedings, pp. 553–560, DOI: 10.1061/9780784412343.0070.Google Scholar
- Lee, C-J., Kim, D-W., Mun, S., and Yoo, P-J. (2012). “Study on a prediction model of the tensile strain related to the fatigue cracking performance of asphalt concrete pavements through design of experiments and harmony search algorithm.” Journal of Korean Society of Road Engineering, Vol. 14, No. 2, pp. 11–17, DOI: 10.7855/IJHE.2012.14.2.011.Google Scholar
- Petroutsatou, C., Lambropoulos, S., and Pantouvakis, J-P. (2006). “Road tunnel early cost estimates using multiple regression analysis.” Operational Research. An International Journal, Vol. 6, No. 3, pp. 311–322, DOI: 10.1007/BF02941259.Google Scholar
- Yang, C. (2014). “The high cost of New York’s broken roads.” Epoch Times, February 27, 2014.Google Scholar