Intelligent crack identification method for high‐rise buildings aided by synthetic environments

Ziluo Yao, Sheng Jiang, Shuo Wang, Jingjing Wang, Hai Liu, Yasutaka Narazaki, Jie Cui, Billie F. Spencer
{"title":"Intelligent crack identification method for high‐rise buildings aided by synthetic environments","authors":"Ziluo Yao, Sheng Jiang, Shuo Wang, Jingjing Wang, Hai Liu, Yasutaka Narazaki, Jie Cui, Billie F. Spencer","doi":"10.1002/tal.2117","DOIUrl":null,"url":null,"abstract":"SummaryCracks can develop in high‐rise buildings because of long‐term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning‐based identification methods can efficiently identify cracks, the accuracy of crack identification in high‐rise buildings needs to be improved due to the lack of crack datasets specifically related to high‐rise structures. Moreover, the number of available images of cracks in high‐rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high‐rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high‐rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full‐scale high‐rise buildings. The trained network can accurately identify different components in images of the full‐scale, high‐rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application‐specific context in crack identification.","PeriodicalId":501238,"journal":{"name":"The Structural Design of Tall and Special Buildings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Structural Design of Tall and Special Buildings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/tal.2117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

SummaryCracks can develop in high‐rise buildings because of long‐term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning‐based identification methods can efficiently identify cracks, the accuracy of crack identification in high‐rise buildings needs to be improved due to the lack of crack datasets specifically related to high‐rise structures. Moreover, the number of available images of cracks in high‐rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high‐rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high‐rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full‐scale high‐rise buildings. The trained network can accurately identify different components in images of the full‐scale, high‐rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application‐specific context in crack identification.
合成环境辅助下的高层建筑智能裂缝识别方法
摘要由于长期的环境变化以及强风或地震等极端荷载事件,高层建筑可能会出现裂缝。虽然基于深度学习的识别方法可以有效识别裂缝,但由于缺乏专门与高层建筑结构相关的裂缝数据集,高层建筑裂缝识别的准确性还有待提高。此外,现有的高层建筑裂缝图像数量有限。为此,本文建立了一种基于逼真合成建模技术的智能裂缝识别方法。首先,构建一个具有假定损伤的高层建筑计算机图形(CG)模型。随后,利用该计算机图形模型生成一个数据集,其中包括高层建筑的逼真图像以及各种组件和损坏类型的相应标签。生成的数据集随后用于训练 DeepLabv3 + 神经网络,以识别结构部件和损坏,然后使用合成和全尺寸高层建筑的图像进行验证。经过训练的网络可以准确识别全尺寸高层建筑图像中的不同组件,并识别这些图像中有意合成的裂缝。结果表明,CG 模型生成的合成数据集不仅可以快速有效地标记神经网络训练,而且优于在裂缝识别中不考虑任何特定应用背景的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信