A multi-objective optimisation approach for sustainable pavement management

João Santos, Adelino Ferreira (Corresponding Author), Gerardo Flintsch, Veronique Cerezo

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
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Abstract

Addressing the multidimensional challenges involved in advancing the sustainability of pavement systems requires the development of optimisation-based decision support system (DSS) for pavement management with the capability to identify optimally sustainable pavement maintenance and rehabilitations (M&R) strategies. The main objective of this research work is to develop a multi-objective optimisation framework that hosts a comprehensive and integrated pavement life cycle costs–life cycle assessment model that covers the pavement’s whole life cycle, from the extraction and production of materials to construction and maintenance, transportation of materials, work-zone traffic management, usage and end-of-life. The capability of the proposed DSS is analysed in a case study aiming at investigating, from a full life cycle perspective, the extent to which a number of pavement engineering solutions are efficient in improving the environmental and economic aspects of pavement sustainability, when applied in the management of a road pavement section. Multiple bi-objective optimisation analyses considering accordingly agency costs, user costs and greenhouse gas emissions were conducted based on a multi-objective genetic algorithm. Pareto fronts were obtained for each analysis, originating a set of non-dominated maintenance and rehabilitation solutions. Posteriorly, a multi-criteria decision analysis method was used to find the best compromise solution for pavement management.

Original languageEnglish
Pages (from-to)854-868
Number of pages15
JournalStructure and infrastructure engineering
Volume14
Issue number7
DOIs
Publication statusPublished - 2018
Externally publishedYes

Keywords

  • UT-Hybrid-D
  • Sustainable pavement management
  • Life cycle assessment
  • Life cycle costs
  • Greenhouse gas emissions
  • Multi-objective optimisation
  • Genetic algorithms
  • Maintenance and rehabilitations

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