Existing performance models developed for interurban pavements are not applicable to urban pavements due to differences in traffic demands and deterioration trends. The objective of the study was to develop performance models for the management of urban pavement networks. Markov chains and Monte Carlo simulation were applied to account for the probabilistic nature of pavements deterioration over time, using data collected in the field. One of the advantages of this methodology is that it can be used by local agencies with scarce technical resources and historical data. Eight performance models were developed and successfully validated for asphalt and concrete pavements in humid, dry and Mediterranean climates with different functional hierarchies. The resulting models evidence the impact of design, traffic demand, climate and construction standards on urban pavements performance. Predicted service life of asphalt and concrete pavements in primary networks are consistent with design standards. However, pavements in secondary and local networks present shorter and longer service life compared to design life, respectively. Climate is a relevant factor for asphalt pavements, where higher deterioration was observed compared to that expected. Opposite to this, no relevant differences between design and performance can be attributed to climate in concrete pavements.

Sustainability, which is founded on the reconciliation of economic, environmental, and social aspects, has become a major issue for infrastructure managers. The economic and environmental impacts of pavement maintenance are not negligible. More than $400 billion are invested globally each year in pavement construction and maintenance. These projects increase the environmental impacts of vehicle operation by 10%. Because maintenance should be technically appropriate, infrastructure managers must integrate technical, economic, and environmental aspects in the evaluation of maintenance alternatives over the life cycle of a pavement. However, these aspects are normally assessed in measurement units that are difficult to combine in the decision-making process. This research examined and compared methods for the integrated consideration of technical, economic, and environmental aspects, and this study aimed to assist highway agencies, researchers, and practitioners with the integration of these aspects for the sustainable management of pavements. For this purpose, a set of maintenance alternatives for asphalt pavements was evaluated. Methods for the integration of these aspects were explored and led to recommendations for the most suitable methods for different scenarios. As a result of this analysis, the analytic hierarchy process (AHP) is recommended when the number of alternatives is small. In these situations, the AHP leads to results that are similar to those of the weighting-sum and multiattribute approaches that are frequently used for intuitive selection. However, when the number of alternatives is large, pair comparison becomes difficult with the AHP and the weighting-sum method becomes more appropriate.

Aged road pavements and insufficient maintenance budgets, along with increasing concerns over the environmental issues related to transportation have introduced additional challenges to highway agencies. Multiobjective optimisation techniques can be used to account for those multiple aspects in the design of maintenance and rehabilitation (M&R) strategies. Contrary to the single-objective optimisation problems where a single solution is optimal, the solution of multiobjective optimisation problems is a set of non-dominated solutions, often referred to as Pareto-optimal set. This set of optimal solutions represents the trade-off between the different and often conflicting objectives, and in many cases is comprised by a vast number of elements. This paper presents the development and application of a fuzzy logic expert system for selecting a single solution from the Pareto set obtained from the multiobjective optimisation of sustainable pavement M&R strategies. It provides decision-makers with an easy and intuitive methodology for the selection of the most preferred solution according to sustainability criteria. The proposed system is applied to a case study from France. Posteriorly, different strategies reflecting the decision-maker’s preferences towards economic and environmental objectives are analysed. Conclusions and recommendations for future improvements are derived from this application.

Insufficient investment in the public sector together with inefficient maintenance infrastructure programs lead to high economic costs in the long term. Thus, infrastructure managers need practical tools to maximize the Long-Term Effectiveness (LTE) of maintenance programs. This paper describes an optimization tool based on a hybrid Greedy Randomized Adaptive Search Procedure (GRASP) considering Threshold Accepting (TA) with relaxed constraints. This tool facilitates the design of optimal maintenance programs subject to budgetary and technical restrictions, exploring the effect of different budgetary scenarios on the overall network condition. The optimization tool is applied to a case study demonstrating its efficiency to ana­lyze real data. Optimized maintenance programs are shown to yield LTE 40% higher than the traditional programs based on a reactive strategy. To extend the results obtained in this case study, a set of simulated scenarios, based on the range of values found in the real example, are also optimized. This analysis concludes that this optimization algorithm enhances the allocation of maintenance funds over the one obtained under a traditional reactive strategy. The sensitivity analysis of a range of budgetary scenarios indicates that the funding level in the early years is a driving factor of the LTE of optimal maintenance programs.