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The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model's effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model. [ABSTRACT FROM AUTHOR]

This article presents a novel system reliability-based framework for multi-objective optimisation of preventive maintenance (PM) management of in-service asphalt pavement. To accurately predict the international roughness index (IRI) sequence of pavement sections, a long short-term memory (LSTM) neural network that considers the spatiotemporal correlations between IRI sequences is trained with data retrieved from the long-term pavement performance (LTPP) program. Based on time-dependent limit-state functions (LSFs) incorporating the uncertainty associated with LSTM neural network prediction and the observational error involved in IRI measurement, Monte Carlo simulation (MCS) with importance sampling (IS) is adopted to calculate the reliability of pavement sections. Pavement sections located in New Mexico and Montana are selected as illustrative examples. Tri-objective optimisation processes are investigated by maximising user benefits (i.e. improved system reliability) and agency benefits (i.e. extended service life) while minimising the associated life-cycle cost (LCC) (i.e. user and agency costs) with multi-objective genetic algorithms (GAs). The obtained Pareto solution sets may assist decision-makers in the selection of well-balanced solutions to identify the optimal timing for applying PM treatments to in-service asphalt pavement. [ABSTRACT FROM AUTHOR]

Existing bridge performance metrics are limited in their ability to objectively reflect the safety and serviceability of bridges and identify effective Maintenance, Repair and Replacement (MR&R) actions for a large number of bridges in the face of budget constraints. This study presents a comprehensive optimal budget allocation framework with an integer linear programing formulation for a portfolio of bridges based on element-level inspection data. Considering budget constraints, this method determines optimal MR&R actions at element-level such that agency and user costs of repair actions required to have bridges in their like-new state at the next budget allocation year are minimized. The framework incorporates structural safety risks via the probability that the designated structural functionality of a bridge is or deemed to be compromised. The approach is applied to 484 National Highway Systems bridges in district 3 of Ohio. Results indicate that the proposed method can identify work plans comprising the optimal set of MR&R actions that maximize the network-level performance of bridge portfolios, while satisfying budgetary constraints. It is observed that this method consistently assigns higher priority to work plans that reduce structural safety risks of bridges, and to bridges with high traffic demands and long detour lengths. [ABSTRACT FROM AUTHOR]