Pavement maintenance planning using a risk-based approach and fault tree analysis

References

• AASHTO, 1993. AASHTO guide for design of pavement structures, 1993. AASHTO, Washington DC.
   Google Scholar
• AASHTO, 2012. Mechanistic-empirical pavement design guide: a manual of practice, 2nd edition. American association of state highway and transportation officials. Washington, USA: American Association of State Highway and Transportation Officials.
   Google Scholar
• Abaza, K.A, 2016. Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. International Journal of Pavement Engineering, 17 (3), 253–264.
   Web of Science ®Google Scholar
• Abaza, K.A, 2017. Empirical approach for estimating the pavement transition probabilities used in non-homogenous Markov chains. International Journal of Pavement Engineering, 18 (2), 128–137.
   Web of Science ®Google Scholar
• Abaza, K.A, 2018a. Optimal empirical-Markovian approach for assessment of potential pavement rehabilitation strategies at the project level. Road Materials and Pavement Design, 19 (3), 646–667.
   Web of Science ®Google Scholar
• Abaza, K.A, 2018b. Empirical-Markovian model for predicting the overlay design thickness for asphalt concrete pavement. Road Materials and Pavement Design, 19 (7), 1617–1635.
   Web of Science ®Google Scholar
• Abbasianjahromi, H., Aghevli, S., and Ravanshadnia, M, 2020. Economic risk assessment of concrete and asphaltic pavements in freeways and highways. Case Studies in Construction Materials, 12, e00346.
   Google Scholar
• Abdelaziz, N., et al., 2020. International Roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering, 21 (1), 88–99.
   Web of Science ®Google Scholar
• Abualigah, L., et al., 2021. The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.
   Web of Science ®Google Scholar
• Albuquerque, F.S., and Núñez, W.P, 2011. Development of roughness prediction models for low-volume road networks in Northeast Brazil. Transportation Research Record: Journal of the Transportation Research Board, 2205 (1), 198–205.
   Google Scholar
• Alimoradi, S., Golroo, A., and Asgharzadeh, S.M, 2020. Development of pavement roughness master curves using Markov chain. International Journal of Pavement Engineering, 23(2), 453–463.
   Web of Science ®Google Scholar
• Ansarilari, Z., and Golroo, A, 2020. Integrated airport pavement management using a hybrid approach of Markov chain and supervised multi-objective genetic algorithms. International Journal of Pavement Engineering, 21 (14), 1864–1873.
   Web of Science ®Google Scholar
• Arba, R., et al., 2019. A monte carlo simulation method for risk management in road pavement maintenance projects. Environmental Engineering and Management Journal, 18 (8), 1639-1646).
   Web of Science ®Google Scholar
• Baadiga, R., et al., 2021. Influence of tensile strength of geogrid and subgrade modulus on layer coefficients of granular bases. Transportation Geotechnics, 29, 100557.
   Web of Science ®Google Scholar
• Chan, W.T., Fwa, T.F., and Tan, C.Y, 1994. Road-maintenance planning using genetic algorithms. I: formulation. Journal of Transportation Engineering, 120 (5), 693–709.
   Google Scholar
• Chou, J.-S, 2011. Cost simulation in an item-based project involving construction engineering and management. International Journal of Project Management, 29 (6), 706–717.
   Web of Science ®Google Scholar
• Corporation, P, 2013. @ risk: Risk analysis and simulation add-in for Microsoft excel.
   Google Scholar
• Denysiuk, R., et al., 2017. Two-Stage multiobjective optimization of maintenance scheduling for pavements. Journal of Infrastructure Systems, 23 (3), 04017001.
   Web of Science ®Google Scholar
• Elhadidy, A.A., Elbeltagi, E.E., and Ammar, M.A, 2015. Optimum analysis of pavement maintenance using multi-objective genetic algorithms. HBRC Journal, 11 (1), 107–113.
   Google Scholar
• Elhadidy, A.A., Elbeltagi, E.E., and El-Badawy, S.M, 2020. Network-based optimization system for pavement maintenance using a probabilistic simulation-based genetic algorithm approach. Journal of Transportation Engineering, Part B: Pavements, 146 (4), 04020069.
   Web of Science ®Google Scholar
• Eskandar, H., et al., 2012. Water cycle algorithm – a novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110–111, 151–166.
