Multi-Objective Optimization Methods for Designing Low-Carbon Concrete Mixtures

Concrete mixtures are complex material systems with a multitude of characteristics that decision-makers may deem important. These characteristics can include economic, environmental, mechanical, and durability-related properties of a concrete mixture. However, traditional concrete mixture design typically employs long-standing heuristics, which satisfy requirements for physical characteristics but are unable to minimize specific characteristics, such as the cost or carbon footprint of the concrete mixture. This work considers these performance characteristics by implementing simulation-optimization as a new paradigm for designing concrete mixtures. The utility of the simulation-optimization framework is tested for several concrete design case studies that simultaneously consider compressive strength, embodied carbon, service life, and cost. Results from these scenarios demonstrate that the local conditions of the case study dictate the most important parameters of the simulation-optimization (i.e., relative constituent costs, in situ service-life conditions). Out of all other parameters, constituent cost and service-life conditions impact the set of optimal concrete mixture designs in terms of the types and quantities of mixture ingredients that are utilized. We present a simulation-optimization framework that is demonstrated herein to be a holistic design tool that allows designers to quantify and visualize tradeoffs between critical concrete performance metrics. Such a tool can be used to precision-tailor low-carbon concrete mixtures to the exact preferences of the designer.

Automated summary

This study introduces simulation-optimization as a new paradigm for designing concrete mixtures, considering performance characteristics and allowing designers to quantify and visualize tradeoffs between critical concrete performance metrics. The framework demonstrates that local conditions of case studies dictate the most important parameters for optimal concrete mixture designs when considering multiple factors.