A fuzzy stochastic multi-criteria model for the selection of urban pervious pavements

• Sustainable urban drainage systems are techniques that involve significant impacts on each of the pillars of sustainability.
• A multi-criteria approach is presented to assist the selection procedure of pervious pavements.
• Monte Carlo methods are introduced to stochastically model the lack of precise information.
• The criteria are weighted by processing the judgments provided by ten experts in water management.

Multi-criteria decision making methods (MCDM) have been widely used throughout the last years to assist project contractors in selection processes related to the construction field. Sustainable urban drainage systems (SUDS) are an especially suitable discipline to implement these techniques, since they involve important impacts on each branch of sustainability: economy, environment and society. Considering that pervious pavements constitute an efficient solution to manage urban stormwater runoff as a source control system, this paper presents a multi-criteria approach based on the Integrated Value Model for Sustainable Assessments (MIVES) method to facilitate their proper selection. Given the lack of accurate information to shape the behavior of the alternatives regarding some of the criteria defining the decision-making environment, a series of variables are modeled by executing stochastic simulations based on the Monte Carlo methods. Additionally, a group of ten experts from various sectors related to water management was requested to provide their opinions about the importance of the set of selected criteria, according to the comparison levels of the Analytic Hierarchy Process (AHP). These judgments are converted into triangular fuzzy numbers, in order to capture the vagueness that human attitude entails when making judgments. A case of study in which the three major types of pervious pavements (porous asphalt, porous concrete and interlocking concrete pavers) are evaluated is presented to demonstrate the potential of the model.