Reverse design for mixture proportions of recycled brick aggregate concrete using machine learning-based meta-heuristic algorithm: A multi-objective driven study

Construction and Demolition Wastes (CDW) have a significant impact on global waste streams. Brick waste stands out as a prominent type of CDW, and numerous studies have explored its recycling for the creation of environmentally-friendly concrete. Reverse design of recycled brick aggregate concrete (RBAC) mixture proportion is presented in this paper with a focus on four key objectives, that is: compressive strength, cost, and environmental elements (i.e., energy consumption and carbon emission). Based on compiled experimental datasets of 374 samples, the back propagation neural network (BP), random forest (RF), and four meta-heuristic algorithm optimization models were constructed to achieve the desired compressive strength objective. In all machine learning (ML) methods, the compressive strength of RBAC can be predicted with high accuracy, with the SSA-BP (optimized back propagation neural network model using the sparrow search algorithm) model achieving superior results (i.e., NSE=0.91, RPD=3.2). The SSA-BP is therefore used as the objective function for compressive strength. The economic objective is primarily influenced by material costs, and the objective functions of energy consumption and carbon emission are determined by various aspects of production, transportation, and their mixing processes. In order to obtain the optimal RBAC design, the Non-Dominated Sorting Genetic Algorithm (NSGA-III) was implemented considering imperative constraints. Results indicate that cement amount and recycled brick aggregate (RBA)-to-natural aggregate proportion have a positive impact on the compressive strength. The suggested design framework allows for the creation of RBAC composite designs with varying levels of RBA substitution rates and strength targets, providing valuable guidance for tackling the CDW challenge and optimizing RBA usage.