Environmental Research: Infrastructure and Sustainability (Jan 2023)
Multi-criteria comparison tools to evaluate cost- and eco-efficiency of ultra-high-performance concrete
Abstract
This work was motivated by the increasing need for proper metrics and tools to demonstrate the effect of mechanical performance, as a function of concrete mix composition, in dictating the dimensions of structural elements and associated costs and embodied carbon dioxide (CO _2 ) emissions. Mixture compositions associated with different concrete technologies were compared using multi-criteria comparison indices derived using structural design considerations and calculated using information on compressive strength, volumetric embodied CO _2 and unit costs. In addition, predicted compressive strengths obtained with machine learning (ML) models are used to calculate these indices for a domain of mix proportions associated with ultra-high-performance concrete materials to generate multi-objective density diagrams (MODDs). The makeup of this tool facilitates the evaluation of rather complicated trends associated with mix proportions and multi-objective outcomes, allowing ML-based tools to be of easy interpretation by industry personnel with no expertise in artificial intelligence. MODDs could be used as aids in the decision-making process during mix design stages and serve as proof of mixture optimization that could be introduced in environmental product declarations. Results show that, in contrast to conventional wisdom, high-binder content and ultra-high strength concrete technologies are not necessarily detrimental to cost and/or eco efficiencies. For the applications evaluated herein, optimum solutions were mostly obtained with these types of concrete, suggesting that industry trends toward requiring minimization of embodied carbon footprint on a per volume of concrete basis are misguided and should not be used as a standalone metric to minimize the total carbon footprint of concrete structures.
Keywords
