Constructability-Based Multi-Objective Optimization for Reinforcing Bar Design in Rectangular Concrete Beams

Constructability-based optimization design of reinforcing bar (rebar) in concrete structures (RC) has been attracting attention in recent years when aiming towards industrialized sustainable construction. This paradigm enables it to more effectively link the practicality of reinforced concrete designs and their associated material usage and construction cost. The problem itself is multi-objective (MO), and the development of effective optimization algorithmic frameworks to approach its solution is essential. For this purpose, Artificial Intelligence (AI) based optimization with enhanced Meta-heuristic algorithms (MA) has demonstrated to be the key to reduce the computational demand. Particularly for beams, the deployment of Graph Neural Networks (GNN) has proven to be of the most effective AI-based optimization approaches. Nonetheless, its application for these elements has been limited, so far, to single-objective (SO) optimization and not for MO optimization, which entails further considerations to effectively reach optimal Pareto Fronts (PF) in a time-efficient manner. Additionally, the lack of constructability metrics, at this point, for rebar design in RC structures, in the literature, is still evident. Even though some efforts have been made in the last years, for some types of elements, there is still a gap when it comes to elaborate and flexible constructability models that may be used in general, for any project at hand. This work presents the development of a novel MO optimization framework with GNN-Enhanced Metaheuristics (MA), for rebar design in multi-span beams. For this purpose, the development of a constructability score (CS) model is proposed based on rebar cuts and labor assembling complexity. The Non-Sorting Genetic Algorithm II (NSGA-II) is used for enhancement. The performance of each algorithm is analyzed and compared between Non-Enhanced and GNN, in terms of convergence and time efficiency.