An AI-based auto-design for optimizing RC frames using the ANN-based Hong–Lagrange algorithm

Abstract

Artificial neural networks (ANNs)-based objective functions such as costs and weights of reinforced concrete (RC) frames with four-by-four bays and four floors are optimized simultaneously based on big datasets of 330,000 designs according to ACI 318-19, whereas corresponding design parameters, which minimize objective functions, are also obtained. The Pareto frontier verified by big datasets shows reductions up to 44.983% and 33.111% in costs and weights, respectively, compared with probable designs based on averages of 688 (0.1%) best designs among 688,000 samples. Optimized designs meeting requirements imposed by codes and architects are achieved using the ANN-based Hong–Lagrange algorithm in which complex analytical objective functions are replaced by ANN-based objective functions. ANN is formulated to provide 32 forward outputs based on 18 forward inputs to minimize or maximize objective functions, such as costs and weights as a function of 18 input parameters. When good training qualities are achieved, objective functions with equality and inequality constraints are implemented in the proposed method, which determines optimal design parameters for building with accuracies and robustness equivalent to derivation-based approaches, which are hard to obtain using metaheuristic methods. The proposed AI-based auto-designs perform optimization where design variables are produced automatically while optimizing design targets.

Keywords

• ann-based hong–lagrange algorithm of rc frames
• pareto frontier of rc frames
• big datasets of rc frames
• kkt solutions of rc frames
• weight fraction of moo problems