A multi-criteria decision-making analysis for the selection of fibres aimed at reinforcing asphalt concrete mixtures

ABSTRACT

In the last few years, fibers have been proposed as one of the most important additives for the development of reinforced asphalt mixtures. The optimal fiber selection is a very complex task, as an extensive range of criteria and alternatives have to be taken into account. Decision support systems have been applied in the construction sector, but not for selecting fibers for bituminous mixtures. To fill this gap, two Multi-Criteria Decision-Making Analysis methodologies for the selection of the best fiber to be used in Asphalt Concretes are presented in this paper. The Weighted Aggregate Sum Product Assessment (WASPAS) methodology and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with Fuzzy Analytic Hierarchy Process (FAHP) are used to evaluate the effect of various types of fibers on the mechanical performance of bituminous mixtures. Given the uncertainty involved, a stochastic simulation is proposed using the Monte Carlo method. A statistical analysis is carried out to verify the results obtained. Both methods of multi-criteria analysis were effective, with TOPSIS being slightly more conservative in the assignment of performance scores. Synthetic fibers proved to be a suitable option as did fibers with high tensile strength and elastic modulus.

KEYWORDS:

• Asphalt concrete
• fibres
• FAHP
• WASPAS
• TOPSIS
• Monte Carlo

Acknowledgements

This work was possible thanks to the research project entitled ‘Fostering the implementation of fibre-reinforced asphalt mixtures by ensuring its safe, optimized and cost-efficient use’ and financed by the CEDR Transnational Road Research Programme – call 2017 under the contract N. 867481. The authors wish to express their gratitude to experts for their contribution to the research by answering the questionnaires.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Österreichische Forschungsförderungsgesellschaft.