Pareto frontier for steel-reinforced concrete beam developed based on ANN-based Hong-Lagrange algorithm

Abstract

ABSTRACT Multi-objective optimization (MOO) is always a challenging issue for engineers in the field of structural engineering, where several objective functions must be satisfied under equality and inequality constraints to meet requirements imposed by engineers and decision-makers. This study proposes a novel approach to solve MOO problems for steel-reinforced concrete (SRC) beams using an artificial neural network (ANN)-based Hong-Lagrange algorithm. Proposed method in this paper optimizes three specific objective functions, including cost (CIb), CO2 emissions, and beam weight (W), simultaneously. Neural networks are trained by 200,000 samples, which are randomly generated by structural mechanics-based calculations, to derive three specific objective functions. Unified objective function is, then, proposed based on weight fractions of each objective function. An ANN-based Hong-Lagrange technique identifies optimal design parameters within the bounds constrained by 16 inequalities against external loads. The proposed method yields a set of optimal results, creating a Pareto frontier that optimizes multiple objectives. Pareto frontier using an ANN-based Hong-Lagrange algorithm is well compared with the lower boundary of large datasets of random designs which include 133,711 samples obtained by structural mechanics. A cost of an SRC beam is obtained as 219,279.1 KRW/m by an ANN-based Hong-Lagrange algorithm with an error of −0.14% verified by structural mechanics.