Implementation of a multicriteria analytical framework for the sustainability evaluation and comparison of concrete materials considering methodological uncertainties

Multicriteria analysis (MCA) is one practical tool to support sustainability decision-making processes considering numerous evaluation criteria. Its conventional analytical stages include the selection of indicators, normalization, weighting, and aggregation. The decisions based on conventional MCA stages, however, could be subject to criticism and bias because each analytical stage can be performed in a multiple number of ways, creating methodological uncertainties which ultimately lead to uncertainty in the MCA output. This paper tackles how to address methodological uncertainties objectively through a practical implementation of an MCA framework for sustainability evaluation under uncertainty. The implementation is demonstrated by a concrete material selection problem, wherein the sustainability performance of different ready-mix concretes are compared, and the most sustainable alternative is identified. The unique characteristics of the framework are the use of uncertainty and sensitivity analyses, which transform the results to a probabilistic form, and provide quantitative measures for the objective management of methodological uncertainties. Due to the stochastic nature of the result, a probabilistic tool to hierarchically order the concrete mixes according to their sustainability performance is also developed. This facilities the identification of the most sustainable alternative concrete mix while also considering the level of uncertainty associated with that result, thus highlighting the applicability of the framework to support sustainable decision-making under methodological uncertainties.

Automated summary

This paper introduces multicriteria analysis (MCA) as a practical tool for supporting sustainable decision-making processes. The traditional stages of MCA can lead to uncertainty, which is addressed through uncertainty and sensitivity analyses. Through a concrete material selection problem, the paper demonstrates how to objectively manage methodological uncertainties in MCA.