Machine learning-based mix design tools to minimize carbon footprint and cost of UHPC. Part 2: Cost and eco-efficiency density diagrams

Abstract

The emergence of ultra-high-performance concrete (UHPC) as an attractive solution for precast and prestressed applications has coincided with global efforts towards sustainable construction. The increasing need for tools capable of intuitively demonstrating the effect of concrete mixture composition on mechanical performance, cost and eco-efficiency concurrently has motivated this work in an effort to promote design of more sustainable solutions to help meet environmental goals. Predicted compressive strengths from “Part 1” of this study, obtained with machine learning (ML) models, are coupled with volumetric environmental factors and unit costs to generate cost- and eco-efficiency density diagrams. The makeup of these tools facilitates the evaluation of rather complicated trends associated with mix proportions and multi-objective outcomes, allowing ML-based tools to be of easy use by industry personnel on a daily basis, while serving as decision-making aids during mix design stages and proof of mixture optimization that could be introduced in Environmental Product Declarations. Mixtures were identified using these diagrams and compared using cost- and eco-efficiency indices. Results show that high paste content, high strength (and ultra-high strength) concrete technologies are not necessarily detrimental to cost or eco efficiencies. Optimum solutions were mostly obtained with these types of concrete, which means that industry trends toward requiring minimization of embodied CO2 in concrete on a per volume basis are misguided and do not minimize the embodied CO2 in concrete structures.