基于生成对抗网络的框架结构平面整体布置方法

钟燕; 雷昕; 龙丹冰; 方长建; 康永君

doi:10.12454/j.jsuese.202300661

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基于生成对抗网络的框架结构平面整体布置方法

绿色建筑与智能建造 | 更新时间：2026-01-24

- 基于生成对抗网络的框架结构平面整体布置方法
  增强出版
- Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network
- 工程科学与技术 2025年57卷第3期页码：72-81
- 作者机构：
  
  1.西南交通大学土木工程学院，四川成都 610031
  2.中国建筑西南设计研究院有限公司，四川成都 610041
- 作者简介：
  
  钟燕（2000—），女，硕士生. 研究方向：建筑信息化. E-mail：2071965375@qq.com
  龙丹冰，讲师，E-mail：85098285@qq.com
- 基金信息：
  
  中国建筑西南设计研究院有限公司资助项目（CSCEC‒2023‒Z‒12）
- DOI：10.12454/j.jsuese.202300661
  中图分类号： TU375.4
- 收稿：2023-08-24，
  
  纸质出版：2025-05-20
- 稿件说明：
移动端阅览
钟燕,雷昕,龙丹冰,等.基于生成对抗网络的框架结构平面整体布置方法[J].工程科学与技术,2025,57(3):72‒81.

Zhong Yan,Lei Xin,Long Danbing,et al.Overall layout method of frame structure plane based on generative adversarial network[J].Advanced Engineering Sciences,2025,57(3):72‒81.
钟燕,雷昕,龙丹冰,等.基于生成对抗网络的框架结构平面整体布置方法[J].工程科学与技术,2025,57(3):72‒81. DOI： 10.12454/j.jsuese.202300661.

Zhong Yan,Lei Xin,Long Danbing,et al.Overall layout method of frame structure plane based on generative adversarial network[J].Advanced Engineering Sciences,2025,57(3):72‒81. DOI： 10.12454/j.jsuese.202300661.

摘要

建筑改建或增建时的结构设计是房屋结构设计中不容忽视的内容。本文面向建筑初步设计阶段，针对部分结构已确定的情况提出了基于生成对抗网络的框架结构平面整体布置方法，在建筑和部分结构双重约束条件下进行框架结构设计。该方法的核心为框架结构平面整体布置模型。在有限数据样本下，为减少模型训练参数，凝练样本特征，达到更好的模型训练效果，提出了建筑信息表达方法用于表达与结构特征有强关联性的建筑特征；提出了框架梁信息表达方法用于在平面图形中表达梁截面特征；提出框架柱信息表达方法用于在平面图形中表达柱截面特征。通过叠加特征图、裁剪和增广等手段，构造了用于训练生成式算法模型的5 120对数据作为数据集。同时，除沿用交并比评价指标外，为更合理地评价模型的“设计”能力，基于框架结构设计规则提出了原柱率、不合理指数和综合指标，并依据指标确定了最佳的框架结构平面整体布置模型。使用时将建筑和部分结构特征图输入最佳模型，即可生成框架结构平面布置图。最后，通过案例分析论证了本文提出的框架结构平面整体布置方法能快速地生成布置合理且满足经验要求的结构设计。

Abstract

Objective

In 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.

Methods

In 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 (

)

the irrationality index (

)

and the comprehensive index (

all

) based on practical experience and frame structure design rules. These indicators comprehensively evaluate the model’s capability to produce an overall frame structure layout.

evaluates the retention of frame columns generated by the model at their original input positions—the higher the ratio

the better the design compliance.

evaluates the distribution of columns across different building components and spaces—the lower the index

the more reasonable the arrangement.

all

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 mode

l is trained

the architectural and partial structure feature maps are input into the optimal model to generate a frame structure layout.

Results and Discussions

A 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

remained at 1.

generally trended downward.

all

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.

was 1

showing full retention of original column positions.

was 1.78

indicating a reasonable arrangement of columns.

all

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.

Conclusions

This 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.

关键词

Keywords

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