Pavement Roughness Prediction Systems: A Bump Integrator Approach

Authors: Manish Pal, Rumi Sutradhar

Abstract:

Pavement surface unevenness plays a pivotal role on roughness index of road which affects on riding comfort ability. Comfort ability refers to the degree of protection offered to vehicle occupants from uneven elements in the road surface. So, it is preferable to have a lower roughness index value for a better riding quality of road users. Roughness is generally defined as an expression of irregularities in the pavement surface which can be measured using different equipments like MERLIN, Bump integrator, Profilometer etc. Among them Bump Integrator is quite simple and less time consuming in case of long road sections. A case study is conducted on low volume roads in West District in Tripura to determine roughness index (RI) using Bump Integrator at the standard speed of 32 km/h. But it becomes too tough to maintain the requisite standard speed throughout the road section. The speed of Bump Integrator (BI) has to lower or higher in some distinctive situations. So, it becomes necessary to convert these roughness index values of other speeds to the standard speed of 32 km/h. This paper highlights on that roughness index conversional model. Using SPSS (Statistical Package of Social Sciences) software a generalized equation is derived among the RI value at standard speed of 32 km/h and RI value at other speed conditions.

Keywords: Bump Integrator, Pavement Distresses, Roughness Index, SPSS.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097275

References:

[1] Rashid M. M. and Koji Tsunokawa (2008), “Potential Bias of Response Type Road Roughness Measuring Systems: Causes and Remedial Measures”, The Open Transportation Journal, Volume 2, pp. 65-73.
[2] Sandra A. K., Sarkar A. K. and Rao V. R. V., (2008), “Estimating Pavement Roughness at Different Operating Speeds”, Highway Research Journal, pp. 1-8.
[3] Kyungwon Park, Natacha E. Thomas, and K. Wayne Lee, (2007), “Applicability of the International Roughness Index as a Predictor of Asphalt Pavement Condition”, Journal of Transportation Engineering, ASCE Vol.133, No. 12, pp. 706-709.
[4] Jian C. S. and Chung H. C. (2012), “Development of Pavement Smoothness Index Relationship”, Journal of ASTM International, Volume 3, No. 8, pp. 48-55.
[5] Badashah S. J. and Subbaiah P., (2012), “Surface Roughness Prediction with Denoising using Wavelet Filter”, International Journal of Advances in Engineering & Technology, ISSN: 2231-1963, Vol. 3, Issue 2, pp. 168-177.
[6] Syed Jahangir Badashah1 and P.Subbaiah, “Surface Roughness Prediction with Denoising using Wavelet Filter”, International Journal of Advances in Engineering & Technology, ISSN: 2231-1963, Vol. 3, Issue 2, pp. 168-177, 2012.
[7] Raju C S Bhagavan, Kumar Dr. M Anjan and Raju Dr. G V R Prsasada (2012), “Performance Study of Flexible Pavements on Non Expansive Soils”, Civil and Environmental Research ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol. 2, No.8, pp. 11-23.
[8] Rokade S, Agarwal P K and Shrivastava (2010), “Study on Performance of Flexible Highway Pavements”, International Journal of Advanced Engineering Technology E-ISSN 0976-3945, Vol. I, pp. 312-338.
[9] Mustafa Birkan Bayrak, Egemen Teomete and Manish Agarwal, “Use of Artificial Neural Networks for Predicting Rigid Pavement Roughness”, Fall Student Conference, Ames, Iowa, 2004.
[10] Hamid Behbahani, and S. Mohammad Elahi, “An Investigation to Determine the Minimum Acceptable Roadway Condition for Iran’s Highways”, International Journal of Civil Engineering. Vol.4, No.1, pp. 64-76, 2006.
[11] Mehmet Saltan and Serdal Terzi, “Comparative Analysis of Using Artificial Neural Networks (ANN) and Gene expression Programming (GEP) in Back Calculation of Pavement Layer Thickness”, Indian Journal of Engineering and Material Science, Vol-12, pp-42-50, 2005.
[12] Jyh-Dong Lin, Jyh-Tyng Yau and Liang-Hao Hsiao, “Correlation Analysis Between International Roughness Index (IRI) and Pavement Distress by Neural Network”, Paper Publication at the 82th Annual Meeting of the Transportation Research Board, Washington, D.C, 2003.
[13] Nii O. Attoh-Okine, “Predicting Roughness Progression in Flexible Pavements Using Artificial Neural Networks”, 3rd International Conference on Managing Pavements, pp-55-62, 1994.
[14] Kasthurirangan Gopalakrishnan, “Effect of training algorithms on neural networks aided pavement diagnosis”, International Journal of Engineering, Science and Technology, Volume 2, No. 2, pp-83-92, 2010.
[15] Zhenyu Lou, Jian John Lu and Manjriker Gunaratne, “Road Surface Crack Condition Forecasting using Neural Network Models”, a project sponsored by Florida Department of Transportation, 1999.