• Vol 6, No 5 (2015)
  • Civil Engineering

The Data Mining Applied for the Prediction of Highway Roughness due to Overloaded Trucks

Andri Irfan Rifai, Sigit P. Hadiwardoyo, Antonio Gomes Correia, Paulo Pereira, Paulo Cortez

Cite this article as:

Rifai, A.I., Hadiwardoyo, S.P., Correia, A.G., Pereira, P., Cortez, P., 2015. The Data Mining Applied for the Prediction of Highway Roughness due to Overloaded Trucks. International Journal of Technology. Volume 6(5), pp. 751-761

Andri Irfan Rifai Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI Depok, Depok 16424, Indonesia
Sigit P. Hadiwardoyo Department of Civil Engineering, Faculty of Engineering, Universitas Indonesia, Kampus Baru UI Depok, Depok 16424, Indonesia
Antonio Gomes Correia Institute for Sustainability and Innovation in Structural Engineering, University of Minho, Guimarães, Portugal
Paulo Pereira Centre for Territory, Environment, and Construction, University of Minho, Guimarães, Portugal
Paulo Cortez Centre Algorithmic, University of Minho, Guimarães, Portugal
Email to Corresponding Author


Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and veri?ed using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks.

Data mining, Overload, Pavement maintenance, Pavement roughness, Prediction


Ai, L., Dewen, L., Dong, L., Zeng, S., 2015. Research on Road Environment Construction of Pavement Management System. International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 2015), pp. 38-41

Arhin, A.S., Williams, L.N., Ribbiso, A., Anderson, M.F., 2015. Predicting Pavement Condition Index using International Roughness Index in a Dense Urban Area. Journal of Civil Engineering Research, Volume 5(1), pp. 10-17

Cortez, P., 2010. Data Mining with Neural Networks and Support Vector Machines using the R/rminer Tool. In: Pemer, P. (Ed)., Advances in Data Mining: Applications and Theoretical Aspects. 10th Industrial Conference on Data Mining, Berlin, Germany, LNAI 6171, Springer-Verlag, pp. 572-583

Cortez, P., Embrechts, M.J., 2013. Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models. Information Sciences, Volume 225, pp. 1-17

D’Andrea, A., Cappadona, C., La Rosa, G., Pallegrino, O., 2014. A Functional Road Classification with Data Mining Techniques. Transport, Volume 29(4), pp. 419-430

DGH, 2012. Directorate General Highway Annual Report 2012. Ministry of Public Works

Ede, A.N., 2014. Cumulative Damage Effects of Truck Overloads on Nigerian Road Pavement. International Journal of Civil and Environmental Engineering, IJCEE-IJENS Volume 14(1), pp. 21-26

Hastie, T., Tibshirani, R., Friedman, J., 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York, USA

Haas, R., 2003. Good Technical Foundations are Essential for Successful Pavement Management. In: Proceedings of MAIREPAV 2003, Guimaraes, Portugal

Hadiwardoyo, S.P., Sumabrata, R.J., Berawi, M.A., 2012. Tolerance Limit for Trucks with Excess Load in Transport Regulation in Indonesia. Makara Journal of Technology, Volume 16(1), pp. 85-92

Karim, R.H., Ibrahim, N.I., Saifizul, A.A., Yamanaka, J., 2014. Effectiveness of Vehicle Weight Enforcement in a Developing Country using Weigh-in-motion Sorting System Considering Vehicle By-pass and Enforcement Capability. IATSS Research, Volume 37(2), pp. 124-129

Karlaftis, A.G., Badr A., 2015. Predicting Asphalt Pavement Crack Initiation Following Rehabilitation Treatments. Transportation Research Part C: Emerging Technologies, Volume 55, pp. 510-517

Medury, A., Madanat, S., 2015. Incorporating Network Considerations into Pavement Management Systems. International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, 12-15 July

Pais, J., Amorim, S., Minhoto, M., 2013. Impact of Traffic Overload on Road Pavement Performance. Journal Transportation Engineering, Volume 139(9), pp. 873-879

Rifai, A.I., Hadiwardoyo, S.P., Correia, A.G., Pereira, P., Cortez, P., 2014. Implementasi Data Mining untuk Mendukung Sistem Manajemen Perkerasan Jalan di Indonesia. Konferensi Regional Teknik Jalan Ke-13, Makassar

Rusbintardjo, 2013. The Influence of Overloading Truck to the Road Condition. In: Proceedings of the Eastern Asia Society for Transportation Studies, Volume 9, pp. 1-16

Sayers, M.W., Karamihas, S.M., 1995. The Little Book of Pro?ling. UMTRI, In: Sayers, W. (Ed)., On the Calculation of IRI from Longitudinal Road Pro?le.

Sianipar, C.P.M., Dowaki, K., 2014. Eco-burden in Pavement Maintenance: Effects from Excess Traf?c Growth and Overload. Sustainable Cities and Society, Volume 12, pp. 31-45

Terzi, S., 2006. Modeling the Pavement Present Serviceability Index of Flexible Highway Pavements using Data Mining. Journal of Applied Sciences, Volume 6(1), pp. 193-197

Tinoco, J., Correia, A.G., Cortez, P., 2011. Application of Data Mining Techniques in the Estimation of the Uniaxial Comprehensive Strength of Jet Grouting Columns over Time. Construction and Building Material, Volume 25(3), pp. 1257-1262

Zhou, G., Wang, L., Wang, D., Reichle, S., 2010. Integration of GIS and Data Mining Technology to Enhance the Pavement Management Decision Making. Journal of Transportation Engineering, Volume 136(4), pp. 332-341

Table of Contents