Automating the mixture design of lightweight foamed concrete using multi-objective firefly algorithm and support vector regression

Abstract

Lightweight concrete (LWC) is widely used in the construction industry due to a variety of advantages. However, compared with traditional normal-weight concrete, more influencing variables (e.g. types of lightweight aggregates) must be considered to optimize multiple properties including uniaxial compressive strength (UCS), density and cost. This makes the mixture design of LWC more difficult or sometimes impossible using laboratory experiments. To address this issue, this study proposes a multi-objective optimization (MOO) method using machine learning and metaheuristic approaches for LWC mixture design through a two-step approach. In the first step, a least squares support vector regression (LSSVR) model is constructed to predict multiple properties of LWC. The hyper-parameters of the LSSVR model are tuned using the firefly algorithm (FA). A dataset containing a large number of different mixtures of LWC is compiled from published literature. High prediction accuracy (0.97 for UCS and 0.90 for density) is achieved on the test dataset (including 30% of all the instances). In the second step, a newly developed multi-objective FA (MOFA) model is used to optimize the LWC mixture, while satisfying the constraints. The Pareto fronts of the triple objectives (UCS, cost and density) are successfully obtained. The proposed MOO method is powerful and efficient in finding optimal LWC mixtures with conflicting objectives and therefore decision making can be facilitated in early phases of construction.