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With the continuous expansion of highway construction scale, a complex road network can be divided into multiple subnetworks in accordance with any criteria, such as highway class, traffic volume, natural zoning, or administrative subdivision. To realize the targeted maintenance and local–whole optimization of a road network, this study proposes a new type of optimization model for road network maintenance decisions based on the idea of a bi-level programming model. The model fully considers the heterogeneity of a subnetwork, adopts different maintenance standards for targeted maintenance, establishes a whole network maintenance fund allocation model and a subnetwork unit maintenance decision-making (DM) model, and obtains the optimal maintenance scheme through the iterative calculation of a genetic algorithm. Finally, the model is applied to the maintenance DM of a highway network in Zhejiang Province and compared with the traditional DM model without subnetworks. Results show that the model developed in this study can better solve the maintenance DM problem of large-scale complex road networks and achieve the targeted maintenance scheme under limited maintenance funds. Hence, this model can help the sustainable development of road network maintenance. [Display omitted] • Constructed a maintenance decision optimization model for complex road networks. • Subnetwork division based on road network characteristics. • The model enables local-global optimization of road network maintenance. [ABSTRACT FROM AUTHOR]

Sustainable highway pavement maintenance is important for achieving sustainability in the transportation sector. Because the three aspects included in sustainability metrics (environment, economy, and society) often contradict each other, maximising the sustainability performance of highway pavements is difficult, especially at the network level. This study developed a novel multi-objective heuristic algorithm to formulate sustainable highway pavement network maintenance plans considering carbon emissions (CE), life cycle agency cost (LCAC), and pavement long-term performance (LTP). The proposed algorithm is a new variant of multi-objective differential evolution (MODE) that incorporates self-adaptive parameter control and hybrid mutation strategies embedded in its framework (MOSHDE). Three state-of-the-art multi-objective heuristics, namely, the non-dominated sorting genetic algorithm II(NSGA-II), classic MODE, and multi-objective particle swarm optimisation (MOPSO), as well as the proposed MOSHDE, were applied to an existing highway pavement network in China for performance evaluation. Compared with other heuristic algorithms, the proposed self-adaptive parameter control strategy enables the automatic adjustment of the control parameters, avoiding the time-consuming process of selecting them and enhancing the robustness and applicability of differential evolution. The hybrid mutation strategy uses both exploration and exploitation operators for the mutation operations, thus leveraging both global and local searches. The results of the numerical experiment demonstrate that MOSHDE outperforms the other tested heuristics in terms of efficiency and quality and diversity of the obtained approximate Pareto set. The optimal solutions obtained by the proposed method correspond to a proactive maintenance policy, as opposed to the reactive maintenance policy commonly adopted in current practice. In addition, these solutions are more cost-effective and environmentally friendly and can provide better pavement performance to highway users over the project life cycle. Therefore, the proposed MOSHDE may help practitioners in the transportation sector make their highway infrastructure more sustainable. [ABSTRACT FROM AUTHOR]

