Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network - ZHONG Yan; LEI Xin; LONG Danbing; FANG Changjian; KANG Yongjun (工程科学与技术)
ObjectiveIn the process of architectural renovation or expansion, structural design is a crucial aspect of building design. After the initial completion of the scheme design, architects often need to consider the compatibility between the architecture and its structure. Therefore, early intervention and immediate response in the structural scheme design are urgently needed. In this paper, addressing the preliminary design phase of architecture and focusing on situations where parts of the structure have already been determined, we propose a framework for the overall layout of the structural plan based on a Generative Adversarial Network (GAN), termed PF‒structGAN. This framework facilitates the design of the structural framework under the dual constraints of both architectural forms and predetermined structural elements. The core of this method involves constructing a model for the overall layout of the structural plan, which includes three main stages: constructing datasets, training and evaluating the model, and applying the model.MethodsIn the dataset construction stage, due to the limited number of data samples, and to reduce model training parameters, refine sample features, and improve training outcomes, this paper proposes three information representation methods for architecture, beams, and columns. It utilizes RGB color channels to store information separately: architectural space information is stored in the blue channel (B), beam information in the green channel (G), and column information in the red channel (R), thereby avoiding feature overlap in overlapping regions. The architectural information representation method is used to express architectural features strongly correlated with structural features. The beam information representation method is designed to express beam cross-sectional features in planar graphics. The column information representation method is designed to express column cross-sectional features in planar graphics. These three methods establish correlations between architectural and structural features. To integrate architectural and structural features, the feature maps are superimposed. The architectural feature map, partial beam feature map, and partial column feature map are superimposed to obtain the architectural and partial structural feature map. The beam and column feature maps are superimposed to obtain the structural feature map. The architectural and partial structural feature map, along with the structural feature map, constitute a pair of feature superposition maps. To address the problem that column features occupy too few pixels in the image, and to help the model learn these features more effectively, the paired feature superposition maps are cropped into four parts to increase the column feature ratio. Further augmentation of the original dataset is achieved by rotating it at 0°, 90°, 180°, and 270°, resulting in an expanded dataset. In the model training phase, the architectural and partial structural feature maps are used as constraint conditions, and the real structural feature maps are used as labels. The generator produces structural feature maps under the given constraints. The discriminator determines whether the generated image is real or synthetic. Through adversarial training, the generator and discriminator iteratively improve until reaching a Nash equilibrium. In the model evaluation phase, to assess the model’s design capability more reasonably, in addition to using the intersection over union (IoU) metric, this paper proposes the original column ratio index (γy), the irrationality index (γS), and the comprehensive index (γall) based on practical experience and frame structure design rules. These indicators comprehensively evaluate the model’s capability to produce an overall frame structure layout. γy evaluates the retention of frame columns generated by the model at their original input positions—the higher the ratio, the better the design compliance. γS evaluates the distribution of columns across different building components and spaces—the lower the index, the more reasonable the arrangement. γall integrates the above indicators—the higher the value, the more reasonable the structural layout. The best-performing model is determined based on these four indicators. Once the PF‒structGAN model is trained, the architectural and partial structure feature maps are input into the optimal model to generate a frame structure layout.Results and DiscussionsA total of 5 120 dataset pairs were created for training the generative model—4 320 for training and 800 for testing. The training set was input into the pix2pixHD framework, and training was stopped once adversarial training reached a Nash equilibrium. Model performance was evaluated using the four indicators. The IoU curve showed a general upward trend as training epochs increased. After the first epoch, γy remained at 1. γS generally trended downward. γall peaked at epoch 26; therefore, the model from the 26th epoch was selected as the best layout model. To verify the model’s structural design capability, an instance analysis was conducted using a teaching building project. The IoU between the model’s design and the engineer’s design was 0.56, indicating high similarity. γy was 1, showing full retention of original column positions. γS was 1.78, indicating a reasonable arrangement of columns. γall reached 0.88, suggesting that the generated structural layout was sound. The model’s generated frame columns met functional requirements, were well-placed, and had appropriate size and density. The layout of columns and beams closely resembled the engineer’s design.ConclusionsThis method enables intelligent and rapid generation of structural designs that comply with regulatory standards and follow conventional design practices. It offers reference solutions for architects during the structural design process.