TY - JOUR ID - 4010 TI - Carbon Dioxide Minimization in Large-Scale Pavement Network Maintenance Planning JO - Amirkabir Journal of Civil Engineering JA - CEEJ LA - en SN - 2588-297X AU - Naseri, Hamed AU - Safari Ghalekoli, Elahe AU - Mohamadzade Saliani, Sina AU - Moghadas Nejad, Fereidoon AU - Golroo, Amir AD - Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran. AD - Amirkabir University of Technology, Tehran, Iran AD - Dep. of Civil Engineering, Amirkabir University of Technology AD - Transportation group, Civil dept, AUT Y1 - 2021 PY - 2021 VL - 53 IS - 8 SP - 3187 EP - 3200 KW - Water cycle algorithm KW - Pavement Management System KW - Large-scale networks KW - ENVIRONMENT KW - optimization DO - 10.22060/ceej.2020.17650.6632 N2 - Choosing an appropriate strategy to maintain pavements has become a significant concern. Recently, pavement agencies tackle large-scale networks, which makes the problem complicated. To prevail in this complexity, utilizing metaheuristic algorithms can be an ideal approach. By increasing the dimension of the problem, the competency of metaheuristic algorithms is by far enhanced. To this end, the water cycle algorithm is applied to solve the problem. In this investigation, two models, including single objective optimization and multi-objective optimization, are taken into consideration. In the single-objective model, the minimization of the network International Roughness Index (IRI) is considered as the objective function. In multi-objective optimization modeling, minimization of the network International Roughness Index and embodied CO2 emission are taken into account simultaneously. Furthermore, a new constraint is considered in the model, which leads to restricting the budget fluctuation in different years of the analysis period. A network, including 79 segments, is the case study of this investigation. The results reveal that the water cycle algorithm is highly qualified to solve the pavement maintenance and rehabilitation problem. According to the results, multi-objective optimization reduces the CO2 emission by 47.2% compared with single-objective modeling. Furthermore, the variation of minimum and maximum costs is less than 20% in the planning horizon. UR - https://ceej.aut.ac.ir/article_4010.html L1 - https://ceej.aut.ac.ir/article_4010_af61ae408879d9f5059776b7f7a6190b.pdf ER -