Modeling of pavement roughness utilizing artificial neural network approach for Laos national road network
Abstract
The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.
Keyword : International Roughness Index (IRI), Laos pavement management system (PMS), artificial neural network (ANN), backpropagation algorithm, double bituminous surface treatment (DBST), asphalt concrete (AC), pavement age, cumulative equivalent single-axle load (CESAL), pavement performance model
How to Cite
Gharieb, M., Nishikawa, T., Nakamura, S., & Thepvongsa, K. (2022). Modeling of pavement roughness utilizing artificial neural network approach for Laos national road network. Journal of Civil Engineering and Management, 28(4), 261–277. https://doi.org/10.3846/jcem.2022.15851
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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ARA. (2001). Guide for mechanistic-empirical design of new and rehabilitated pavement structures. Appendix OO-1: Background and preliminary smoothness prediction models for flexible pavements. National Cooperative Highway Research Program.
Asakawa, H., Nagayama, T., Fujino, Y., Nishikawa, T., Akimoto, T., & Izumi, K. (2012). Development of a simple pavement diagnostic system using dynamic responses of an ordinary vehicle. Journal of Japan Society of Civil Engineers, Ser. E1 (Pavement Engineering), 68, 20–31. https://doi.org/10.2208/jscejpe.68.20
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Choi, J. H., Adams, T. M., & Bahia, H. U. (2004). Pavement roughness modeling using back-propagation neural networks. Computer-Aided Civil and Infrastructure Engineering, 19, 295–303. https://doi.org/10.1111/j.1467-8667.2004.00356.x
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Douangphachanh, V., & Oneyama, H. (2014). A study on the use of smartphones under realistic settings to estimate road roughness condition. EURASIP Journal on Wireless Communications and Networking, 2014, 114. https://doi.org/10.1186/1687-1499-2014-114
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Garson, D. G. (1991). Interpreting neural network connection weights. AI Expert, 6(4), 46–51.
George, K. P., Rajagopal, A .S., & Lim, L. K. (1989). Models for predicting pavement deterioration. Transportation Research Record, 1215.
Georgiou, P., Plati, C., & Loizos, A. (2018). Soft computing models to predict pavement roughness: A comparative study. Advances in Civil Engineering, 2018, 5939806. https://doi.org/10.1155/2018/5939806
Gharieb, M., & Nishikawa, T. (2021). Development of roughness prediction models for Laos national road network. CivilEng, 2, 158–173. https://doi.org/10.3390/civileng2010009
Goh, A. T. C. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9, 143–151. https://doi.org/10.1016/0954-1810(94)00011-S
Gupta, A., Kumar, P., & Rastogi, R. (2011). Pavement deterioration and maintenance model for low volume roads. International Journal of Pavement Research and Technology, 4, 195–202. https://doi.org/10.6135/ijprt.org.tw/2011.4(4).195
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Hossain, M., Gopisetti, L. S. P., & Miah, M. S. (2020). Artificial neural network modelling to predict international roughness index of rigid pavements. International Journal of Pavement Research and Technology, 13, 229–239. https://doi.org/10.1007/s42947-020-0178-x
Huang, Y., & Moore, R. K. (1997). Roughness level probability prediction using artificial neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1592, 89–97. https://doi.org/10.3141/1592-11
Japan International Cooperation Agency (JICA), & Mitsubishi Research Institute (2013). Information collection and confirmation survey on road and bridge maintenance management (Final report, Summary).
Jokić, A., Grahovac, J., Dodić, J., Dodić, S., Popov, S., & Vucurovic, D. (2011). Interpreting the neural networkfor prediction of fermentation of thick juice from sugar beet processing. Acta Periodica Technologica, 42, 241–249. https://doi.org/10.2298/APT1142241J
Justo-Silva, R., Ferreira, A., & Flintsch, G. (2021). Review on machine learning techniques for developing pavement performance prediction models. Sustainability, 13, 5248. https://doi.org/10.3390/su13095248
Kaloop, M., El-Badawy, S., Ahn, J., Sim, H.-B., Hu, J., & Abd El-Hakim, R. (2020). A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of International Roughness Index of rigid pavements. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2020.1776281
Kırbaş, U., & Karaşahin, M. (2016). Performance models for hot mix asphalt pavements in urban roads. Construction and Building Materials, 116, 281–288. https://doi.org/10.1016/j.conbuildmat.2016.04.118
La Torre, F., Domenichini, L., & Darter, M. I. (1998). Roughness prediction model based on the artificial neural network approach. In Fourth International Conference on Managing Pavements.
