Optimal Planning of Pavement Maintenance and Rehabilitation Considering Pavement Deterioration Uncertainty

Document Type : Research Article

Authors

1 Department of civil and environmental engineering, Amirkabir university of technology, Iran

2 Civil Engineering Department, Amir Kabir University of Technology

3 Department of Civil Engineering, Amirkabir University of Technology

Abstract

Optimization of pavement maintenance and rehabilitation (M&R) is one of the most substantial parts of the pavement management system (PMS). Highway agencies should plan M&R treatments efficiently. An accurate pavement performance model is required to predict pavements’ future conditions. Thus, an accurate international roughness index (IRI) model was developed to predict IRI. Moreover, M&R was scheduled deterministically in many past studies, but this issue does not match the uncertain essence of deterioration and future M&R expenditures. Hence, the uncertainty associated with pavement deterioration and budget calculation should be considered in scheduling M&R activities. This study scheduled M&R activities deterministically and probabilistically to compare the solutions obtained from both approaches. The uncertainty of several features in the IRI model and budget calculation was not considered in the deterministic approach. Furthermore, in the probabilistic approach, historical data were employed to fit the distribution function for uncertain features in the model. Then, Monte Carlo simulation and optimizer were run to generate probability distributions for sections’ IRI and required budget and optimize M&R scheduling. The IRI model was developed using 288 data. The testing data R-square of the model was 0.917. As a case study, the research results were applied to a network, including five sections during a 5-year-planning. Additionally, the costs of M&R scheduling in the deterministic and probabilistic approaches were $52,149 and $40,195. Hence, the cost of the deterministic approach was 29.7% higher than the probabilistic approach. Besides, the probabilistic method applied more preventive maintenance, desirable for users, than the deterministic one.

