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 × 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.

梁板设计是剪力墙结构设计的重要组成部分。目前，传统的手动设计非常耗时，并且定义优化设计的目标函数和设计变量具有挑战性。相比之下，深度学习方法可以学习高维图像特征并生成新设计，为高效、智能的结构设计提供新的解决方案。因此，基于深度神经网络，本研究提出了一种钢筋混凝土剪力墙结构梁的智能布局设计方法，该方法使用融合的建筑空间和元素属性的输入。该方法学习了现有设计的隐含规律，实现了新布局方案的推理生成。随后，基于数理统计，提出了确定联轴器和框架梁类型和尺寸的方法。一个典型的案例研究表明，用这种方法设计的梁和板的结构性能与有能力的工程师相当。该方法设计结果的最大层间漂移比与工程师设计的结果相差不超过5×10 ^-5. 该方法的设计结果与工程师的设计结果在最大竖向典型楼板位移、混凝土用量和钢材用量上的差异分别为0.8%、2.88%和6.20%。此外，设计效率显着提高了30多倍。