居住区性能模拟图像预测生成方法研究  

A Prediction and Generation Method for Performance Simulation Images in Residential Areas

作　　者：王虹宇 应小宇 WANG Hongyu;YING Xiaoyu

机构地区：[1]西南交通大学希望学院 [2]浙大城市学院国土空间规划学院

出　　处：《南方建筑》2024年第1期29-37,共9页South Architecture

基　　金：国家自然科学基金资助项目(51878608):基于图形参数化的夏热冬冷地区高层建筑群风环境评价与布局设计策略研究;浙江省自然科学基金资助项目(LY22E080004):方案设计视角下的高层办公建筑低能耗形态生成方法。

摘　　要：为解决传统用数值模拟软件调整居住区设计方案物理性能的繁琐流程,提升以性能优化为导向的设计方法的实用价值。立足于深度学习,以对居住区较为重要的两项物理性能(风环境和日照环境)为例,构建基于条件生成对抗网络(CGAN)的性能模拟图像预测模型。实验结果表明:该方法的提出可在有限的误差范围内快速预测生成目标布局对应的性能模拟图像。建筑师可根据预测生成的性能模拟图像对目标方案的总平面图进行调整优化,提高工作效率,在住区强排的初步设计阶段具有较强实用性。Modeling,simulation,analysis,integration,and optimization are frequently steps in the process of modifying the physical performance of home design schemes using computer numerical simulation software.This process has poor application value for several reasons:it takes a long time,decreases productivity,and has high hardware requirements.Despite these issues,architects require this numerical data.[Method]A case study based on two physical aspects(wind environment and sunshine environment)of residential areas was carried out using computer deep learning.A performance simulation image prediction model based on conditional generative adversarial network(CGAN)was built by learning the overall layout plane of residential areas under different height distribution characteristics as well as the corresponding sunshine simulation and wind environment simulation images.Construction of the performance simulation image prediction model covered training dataset establishment,physical performance training mode,Tensor Flow tools,and CGAN data structure.Specifically,the training dataset can apply multiple methods.Physical performance training mode can be divided by type based on physical model and the type of data.It defines the generator network,discriminator network,and loss function by using Tensor Flow tools.The data structure of CGAN includes determination of the generator network,discriminator network,and loss function.The objective image quality assessment algorithm(SSIM)based on structural similarity was used to analyze and validate the predictions of the trained model.The experimental findings prove CGAN efficacy in image generation.Therefore,CGAN can realize fast prediction and generation of performance simulation images with limited errors and has considerable practical value.In addition,the performance simulation images produced by CGAN can facilitate adjustment and optimization of the overall layout.Based on the open source programming platform,the performance prediction model was trained and built using CGAN.The par

关 键 词：深度学习 数值模拟 性能预测 条件生成对抗网络 高层居住区 

分 类 号：TU984.12[建筑科学—城市规划与设计]