Pavement Roughness Prediction Systems: A Bump Integrator Approach
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.
[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.
[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.
@article{"International Journal of Architectural, Civil and Construction Sciences:68480", author = "Manish Pal and Rumi Sutradhar", title = "Pavement Roughness Prediction Systems: A Bump Integrator Approach", 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.", volume = "8", number = "12", pages = "1258-4", }
