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Research Article

A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying Whale Optimization Algorithm and random forest regression

, , , , & ORCID Icon
Article: 2147672 | Received 18 Apr 2022, Accepted 09 Nov 2022, Published online: 29 Nov 2022

References

  • Abaza, K.A, 2016. Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. International Journal of Pavement Engineering. doi:10.1080/10298436.2014.993185.
  • Abaza, K.A, 2017. Empirical Markovian-based models for rehabilitated pavement performance used in a life cycle analysis approach. Structure and Infrastructure Engineering. doi:10.1080/15732479.2016.1187180.
  • Abdelaziz, N., et al., 2020. International roughness index prediction model for flexible pavements. International Journal of Pavement Engineering, 21, 88–99. doi:10.1080/10298436.2018.1441414
  • Altarabsheh, A., Altarabsheh, I., and Ventresca, M, 2021. A hybrid genetic algorithm to maintain road networks using reliability theory. Structure and Infrastructure Engineering. doi:10.1080/15732479.2021.1981400.
  • American Association of State Highway and Transportation, (AASHTO), 2012. Pavement management guide (2nd ed). Transportation Research Board.
  • Augeri, M.G., Greco, S., and Nicolosi, V, 2019. Planning urban pavement maintenance by a new interactive multiobjective optimization approach. European Transport Research Review, 11. doi:10.1186/s12544-019-0353-9
  • Bannour, A., et al., 2019. Optimization of the maintenance strategies of roads in Morocco: calibration study of the degradations models of the highway development and management (HDM-4) for flexible pavements. International Journal of Pavement Engineering, 20, 245–254. doi:10.1080/10298436.2017.1293261
  • Barzegaran, J., Shahni Dezfoulian, R., and Fakhri, M, 2021. Estimation of IRI from PASER using ANN based on k-means and fuzzy c-means clustering techniques: a case study. International Journal of Pavement Engineering. doi:10.1080/10298436.2021.2000988.
  • Breiman, L, 2001. Random forests. Machine Learning. doi:10.1023/A:1010933404324.
  • Chan, W.T., Fwa, T.F., and Tan, C.Y, 1994. Road-maintenance planning using genetic algorithms. I: formulation. Journal of Transportation Engineering, 120, 693–709. doi:10.1061/(ASCE)0733-947X(1994)120:5(693)
  • Desai, S., and Ouarda, T.B.M.J, 2021. Regional hydrological frequency analysis at ungauged sites with random forest regression. Journal of Hydrology, 594, 125861. doi:10.1016/j.jhydrol.2020.125861
  • Eurostat, 2020. Energy, transport and environment statistics 2020 edition, printed by Imprimeries Bietlot Freres, Luxembourg, Belgium- statistical books.
  • Fani, A., et al., 2020a. Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty. International Journal of Pavement Engineering, doi:10.1080/10298436.2020.1748628.
  • Fani, A., et al., 2020b. A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty. International Journal of Pavement Engineering, 2460–2472. doi:10.1080/10298436.2020.1859506.
  • Fwa, T.F. 2005. The handbook of highway engineering, CRC Press, Taylor & Francis Group, Boca Raton, FL, USA.
  • George, K.P., 2000a. MDOT pavement management system: prediction models and feedback system.
  • George, K.P., 2000b. MDOT PAVEMENT MANAGEMENT SYSTEM PREDICTION MODELS AND FEEDBACK SYSTEM by The University of Mississippi MDOT Pavement Management System : Prediction Models and MS-DOT-RD-00-119 University of Mississippi Mississippi Department of Transportation.
  • Gharehchopogh, F.S., and Gholizadeh, H, 2019. A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, doi:10.1016/j.swevo.2019.03.004.
  • Gharieb, M., et al., 2022. Modeling of pavement roughness utilizing artificial neural network approach for Laos national road network. Journal of Civil Engineering and Management, 28, 261–277. doi:10.3846/jcem.2022.15851
  • Gharieb, M., and Nishikawa, T, 2021. Development of roughness prediction models for Laos national road network. Civil Engineering, 2, 158–173. doi:10.3390/civileng2010009
  • Ghavami, S., et al., 2021. The impacts of nano-SiO2 and silica fume on cement kiln dust treated soil as a sustainable cement-free stabilizer. Construction and Building Materials, doi:10.1016/j.conbuildmat.2021.122918.
  • Golroo, A., Shokoohi, M., and Ardeshir, A, 2021. Optimum pavement maintenance planning considering pavement deterioration and budget uncertainty. AUT Journal of Civil Engineering. doi:10.22060/AJCE.2021.19960.5753.
  • Gong, H., et al., 2018. Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials. doi:10.1016/j.conbuildmat.2018.09.017.
  • Hafez, M., Ksaibati, K., and Atadero, R.A, 2018. Applying large-scale optimization to evaluate pavement maintenance alternatives for low-volume roads using genetic algorithms. Transportation Research Record: Journal of the Transportation Research Board, doi:10.1177/0361198118781147.