   Web of Science ®Google Scholar
• EUROSTAT, 2020. Energy, transport and environment statistics – 2020 edition. Luxembourg: Publications Office of the European Union.
   Google Scholar
• Fani, A., et al., 2022a. Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty. International Journal of Pavement Engineering, 23 (2), 414–424.
   Web of Science ®Google Scholar
• Fani, A., et al., 2022b. A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty. International Journal of Pavement Engineering, 23 (7), 2460–2472.
   Web of Science ®Google Scholar
• Fani, A., et al., 2023. Analysis of the pavement deterioration uncertainty scenarios on pavement maintenance and rehabilitation planning optimization. Structure and Infrastructure Engineering, 1–18.
   Web of Science ®Google Scholar
• Faramarzi, A., et al., 2020. Marine predators algorithm: a nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377.
   Web of Science ®Google Scholar
• France-Mensah, J., and O’Brien, W.J, 2018. Budget allocation models for pavement maintenance and rehabilitation: comparative case study. Journal of Management in Engineering, 34 (2), 05018002.
   Web of Science ®Google Scholar
• García-Segura, T., et al., 2020. Incorporating pavement deterioration uncertainty into pavement management optimization. International Journal of Pavement Engineering, 23(6), 2062–2073.
   Web of Science ®Google Scholar
• Gerami Matin, A., Vatani Nezafat, R., and Golroo, A., 2017. A comparative study on using meta-heuristic algorithms for road maintenance planning: insights from field study in a developing country. Journal of Traffic and Transportation Engineering (English Edition), 4 (5), 477–486.
   Google Scholar
• Gomes Correia, M., et al., 2022. An integer linear programming approach for pavement maintenance and rehabilitation optimization. International Journal of Pavement Engineering, 23 (8), 2710–2727.
   Web of Science ®Google Scholar
• 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, 2672 (52), 205–215.
   Web of Science ®Google Scholar
• Holland, J.H, 1976. Book review. The University of Michigan Press, Philadelphia.
   Google Scholar
• Hossein Alavi, A., and Hossein Gandomi, A., 2011. A robust data mining approach for formulation of geotechnical engineering systems. Engineering Computations, 28 (3), 242–274.
   Web of Science ®Google Scholar
• Hu, J., et al., 2017. Research on comfort and safety threshold of pavement roughness. Transportation Research Record: Journal of the Transportation Research Board, 2641 (1), 149–155.
   Google Scholar
• Jiao, P., et al., 2019. New machine learning prediction models for compressive strength of concrete modified with glass cullet. Engineering Computations, 36 (3), 876–898.
   Web of Science ®Google Scholar
• Justo-Silva, R., Ferreira, A., and Flintsch, G, 2021. Review on machine learning techniques for developing pavement performance prediction models. Sustainability, 13(9), 5248.
   Google Scholar
• Kiihnl, L., and Braham, A, 2021. Exploring the influence of pavement preservation, maintenance, and rehabilitation on Arkansas’ highway network: an education case study. International Journal of Pavement Engineering, 22 (5), 570–581.
   Web of Science ®Google Scholar
• Li, N., Haas, R., and Huot, M, 1998. Integer programming of maintenance and rehabilitation treatments for pavement networks. Transportation Research Record: Journal of the Transportation Research Board, 1629 (1), 242–248.
   Google Scholar
• Majidifard, H., et al., 2019. New machine learning-based prediction models for fracture energy of asphalt mixtures. Measurement, 135, 438–451.
   Web of Science ®Google Scholar
• 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 (5), 448–455.
   Google Scholar
• Meneses, S., and Ferreira, A, 2015. Flexible pavement maintenance programming considering the minimisation of maintenance and rehabilitation costs and the maximisation of the residual value of pavements. International Journal of Pavement Engineering, 16 (7), 571–586.
   Web of Science ®Google Scholar
• Mirjalili, S, 2016. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.
   Web of Science ®Google Scholar
• Moreira, A. V, et al., 2018. An application of Markov chains to predict the evolution of performance indicators based on pavement historical data. International Journal of Pavement Engineering, 19 (10), 937–948.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2020. Designing sustainable concrete mixture by developing a new machine learning technique. Journal of Cleaner Production, 258, 120578.
