International Journal of Transportation Engineering

International Journal of Transportation Engineering

Developing Correlation Between Structural Parameter and Functional Parameter for Pavement Evaluation

Document Type : Research Paper

Authors
Ayesha Ahmad
Zia Ur Rehman
Ammad Hassan Khan
Department of Transportation Engineering and Management, Faculty of Civil Engineering, University of Engineering and Technology, Lahore, Pakistan
10.22119/ijte.2024.415167.1651
Abstract
Pavement functional and structural parameters are important for pavement evaluation. This research aims at developing a prediction model to predict the deflection of flexible pavements using International Roughness Index (IRI) values to optimize maintenance activities without conducting deflection testing. For this purpose, Multan-Lodhran south bound section, a part of N-5 is selected as study area. It starts from km 929+000 and ends at km 867+000, having a length of 62 km, dual carriageway having two lanes each side. The data used for this study had been collected from the database of Road Asset Management Division (RAMD), National Highway Authority, Islamabad, Pakistan on basis of data availability and reliability. Collected data includes International Roughness Index (IRI) values measured by profilometer and deflection data measured by Falling Weight Deflectometer (FWD). Existing pavement structure data was collected from NHA Regional office. Linear and Logarithmic Regression analysis methodology using SPSS Statistical Analysis Software was used to develop correlation between pavement functional and structural parameters for flexible pavement. Also, Microsoft Excel was used for data analysis and validation purpose. The developed regression model showed significant relationship between deflection and IRI. Additionally, AASHTO Back calculation method was also used for determining the effective structure number (SNeff) using deflection results from FWD to determine correlation of IRI with another structural parameter. Considering this, a significant correlation was found between SNeff and IRI. However, it was found that other parameters i.e., subgrade resilient modulus (Mr) and pavement elastic modulous (Ep) did not show any significant relation. The result showed that if pavement IRI values are known, deflection and SNeff can be predicted providing feasibility for transportation agencies that do not have the capabilities as deflection tests are costly and require sophistication for its data handling.
Keywords
Flexible Pavement, International Roughness Index (IRI), Deflection, Falling Weight Deflectometer (FWD), Profilometer
Correlatio
  • AASHTO Guide for Design of Pavement Structure. (1993). American Association of Highway and Transportation Officials.

 

  • Abdelaziz, N., Abd El-Hakim, R., El-Badawy, S., & Afify, A. (2018). IRI prediction model for flexible pavement. International Journal of Pavement Engineering.

 

  • Acebo, H., Unamunzaga, A., Roji, E., & Orden, H. (2020). IRI Performance Models for Flexible Pavements in Two-Lane Roads until First Maintenance and/or Rehabilitation Work. MDPI, Coatings.

 

  • Alharbi, F., & Smadi, O. (2017). ANN model to correlae structural and functional conditions in AC Pavement at netwrok level. International Journal of Advanced Engineering, Management and Science, 919-923.

 

  • Arhin, S., & Noel, E. (2014). Predicting Pavement Condition Index from International Roughness Index in Washington, DC. Washington, DC: Howard University Transportation Research Center (HUTRC).

 

  • Bryce, J., Flintsch, G., Katicha, S., & Diefenderfer, B. (2013). Enhancing Network-Level Decision Making Through the Use of a Structural Capacity Index. Transportation Research Record: Journal of the Transportation Research Board, 64-70.

 

  • Castello, D., Segura, T., Domingo, L., Benlloch, A., & Pellicer, E. (2020). Influence of Pavement Structure, Traffic, and Weather on Urban Flexible Pavement Deterioration.www.mdpi.com/sustainability.

 

  • Dasari, K., Salari, S., Osborn, D. J., & A. Elseifi, M. (2013). Effects of Pavement Conditions on Effective Structural Number of In-Service Pavements. Airfield & Highway Pavement Conference.

 

  • Fakhri, M., & Dezfoulian, R. (2019). Pavement structural evaluation based on roughness and surface distress survey using neural network model. Construction and Building Materials, 768-780.

 

  • Gkyrtis, K., Loizos, A., & Plati, C. (2021). Integrating pavement sensing data for pavement condition evaluation. sensors.

 

  • Hermawan, Suprapto, M., & Setyawan, A. (2016). The use of IRI and SN for Rehabilitation and Maintenance Policy of Local Highway. IOP Publishing. International Conference on Advanced Materials for Better Future.

 

  • Huang, Y. H. (2019). Pavement Analysis and Design. Pearson Education Inc. and Dorling Kindersley Publishing,Inc.

 

  • Joni, H., Hilal, M., & Abed, M. (2020). Developing International Roughness Index (IRI) for visible pavement distresses. Material Science and Engineering.

 

  • Karballaeezadeh, N., Zaremotekhases, F., & Shamshirband, S. (2020). Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems. energies.

 

  • Kavussi, A., Ghorbani, M., Nejad, F., & Ziksari, A. (2017). A new method to determine maintenance and repair activities at network-level pavement management using falling weight deflectometer. Journal of Civil Engineering and Management, 338-346.

 

  • Momin, K., & Hamim, O. (2021). PAVEMENT MANAGEMENT SYSTEM USING DEFLECTION PREDICTION MODEL OF FLEXIBLE PAVEMENTS IN BANGLADESH. Proceedings of the 5th International Conference on Advances in Civil Engineering (ICACE 2020) (pp. 171-175). Chattogram: Imam, Rahman and Pal (eds.).

 

  • Nam, B., An, J., Kim, M., Murphy, M. R., & Zhang, Z. (2015). Improvements to the structural condition index (SCI) for pavement structural evaluation at Network Level. International Journal of Pavement Engineering.

 

  • Rahman, M., & Tarefer, R. (2014). PCI and non-PCI-based pavement evaluation. Journal of Air Transport Management.

 

  • Romanoschi, S., & Metcalf, J. (1999). Simple Approach to Estimation of Pavement Structural Capacity. Journal of Transportation Research Record.

 

  • Rusmanto, U., Syafi, & Handayani, D. (2018). Structural and Functional Prediction of Pavement Condition (A Case Study on South Arterial Road, Yogyakarta). AIP Conference Proceedings. Human-Dedicated Sustainable Product and Process Design: Materials, Resources, and Energy.

 

  • Sollazzo, G., Fwa, T., & Bosurgi, G. (2017). An ANN model to correlate roughness and structural performance in aspahlt pavments. Construction and Building Material, 684-693.

 

  • Talvik, O., & Aavik, A. (2009). Use of FWD Deflection Basin Parameters (SCI, BDI, BCI) for Pavement Condition Assessment. THE BALTIC JOURNAL OF ROAD AND BRIDGE ENGINEERING.

 

  • Thabassum, S. (2015). Correlation between Deflection and Unevenenss Index for Evaluation of Flexible Pavement. International Journal of Transportation Engineering, Vol.2/No.4/Spring , 317-322.

 

  • Vyas, V., Pratap, A., Singh, & Srivastava, A. (2020). Prediction of asphalt pavement condition using FWD deflection basin parameters and artificial neural networks. Road Materials and Paveemnt Design.

 

  • Ziaria, H., Jafar, S., Ayoubinejad, J., & Hartmann, T. (2015). Prediction of IRI in short and long terms for flexible pavement: ANN and GDMH Model. International Journal of Pavement Engineering.
International Journal of Transportation Engineering
Volume 11, Issue 4 - Serial Number 44
Spring 2024
Pages 1667-1677