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Main Subjects


  1. Faturechi, E. Miller-Hooks, Measuring the Performance of Transportation Infrastructure Systems in Disasters: A Comprehensive Review, Journal of Infrastructure Systems, 21(1) (2015) 04014025.
  2. Saha, K. Ksaibati, A risk-based optimization methodology for pavement management system of county roads, International Journal of Pavement Engineering, 17(10) (2016) 913-923.
  3. R. Seyedshohadaie, I. Damnjanovic, S. Butenko, Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks, Transportation Research Part A: Policy and Practice, 44(4) (2010) 236-248.
  4. Fani, H. Naseri, A. Golroo, S.A. Mirhassani, A.H. Gandomi, A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty, International Journal of Pavement Engineering, (2020) 1-13.
  5. ASCE (American Society of Civil Engineers), Infrastructure report card-Roads., (2017).
  6. Y. Shahin, Pavement management for airports, roads, and parking lots, 1994.
  7. Naseri, A. Fani, A. Golroo, Toward equity in large-scale network-level pavement maintenance and rehabilitation scheduling using water cycle and genetic algorithms, International Journal of Pavement Engineering, (2020) 1-13.
  8. K. Sandra, A.K. Sarkar, Development of a model for estimating International Roughness Index from pavement distresses, International Journal of Pavement Engineering, 14(8) (2013) 715-724.
  9. G. Kerali, J.B. Odoki, E.E. Stannard, Overview of HDM-4, The highway development and management series, 4 (2000).
  10. Mazari, D.D. Rodriguez, Prediction of pavement roughness using a hybrid gene expression programming-neural network technique, Journal of Traffic and Transportation Engineering (English Edition), 3(5) (2016) 448-455.
  11. Firoozi Yeganeh, A. Mahmoudzadeh, M.A. Azizpour, A. Golroo, Validation of Smartphone-Based Pavement Roughness Measures, AUT Journal of Civil Engineering, 1(2) (2017) 135-144.
  12. Li, M. Huot, R. Haas, Cost-effectiveness-based priority programming of standardized pavement maintenance, Transportation research record, 1592(1) (1997) 8-16.
  13. Abdelaziz, R.T. Abd El-Hakim, S.M. El-Badawy, H.A. Afify, International Roughness Index prediction model for flexible pavements, International Journal of Pavement Engineering, 21(1) (2020) 88-99.
  14. H. Choi, T.M. Adams, H.U. Bahia, Pavement Roughness Modeling Using Back‐Propagation Neural Networks, Computer‐Aided Civil and Infrastructure Engineering, 19(4) (2004) 295-303.
  15. Marcelino, M. de Lurdes Antunes, E. Fortunato, M.C. Gomes, Machine learning approach for pavement performance prediction, International Journal of Pavement Engineering, 22(3) (2021) 341-354.
  16. Gong, Y. Sun, X. Shu, B. Huang, Use of random forests regression for predicting IRI of asphalt pavements, Construction and Building Materials, 189 (2018) 890-897.
  17. Ziari, M. Maghrebi, J. Ayoubinejad, S.T. Waller, Prediction of pavement performance: application of support vector regression with different kernels, Transportation Research Record, 2589(1) (2016) 135-145.
  18. Fani, A. Golroo, S. Ali Mirhassani, A.H. Gandomi, Pavement maintenance and rehabilitation planning optimization under budget and pavement deterioration uncertainty, International Journal of Pavement Engineering, (2020) 1-11.
  19. Zhang, L. Gao, A nested modeling approach to infrastructure performance characterization, International Journal of Pavement Engineering, 19(2) (2018) 174-180.
  20. Rose, B.S. Mathew, K.P. Isaac, A. Abhaya, Risk based probabilistic pavement deterioration prediction models for low volume roads, International Journal of Pavement Engineering, 19(1) (2018) 88-97.
  21. Hong, S. Wang, Stochastic modeling of pavement performance, International Journal of Pavement Engineering, 4(4) (2003) 235-243.
  22. O. Owolabi, O.M. Sadiq, O.S. Abiola, Development of performance models for a typical flexible road pavement in Nigeria, International Journal for Traffic and Transport Engineering, 2(3) (2012) 178-184.
  23. Hossain, L. Gopisetti, M. Miah, International roughness index prediction of flexible pavements using neural networks, Journal of Transportation Engineering, Part B: Pavements, 145(1) (2019) 04018058.
  24. George, MDOT pavement management system: prediction models and feedback system, Mississippi. Dept. of Transportation, 2000.
  25. S. Albuquerque, W.P. Núñez, Development of roughness prediction models for low-volume road networks in northeast Brazil, Transportation research record, 2205(1) (2011) 198-205.
  26. F.B. Mohamed Jaafar, W. Uddin, Y. Najjar, Asphalt Pavement Roughness Modeling Using the Artificial Neural Network and Linear Regression Approaches for LTPP Southern Region, 2016.
  27. Dalla Rosa, L. Liu, N.G. Gharaibeh, IRI prediction model for use in network-level pavement management systems, Journal of Transportation Engineering, Part B: Pavements, 143(1) (2017) 04017001.
  28. Pérez-Acebo, A. Linares-Unamunzaga, E. Rojí, H. Gonzalo-Orden, IRI performance models for flexible pavements in two-lane roads until first maintenance and/or rehabilitation work, Coatings, 10(2) (2020) 97.
  29. Pérez-Acebo, N. Mindra, A. Railean, E. Rojí, Rigid pavement performance models by means of Markov Chains with half-year step time, International Journal of Pavement Engineering, 20(7) (2019) 830-843.
  30. Alimoradi, A. Golroo, S.M. Asgharzadeh, Development of pavement roughness master curves using Markov Chain, International Journal of Pavement Engineering, (2020) 1-11.
  31. Wang, Z. Zhang, R.B. Machemehl, Decision-making problem for managing pavement maintenance and rehabilitation projects, Transportation Research Record, 1853(1) (2003) 21-28.
  32. Chakroborty, P.K. Agarwal, A. Das, Comprehensive pavement maintenance strategies for road networks through optimal allocation of resources, Transportation Planning and Technology, 35(3) (2012) 317-339.
  33. Naseri, M. Ehsani, A. Golroo, F. Moghadas Nejad, Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimization algorithm, International Journal of Pavement Engineering, (2021) 1-18.
  34. A. Elhadidy, E.E. Elbeltagi, S.M. El-Badawy, Network-Based Optimization System for Pavement Maintenance Using a Probabilistic Simulation-Based Genetic Algorithm Approach, Journal of Transportation Engineering, Part B: Pavements, 146(4) (2020) 04020069.
  35. Hafez, K. Ksaibati, R.A. Atadero, Applying large-scale optimization to evaluate pavement maintenance alternatives for low-volume roads using genetic algorithms, Transportation Research Record, 2672(52) (2018) 205-215.
  36. Khavandi Khiavi, H. Mohammadi, Multiobjective optimization in pavement management system using NSGA-II method, Journal of Transportation Engineering, Part B: Pavements, 144(2) (2018) 04018016.
  37. Naseri, M. Shokoohi, H. Jahanbakhsh, A. Golroo, A.H. Gandomi, Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning, International Journal of Pavement Engineering, (2021) 1-15.
  38. J. Khattak, M.A. Nur, M.R.-U.-K. Bhuyan, K. Gaspard, International roughness index models for HMA overlay treatment of flexible and composite pavements, International Journal of Pavement Engineering, 15(4) (2014) 334-344.
  39. Naseri, H. Jahanbakhsh, F. Moghadas Nejad, A. Golroo, Developing a novel machine learning method to predict the compressive strength of fly ash concrete in different ages, AUT Journal of Civil Engineering, 4(4) (2020) 3-3.
  40. H. Alavi, A.H. Gandomi, Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing, Computers & Structures, 89(23-24) (2011) 2176-2194.
  41. Wu, C. Yuan, H. Liu, A risk-based optimization for pavement preventative maintenance with probabilistic LCCA: a Chinese case, International Journal of Pavement Engineering, 18(1) (2017) 11-25.
  42. A. McCarl, A. Meeraus, P. van der Eijk, M. Bussieck, S. Dirkse, P. Steacy, F. Nelissen, McCarl GAMS user guide, GAMS Development Corporation, (2014).
  43. Corporation, Guide to using@ RISK, version 5.7, in, Palisade Corporation Ithaca, 2010.
  44. Hu, X. Gao, R. Wang, S. Sun, Research on comfort and safety threshold of pavement roughness, Transportation Research Record, 2641(1) (2017) 149-155.
  45. Kiihnl, A. Braham, Exploring the influence of pavement preservation, maintenance, and rehabilitation on Arkansas’ highway network: an education case study, International Journal of Pavement Engineering, 22(5) (2021) 570-581.
  46. R. Rada, R.W. Perera, V.C. Prabhakar, L.J. Wiser, Relating ride quality and structural adequacy for pavement rehabilitation and management decisions, Transportation research record, 2304(1) (2012) 28-36.
  47. A. Pires, R.A. Perdigão, Non-Gaussianity and asymmetry of the winter monthly precipitation estimation from the NAO, Monthly weather review, 135(2) (2007) 430-448.