  • Holland, J.H, 1976. Book review. The University of Michigan Press. Ann Arbor, Michigan, USA. doi:10.1111/j.1559-3584.1962.tb04856.x
  • Jaafar, M., and Fahmi, Z., 2016. Asphalt pavement roughness modeling using the Artificial Neural Network and linear regression approaches for LTPP Southern Region. Transp. Res. Board 95th Annu. Meet. (No. 16-4191).
  • Kayadelen, C., et al., 2022. Effects of maintenance, traffic and climate condition on international roughness index of flexible pavement. International Journal of Pavement Engineering, doi:10.1080/10298436.2022.2038382.
  • Khiavi, A.K., and Mohammadi, H, 2018. Multiobjective optimization in pavement management system using NSGA-II method. Journal of Transportation Engineering, Part B: Pavements, 144. doi:10.1061/JPEODX.0000041.
  • Li, Y., et al., 2018. Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy, 232, 197–210. doi:10.1016/j.apenergy.2018.09.182
  • Long-Term Pavement Performance (LTPP), 2021. Dataset. Available from https://infopave.fhwa.dot.gov/ [Accessed 2021].
  • Mathew, B.S., and Isaac, K.P, 2014. Optimisation of maintenance strategy for rural road network using genetic algorithm. International Journal of Pavement Engineering, 15, 352–360. doi:10.1080/10298436.2013.806807
  • Mazari, M., and Rodriguez, D.D, 2016. Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. Journal of Traffic and Transportation Engineering (English Edition), 3, 448–455. doi:10.1016/j.jtte.2016.09.007
  • Mirjalili, S., and Lewis, A, 2016. The Whale Optimization Algorithm. Advances in Engineering Software. doi:10.1016/j.advengsoft.2016.01.008.
  • Mirtabar, Z., et al., 2022. Development of a crowdsourcing-based system for computing the international roughness index. International Journal of Pavement Engineering, 23, 489–498. doi:10.1080/10298436.2020.1755434
  • Mirzahosseini, M., et al., 2019. New machine learning prediction models for compressive strength of concrete modified with glass cullet. Engineering Computations, doi:10.1108/EC-08-2018-0348.
  • Mohammadi, A., Amador-Jimenez, L., and Elsaid, F, 2019. Simplified pavement performance modeling with only two-time series observations: a case study of Montreal island. Journal of Transportation Engineering, Part B: Pavements, 145, 05019004. doi:10.1061/JPEODX.0000138
  • Moreira, A. V., et al., 2017. Coordination of user and agency costs using two-level approach for pavement management optimization. Transportation Research Record: Journal of the Transportation Research Board, 2639, 110–118. doi:10.3141/2639-14
  • Múčka, P, 2017. International roughness index specifications around the world. Road Materials and Pavement Design, 18, 929–965. doi:10.1080/14680629.2016.1197144
  • Naseri, H, 2019. Cost optimization of no-slump concrete using genetic algorithm and particle swarm optimization. International Journal of Innovation, Management and Technology, 10. doi:10.18178/ijimt.2019.10.1.832
  • Naseri, H., et al., 2020b. Designing sustainable concrete mixture by developing a new machine learning technique. Journal of Cleaner Production, 258, 120578. doi:10.1016/j.jclepro.2020.120578
  • Naseri, H., et al., 2021a. Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm. International Journal of Pavement Engineering, 23, 2870–2887. doi:10.1080/10298436.2021.1873331
  • Naseri, H., et al., 2021b. Toward sustainability in optimizing the fly ash concrete mixture ingredients by introducing a new prediction algorithm, environment, development and sustainability. 24(2), 2767–2803. doi:10.1007/s10668-021-01554-2.
  • Naseri, H., et al., 2021c. Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning. International Journal of Pavement Engineering, 1–15. doi:10.1080/10298436.2021.1969019
  • Naseri, H., et al., 2021d. A novel feature selection technique to better predict climate change stage of change. Sustainability, 14, 40. doi:10.3390/su14010040
  • Naseri, H., et al., 2022a. Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm. doi:10.1080/15732479.2022.2095407
  • Naseri, H., et al., 2022b. A novel evolutionary learning to prepare sustainable concrete mixtures with supplementary cementitious materials. Environment, Development and Sustainability, 2022, 1–35. doi:10.1007/S10668-022-02283-W.
  • Naseri, H., Ali, M., and Ghasbeh, E, 2018. Time-cost trade off to compensate delay of project using genetic algorithm and linear programming. International Journal of Innovation, Management and Technology, 9. doi:10.18178/ijimt.2018.9.6.826.
  • Naseri, H., Fani, A., and Golroo, A, 2020a. Toward equity in large-scale network-level pavement maintenance and rehabilitation scheduling using water cycle and genetic algorithms. International Journal of Pavement Engineering, doi:10.1080/10298436.2020.1790558.