   Web of Science ®Google Scholar
• 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(8), 2870–2887.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2021b. Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning. International Journal of Pavement Engineering, 23 (13), 4649–4663.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2022b. Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm. Structure and Infrastructure Engineering, 1–13.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2022c. A novel evolutionary learning to prepare sustainable concrete mixtures with supplementary cementitious materials. Environment, Development and Sustainability, 25, 5831–5865.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2022d. A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying whale optimization algorithm and random forest regression. International Journal of Pavement Engineering, 1–13.
   Web of Science ®Google Scholar
• Naseri, H., et al., 2022e. A novel soft computing approach to better predict flexible pavements roughness. SSRN Electronic Journal, 2677 (10), 246-259.
   Google Scholar
• Naseri, H., Fani, A., and Golroo, A, 2022a. Toward equity in large-scale network-level pavement maintenance and rehabilitation scheduling using water cycle and genetic algorithms. International Journal of Pavement Engineering, 23 (4), 1095–1107.
   Web of Science ®Google Scholar
• Nezhadpour Esmaeeli, A., and Heravi, G., 2019. A decision support framework for economic evaluation of flexible strategies in pavement construction projects. International Journal of Pavement Engineering, 20 (11), 1342–1358.
   Web of Science ®Google Scholar
• Pérez-Acebo, H., et al., 2019. Rigid pavement performance models by means of Markov chains with half-year step time. International Journal of Pavement Engineering, 20 (7), 830–843.
   Web of Science ®Google Scholar
• Pérez-Acebo, H., et al., 2020. IRI performance models for flexible pavements in two-lane roads until first maintenance and/or rehabilitation work. Coatings, 10 (2), 97.
   Web of Science ®Google Scholar
• Rose, S., et al., 2018. Risk based probabilistic pavement deterioration prediction models for low volume roads. International Journal of Pavement Engineering, 19 (1), 88–97.
   Web of Science ®Google Scholar
• Salas, M., et al., 2018. Bitumen modified with recycled polyurethane foam for employment in hot mix asphalt. Ingenieria e Investigación, 38 (1), 60-66.
   Web of Science ®Google Scholar
• Sayers, M.W., 1986a. Guidelines for conducting and calibrating road roughness measurements.
   Google Scholar
• Sayers, M.W., 1986b. The international road roughness experiment: establishing correlation and a calibration standard for measurements.
   Google Scholar
• Seyedshohadaie, S.R., Damnjanovic, I., and Butenko, S, 2010. Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks. Transportation Research Part A: Policy and Practice, 44 (4), 236–248.
   Web of Science ®Google Scholar
• Shahin, M.Y., 2005. Pavement management for airports, roads, and parking lots. New York: Springer.
   Google Scholar
• Shokoohi, M., Golroo, A., and Ardeshir, A, 2021. Optimal planning of pavement maintenance and rehabilitation considering pavement deterioration uncertainty. AUT Journal of Civil Engineering, 5 (3), 9.
   Google Scholar
• Si, W., et al., 2014. Reliability-based assessment of deteriorating performance to asphalt pavement under freeze–thaw cycles in cold regions. Construction and Building Materials, 68, 572–579.
   Web of Science ®Google Scholar
• Sidess, A., Ravina, A., and Oged, E, 2022. A model for predicting the deterioration of the international roughness index. International Journal of Pavement Engineering, 23 (5), 1393–1403.
   Web of Science ®Google Scholar
• Sindi, W., and Agbelie, B, 2020. Assignments of pavement treatment options: genetic algorithms versus mixed-integer programming. Journal of Transportation Engineering, Part B: Pavements, 146 (2), 04020008.
   Web of Science ®Google Scholar
• Tayebi, N.R., Nejad, F.M., and Mola, M, 2014. Comparison between GA and PSO in analyzing pavement management activities. Journal of Transportation Engineering, 140 (1), 99–104.
   Google Scholar
• 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 (2), 151–166.
   Web of Science ®Google Scholar
• Uddin, W, 2006. Pavement management systems. The handbook of highway engineering. Taylor \& Francis.
   Google Scholar
• Wen, T., et al., 2022. Automated pavement distress segmentation on asphalt surfaces using a deep learning network. International Journal of Pavement Engineering, 1–14.
   Web of Science ®Google Scholar
• Zhang, Z., et al., 2022. Pavement roughness evaluation method based on the theoretical relationship between acceleration measured by smartphone and IRI. International Journal of Pavement Engineering, 23 (9), 3082–3098.
   Web of Science ®Google Scholar