Laos Ministry of Public Works and Transport. (2020). Summary of road network statistics year. Laos.
Laos Ministry of Public Works and Transport. (2018). Road design manual. Laos.
Lin, J.-D., Yau, J.-T., & Hsiao, L.-H. (2003). Correlation analysis between international roughness index (IRI) and pavement distress by neural network. In 82nd Annual Meeting of the Transportation Research Board (pp. 12–16), Transportation Research Board, Washington, D.C.
Liu, L. (2013). A methodology for developing performance-related specifications for pavement preservation treatments [Dissertation]. Texas A&M University, Texas, USA.
Makendran, C., Murugasan, R., & Velmurugan, S. (2015). Performance prediction modelling for flexible pavement on low volume roads using multiple linear regression analysis. Journal of Applied Mathematics, 2015, 192485. https://doi.org/10.1155/2015/192485
Mazari, M., & Rodriguez, D. D. (2016). Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. Journal of Traffic and Transportation Engineering (English Edition), 3, 448–455. https://doi.org/10.1016/j.jtte.2016.09.007
Mosa, A. M. (2017). Neural network for flexible pavement maintenance and rehabilitation. Applied Research Journal, 3, 114–129.
Múčka, P. (2017). International Roughness Index specifications around the world. Road Materials and Pavement Design, 18, 929–965. https://doi.org/10.1080/14680629.2016.1197144
Nguyen, H.-L., Pham, B. T., Son, L. H., Thang, N. T., Ly, H.-B., Le, T.-T., Ho, L. S., Le, T.-H., & Tien Bui, D. (2019). Adaptive network based fuzzy inference system with meta-heuristic optimizations for international roughness index prediction. Applied Sciences, 9, 4715. https://doi.org/10.3390/app9214715
Nourani, V., & Sayyah Fard, M. (2012). Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software, 47, 127–146. https://doi.org/10.1016/j.advengsoft.2011.12.014
Obunguta, F., & Matsushima, K. (2020). Optimal pavement management strategy development with a stochastic model and its practical application to Ugandan national roads. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2020.1857759
Odoki, J. B., & Kerali, G. R. H. (2001). Volume four: Analytical framework and model descriptions. Highway Development and Management Model HDM-4 (Version 1.2). World Road Association.
Olowosulu, A. T., Kaura, J. M., Murana, A. A., & Adeke, P. T. (2021). Development of framework for performance prediction of flexible road pavement in Nigeria using Fuzzy logic theory. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2021.1922907
Owusu‐Ababio, S. (2002). Effect of neural network topology on flexible pavement cracking prediction. Computer-Aided Civil and Infrastructure Engineering, 13, 349–355. https://doi.org/10.1111/0885-9507.00113
Pérez-Acebo, H., Gonzalo-Orden, H., Findley, D. J., & Rojí, E. (2021). Modeling the international roughness index performance on semi-rigid pavements in single carriageway roads. Construction and Building Materials, 272, 121665. https://doi.org/10.1016/j.conbuildmat.2020.121665
Pérez-Acebo, H., Linares-Unamunzaga, A., Rojí, E., & Gonzalo-Orden, H. (2020). IRI performance models for flexible pavements in two-lane roads until first maintenance and/or rehabilitation work. Coatings, 10(2), 97. https://doi.org/10.3390/coatings10020097
Pérez-Acebo, H., Mindra, N., Railean, A., & Rojí, E. (2019). Rigid pavement performance models by means of Markov Chains with half-year step time. International Journal of Pavement Engineering, 20, 830–843. https://doi.org/10.1080/10298436.2017.1353390
Sandra, A. K., & Sarkar, A. K. (2013). Development of a model for estimating International Roughness Index from pavement distresses. International Journal of Pavement Engineering, 14, 715–724. https://doi.org/10.1080/10298436.2012.703322
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