Intelligent design method for beam and slab of shear wall structure based on deep learning

Beam and slab design is a critical component of shear wall structure design. Currently, conventional manual design is time-consuming, and defining objective functions and design variables of an optimization design is challenging. In contrast, deep learning methods can learn high-dimensional image features and generate new designs, providing new solutions for efficient and intelligent structural design. Therefore, based on deep neural networks, this study proposes an intelligent layout design method for beams of reinforced concrete shear-wall structures using the input of fused building space and element attributes. This method learned the implicit laws of existing designs and realized the inferential generation of new layout schemes. Subsequently, based on mathematical statistics, methods to determine the type and size of coupling and frame beams are proposed. A typical case study shows that the structural performance of the beam and slab designed by this method was comparable to that of competent engineers. The maximum inter-story drift ratio of the result designed by the proposed method differs from that designed by engineers by no more than 5 x 10(-5). The differences in the maximum vertical typical-floor-slab displacement, the concrete consumption, and the steel consumption between the design result of the proposed method and the engineer's design result are 0.8%, 2.88%, and 6.20%, respectively. Moreover, the design efficiency was significantly improved by more than 30 times.

Automated summary

This study proposes an intelligent layout design method for beams of reinforced concrete shear-wall structures based on deep neural networks. By learning the implicit laws of existing designs and generating new layout schemes, this method improves design efficiency and achieves comparable results to competent engineers.