A model to compute service life of rural roads using present pavement condition and pavement age

Authors

  • Nautiyal A PhD research scholar, Civil Engineering Department, National Institute of Technology Hamirpur, Himachal Pradesh - 177005(INDIA)
  • Sharma S Assistant Professor, Civil Engineering Department, National Institute of Technology Hamirpur, Himachal Pradesh - 177005 (INDIA)

Keywords:

Pavement distress, international roughness index, linear regression analysis pavement maintenance, remaining service life

Abstract

This paper presents a scientific approach to forecast Remaining Service Life (RSL) of low volume rural roads. A case study of twenty six roads constructed under Pradhan Mantry Gram Sadak Yojna in Hamirpur district of Himachal Pradesh state in India has been selected to implement the approach. Pavement age and Pavement Condition Index (PCI) are the key factors considered to predict RSL of each road. PCI is calculated from the pavement surface distress and roughness data. Bump Integrator is used to calculate the pavement condition based on surface roughness (PCIR)and seven major types of pavement surface distresses namely: alligator cracking, edge cracks, pothole, patching, ravelling, rutting and longitudinal cracks are used in calculating pavement condition index based on distresses (PCID). PCIO is defined as the overall PCI computed from assigning 40% weight to the PCID and 60% to the PCIR. A correlation equation is developed between the pavement age and the PCIO using linear regression analysis: y = -25.62ln(x) + 116.16. However, this equation is valid in conditions similar to those in the study area. A correlation coefficient (r) of 0.8899 was obtained which shows that the curve is a good fit. The developed relationship can help plan repair and maintenance works based on RSL of each road.

References

Al-Suleiman, T. I., & Shiyab, A. M. (2003). Prediction of pavement remaining service life using roughness data: case study in Dubai. International Journal of Pavement Engineering, 4(2), 121-129.

Morse, A. A., and Miller, D.W. (2004). “Pavement Design and Rehabilitation Manual.” The Ohio Department of Transportation, Columbus.

Shah, Y. U., Jain, S. S., Tiwari, D., & Jain, M. K. (2013). Development of overall pavement condition index for urban road network. Procedia-Social and Behavioural Sciences, 104, 332-341.

Tawalare, A., & Raju, K. V. (2016). Pavement Performance Index for Indian rural roads. Perspectives in Science, 8, 447-451.

Setyawan, A., Nainggolan, J., & Budiarto, A. (2015). Predicting the remaining service life of road using pavement condition index, Procedia Engineering, 125, 417-423.

Huang, Y. H. (1993). “Pavement Analysis and Design”, Prentice Hall, New Jersey.

Federal Highway Administration, „Pavement distress identification manual for NPS road inventory system”, Department of transportation, United States, 2006-2009

IRC SP20. 2002. “Rural Roads Manual.” New Delhi India.

Park, K., Thomas, N. E., & Wayne Lee, K. (2007). Applicability of the international roughness index as a predictor of asphalt pavement condition. Journal of Transportation Engineering, 133(12), 706-709.

Dalla Rosa, F., Liu, L., & Gharaibeh, N. G. (2017). IRI Prediction Model for Use in Network-Level Pavement Management Systems. Journal of Transportation Engineering, Part B: Pavements, 143(1), 04017001.

Prasad, J. R., Kanuganti, S., Bhanegaonkar, P. N., Sarkar, A. K., & Arkatkar, S. (2013). Development of relationship between roughness (IRI) and visible surface distresses: a study on PMGSY roads. Procedia-Social and Behavioural Sciences, 104, 322-331.

Mubaraki, M. (2016). Highway subsurface assessment using pavement surface distress and roughness data. International Journal of Pavement Research and Technology, 9(5), 393-402.

Gedafa, D. S., Hossain, M., Miller, R., & Van, T. (2009). Estimation of remaining service life of flexible pavements from surface deflections. Journal of Transportation Engineering, 136(4), 342-352.

Baladi, G. Y., Dawson, T. A., Dean, C. M., Haider, S. W., & Chatti, K. (2011). The theoretical and actual trends of the remaining service life (No. 11-1990).

Witczak, M. W. and Bell, K. R. (1978). “Remaining life analysis of flexible pavements.” Association of Asphalt Paving Technologies Proceeding, Vol. 47, 229-269, Minneapolis, Minnesota

Carson, J. M., and Rose, J. L. (1980). “An ultrasonic non-destructive test procedure for the early detection of fatigue damage and the prediction of remaining life.” Materials Evaluation, 27-34, American Society for Nondestructive Testing, Evanston, Illinois.

McNerney, M. T., McCullough, B. F., Stokoe, K. H., Lee, N., Bay, J. and Wilde, J. (1997) “Prediction of remaining life on airport pavements”, Conference Proceedings: Air-craft/Pavement Technology in the Midst of Change, 77-93, Seattle, Washington.

Dossey, T., Easley, S. and McCullough, B. F. (1996). “Methodology for estimating remaining life of continuously reinforced concrete pavements.”Transportation

Ferregut, C., Abdallah, I., Melchor, O., and Nazarian, S. (1999). “Artificial neural networkbased methodologies for rational assessment of remaining life of existing pavements. Development of a comprehensive, rational method for determining of remaining life of an existing pavement.” Report No.TX-99 1711-1, Texas Department of Transportation, Austin, Texas.

Downloads

Published

2024-02-26

How to Cite

Nautiyal, A., & Sharma, S. (2024). A model to compute service life of rural roads using present pavement condition and pavement age. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(07), 3261–3268. Retrieved from https://ijact.in/index.php/j/article/view/513

Issue

Section

Original Research Article

Similar Articles

<< < 3 4 5 6 7 8 9 10 11 12 > >> 

You may also start an advanced similarity search for this article.