  • Naseri, H., Jahanbakhsh, H., and Moghadas Nejad, F, 2019. Developing a novel machine learning method to predict the compressive strength of fly ash concrete in different ages. AUT Journal of Civil Engineering. doi:10.22060/AJCE.2019.16124.5569.
  • Obunguta, F., and Matsushima, K, 2020. Optimal pavement management strategy development with a stochastic model and its practical application to Ugandan national roads. International Journal of Pavement Engineering, doi:10.1080/10298436.2020.1857759.
  • Office of Road Maintenance (ORM), 2022. Roads information [Data set]. Iran’s Road Maint. Transp. Organ. (RMTO), Tehran, Iran, Available from http://www.rmto.ir.
  • Onyango, M., et al., 2018. Analysis of cost effective pavement treatment and budget optimization for arterial roads in the city of Chattanooga. Frontiers of Structural and Civil Engineering, 12, 291–299. doi:10.1007/s11709-017-0419-5
  • Paterson, W.D, 1987. Road deterioration and maintenance effects: Models for planning and management, Transportation Research Part A: General. doi:10.1016/0191-2607(90)90027-4.
  • Pérez-Acebo, H., et al., 2020. IRI performance models for flexible pavements in two-lane roads until first maintenance and/or rehabilitation work. Coatings. doi:10.3390/coatings10020097.
  • Pérez-Acebo, H., et al., 2021. Modeling the international roughness index performance on semi-rigid pavements in single carriageway roads. Construction and Building Materials, 272, 121665. doi:10.1016/j.conbuildmat.2020.121665
  • Pérez-Acebo, H., Bejan, S., and Gonzalo-Orden, H, 2018. Transition probability matrices for flexible pavement deterioration models with half-year cycle time. International Journal of Civil Engineering, 16, 1045–1056. doi:10.1007/s40999-017-0254-z
  • Raheel Shah, S.A., et al., 2018. Saving energy in the transportation sector: an analysis of modified bitumen application based on Marshall test. Energies, 11, 3025. doi:10.3390/en11113025
  • Saha, P., and Ksaibati, K, 2019. Optimization model to determine critical budgets for managing pavement and safety: case study on statewide county roads. Journal of Transportation Engineering, Part A: Systems, 145. doi:. doi:10.1061/JTEPBS.0000218
  • Salas, M.Á., and Pérez-Acebo, H, 2018. Introduction of recycled polyurethane foam in mastic asphalt. Journal of the Croatian Association of Civil Engineers, 70, 403–412. doi:10.14256/JCE.2181.2017
  • Santos, J., Ferreira, A., and Flintsch, G, 2019. An adaptive hybrid genetic algorithm for pavement management. International Journal of Pavement Engineering, 20, 266–286. doi:10.1080/10298436.2017.1293260
  • Sayers, M., Gillespie, T., and Paterson, W., 1986. Guidelines for conducting and calibrating road roughness measurements. World Bank technical paper number 46, World Bank technical paper.
  • Shahin, M.Y, 2005. Pavement management for airports, roads, and parking lots. Springer New York, Collins, NY, USA.
  • Shirzadi Javid, A.A., Naseri, H., and Etebari Ghasbeh, M.A., 2020. Estimating the optimal mixture design of concrete pavements using a numerical method and meta-heuristic algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, doi:10.1007/s40996-020-00352-6.
  • Sollazzo, G., Fwa, T.F., and Bosurgi, G, 2017. An ANN model to correlate roughness and structural performance in asphalt pavements. Construction and Building Materials, 134, 684–693. doi:10.1016/j.conbuildmat.2016.12.186
  • Sun, Y., et al., 2022. Machine-Learning approaches to identify travel modes using smartphone-assisted survey and map application programming interface. Transportation Research Record: Journal of the Transportation Research Board. doi:10.1177/03611981221106483
  • Tsunokawa, K., and Schofer, J.L, 1994. Trend curve optimal control model for highway pavement maintenance: case study and evaluation. Transportation Research Part A: Policy and Practice, 28 (No. 2), 151–166. doi:. doi:10.1016/0965-8564(94)90035-3
  • Xin, J., et al., 2022. Multi-objective optimisation of in-service asphalt pavement maintenance schedule considering system reliability estimated via LSTM neural networks. Structure and Infrastructure Engineering, 1002–1019. doi:10.1080/15732479.2022.2038641
  • Yamany, M.S., Abraham, D.M., and Labi, S, 2021. Comparative analysis of Markovian methodologies for modeling infrastructure system performance. Journal of Infrastructure Systems, 27, 04021003. doi:10.1061/(ASCE)IS.1943-555X.0000604
  • Zhang, Y., Kim, C.W., and Tee, K.F, 2017. Maintenance management of offshore structures using Markov process model with random transition probabilities. Structure and Infrastructure Engineering, 13, 1068–1080. doi:10.1080/15732479.2016.1236393.

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