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Automated structural design optimization of steel reinforcement using graph neural network and exploratory genetic algorithms

2022; Elsevier BV; Volume: 146; Linguagem: Inglês

10.1016/j.autcon.2022.104677

1872-7891

Mingkai Li, Yuhan Liu, Billy C.L. Wong, Vincent J.L. Gan, Jack C.P. Cheng,

BIM and Construction Integration

Structural design optimization for steel reinforcement (rebar) is a critical part of reinforced concrete structures. In practice, this process is conducted manually or semi-automatically for each element, which is time-consuming and relies on engineers' knowhow and experience for buildability improvement, material savings and clash resolution. This paper presents an automated pipeline for clash-free rebar design optimization integrating graph neural networks (GNN) and exploratory genetic algorithms (EGA). Graph representation is adopted to characterize rebar layouts in both structural elements and different kinds of reinforced concrete joints. GNN leverages graph representation of rebars to consider the parametric relationship between different rebar groups in a single structural element or among multiple structural elements, enhancing the efficiency of clash-free rebar design optimization. EGA supports design checking and further optimization according to building codes to achieve the optimal design. Compared with conventional optimization methods, the proposed pipeline can automatically identify optimal clash-free rebar design in compliance with code-stipulated requirements while reducing 75%–90% of the computation time, which shows enormous potential for practical use in the industry.