Knowledge‐guided generative adversarial networks for intelligent design of internal supporting structures in foundation pits

This study presents SupportGAN, a knowledge‐guided framework based on a two‐stage generative adversarial network, for preliminary conceptual plan‐view layout of corner‐ and cross‐supporting structures in foundation pits. Design drawings of support structures collected from authoritative design institutes were semantically processed and expanded through a structural knowledge‐guided augmentation (SKGA) approach. The SupportGAN model was then trained with multiple hyperparameter configurations to achieve optimal performance. Additionally, SupportGAN was evaluated and compared with two mainstream GAN models (pix2pix and pix2pixHD), demonstrating its superior capabilities. The design results generated by SupportGAN were evaluated using visual assessment and quantitative metrics. A feasibility assessment was also performed to confirm the economic and mechanical viability of the generated layouts: A pixel‐count proxy showed a 6.90% material gap versus engineer designs, and results of the finite element (FE) analysis on two cases indicated comparable structural performance (force difference ). The results indicate that SupportGAN's outputs exhibit significant similarities to those of expert engineers across various aspects, demonstrating its potential to aid designers in the preliminary conceptual layout design of corner‐ and cross‐supporting structures.