The single life cycle assessment on advanced vehicle technologies and clean construction supply chains offers the potential for reducing carbon dioxide emissions from vehicle operation and road infrastructure separately. However, the impact of the combination of these two aspects on CO 2 emissions mitigation of road life cycle is less known even though it is critical to the longer-term road transportation decarbonization and long-range policy making for transportation sector, especially under latest worldwide net-zero carbon emission goal. In this study, taking a real project-level asphalt pavement freeway with 15-year life span in China as a case, the amount and hot spots of CO 2 emissions of road transportation were evaluated by considering emissions abatement measures of advanced vehicle technologies and construction supply chains which are theoretically practicable in near-, mid-term and long-term future (from 2020 to 2035). Furthermore, a scenario-based sensitivity analysis on different increasing scope together with various increasing degree of abatement levels over time for these measures is provided. Results demonstrate that the timely implementation of combined decarbonation measures of advanced vehicle technologies and construction supply chains by 2035 could reduce 16.3% of total CO 2 emissions from a road life cycle. In detail, for maintenance activities and use phase, total CO 2 emissions reduce 32% and 16.2% separately. The construction equipment still is the top emissions contributor for maintenance activities. The hybrid electric vehicles (HEV), not battery electric vehicles (EV), replaces conventional internal combustion engine vehicles (ICEV) and becomes the largest source for passenger-related CO 2 emissions from use phase by 2035. Findings are sensitive to assumptions regarding cleaner fuel-produced cement, construction equipment and asphalt, and the market penetration of new energy vehicle. The tight and accelerating pace of primary abatements assist in alleviating the infrastructure carbon lock-in effects caused by maintenance activities with 15% additional CO 2 emissions saving, but not bring about a significant additional reduction (no more than 2.0%) for total road life cycle CO 2 emissions. Current abatement measures are effective but obvious emissions mitigation substitutes of a road is still required, especially innovative road structure and cleaner supply chains inputs as well as deeper decarbonized electrified vehicle. [ABSTRACT FROM AUTHOR]

The functional rural road network has a significant effect on the socio-economic growth of a nation. Newly constructed road pavements deteriorate with time, their regular maintenance will not only keep them functional but also increase their service life. Planning and scheduling of maintenance activities is an important part of any maintenance program for a large network of roads. The present study proposes a maintenance plan for low volume rural roads at network level as well as project level. In the first step of network level planning, the roads in the network are prioritized for pavement maintenance, depending primarily on their pavement condition and traffic volume. In the second step of project level planning, a suitable repair and maintenance technique is selected for each road based on the predominant pavement defects, traffic volume, and climatic conditions. ArcGIS was used to visualize the roads on a map and manage the data collected for each road. The maintenance planning was automated through a tool developed within ArcGIS. Verification of the methodology was done by applying it to a case study of 203 rural roads in the state of Himachal Pradesh in India. The results show that the pavement condition of a road (56.4%) is the most critical factor for prioritizing roads for maintenance. Pavement condition is followed by socio-economic facilities located along the road (26.2%), average daily traffic volume (11.7%), and type of connectivity provided by the road (5.7%). The outcome of the maintenance plan proposed in this study is the priority ranking along with the most suitable repair technique for each road in the network. [Display omitted] • A comprehensive pavement maintenance plan is proposed for low volume rural roads in India. • Both spatial and non-spatial information are maintained within a GIS software ArcGIS. • AHP is used as a multi-criteria decision making tool to establish relative importance of each factor. • Most appropriate maintenance technique is selected step-wise based on engineering considerations. • A total of 203 low volume rural roads in the state of Himachal Pradesh in India were included in the case study. [ABSTRACT FROM AUTHOR]

With growing maintenance demands whereas limited budgets, an integrated methodology unifying life-cycle assessment (LCA), life cycle cost assessment (LCCA) and multi-objective optimization is proposed to establish maintenance decision-making system, considering performance, economic as well as environmental dimensions. Firstly, system scope and unit process of the methodology are redefined considering pavement industry in China. Next, assessment models and parameters are determined to establish the inventory. Then, LCCA is adopted to convert environmental footprint into an economically measurable unit - environmental damage cost (EDC). Lastly, a programming optimization is executed to obtain the optimal maintenance schedule over the whole pavement life cycle. To verify, a case study based on an existing highway in southeast China is conducted, considering ECA-10 thin overly (ECA-10), milling and resurfacing the upper layer (MRU) and hot-in-place rehabilitation (HR) strategies. Results indicate that user cost is the dominant factor whereas environmental impact is relatively negligible. Pavement design life and service life of maintenance alternatives have significant influence on the optimal schedule, and longer pavement design life should be considered when conducting LCA-based programming optimization. With the extension of design life, MRU shows the best long-term benefit. Materials and construction are the main stages potentially improved. Few attempts have been made to introduce LCA into the pavement industry of China, but this model successfully contributes to sustainability goal and scientific decision-making. [ABSTRACT FROM AUTHOR]