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Article(id=1149774728384180873, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149774724923880044, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2404259, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717689600000, receivedDateStr=2024-06-07, revisedDate=1739116800000, revisedDateStr=2025-02-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1752057257027, onlineDateStr=2025-07-09, pubDate=1745769600000, pubDateStr=2025-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752057257027, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752057257027, creator=13701087609, updateTime=1752057257027, updator=13701087609, issue=Issue{id=1149774724923880044, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='12', pageStart='4827', pageEnd='5272', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752057256203, creator=13701087609, updateTime=1768456746933, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559174552764785, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149774724923880044, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559174552764786, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149774724923880044, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=5058, endPage=5065, ext={EN=ArticleExt(id=1149774728694559372, articleId=1149774728384180873, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Updating Methods of Digital Twin Models for Historical Buildings Based on Shape Distribution-mean Absolute Error Method, columnId=1156262729162810294, journalTitle=Science Technology and Engineering, columnName=Papers·Automation and Computational Technology, runingTitle=null, highlight=null, articleAbstract=

Aiming at the issue of how to realize the dynamic updating of the digital twin model of historic buildings, a “build-compare-update” updating idea was proposed. Firstly, the shape distribution method was used to draw the shape distribution curve of the digital twin model of the historical building at different time points, based on completing the construction of the digital twin model of the historical building. Secondly, the mean absolute error method was used to assess the similarity of the two models. Furthermore, according to the results of the similarity assessment, the updating strategy for the model of the components of the historic building was set up and the updating of the digital twin model was completed with the help of relevant software. Finally, to validate the design, the arch column component of the Jade Buddha Hall of Hongci Temple was used as a case study. The results show that the proposed idea of updating the historical building model and its implementation method not only lay the foundation for realizing the dynamic updating of the digital twin model of historical buildings, but also provide reference value for the intelligent protection of historical buildings.

, correspAuthors=Kai WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Ai-lin ZHANG, Kai WANG, Chao DING, Ze-wen YAN, Xiu-ying LANG), CN=ArticleExt(id=1149774765130477586, articleId=1149774728384180873, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于形状分布-平均绝对误差法的历史建筑数字孪生模型更新方法, columnId=1156262729783567290, journalTitle=科学技术与工程, columnName=论文·自动化技术、计算机技术, runingTitle=null, highlight=null, articleAbstract=

针对如何实现历史建筑数字孪生模型动态更新这一问题,提出一种“构建-对比-更新”的更新思路。首先,在完成历史建筑数字孪生模型构建的基础上,借助形状分布法,绘制不同时点下构建的历史建筑数字孪生模型的形状分布曲线;其次,利用平均绝对误差法的变形实现两种模型的相似度评估;此外,根据不同的相似度评估结果,设定历史建筑构件模型的更新策略,并借助相关软件完成数字孪生模型更新;最后,以弘慈寺玉佛殿斗拱柱构件为对象进行实例验证。结果表明:所提出的历史建筑模型更新思路及其实现方法不仅为实现历史建筑数字孪生模型动态更新奠定了基础,还为历史建筑的智能化保护提供参考价值。

, correspAuthors=王凯, authorNote=null, correspAuthorsNote=
* 王凯(2000—),男,汉族,山东德州人,硕士研究生。研究方向:数字孪生模型对比及更新、历史建筑数字化保护。E-mail:。
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张爱琳(1978—),女,汉族,山东肥城人,硕士,副教授。研究方向:建筑信息化技术、智能建造与运维。E-mail:。

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张爱琳(1978—),女,汉族,山东肥城人,硕士,副教授。研究方向:建筑信息化技术、智能建造与运维。E-mail:。

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张爱琳(1978—),女,汉族,山东肥城人,硕士,副教授。研究方向:建筑信息化技术、智能建造与运维。E-mail:。

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model, figureFileSmall=gWj9aywPuGLATsSUkZoRfg==, figureFileBig=DZqH+Adn2YVBoSuklXyozw==, tableContent=null), ArticleFig(id=1179790635470045359, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=图2, caption=数字孪生模型更新逻辑图, figureFileSmall=gWj9aywPuGLATsSUkZoRfg==, figureFileBig=DZqH+Adn2YVBoSuklXyozw==, tableContent=null), ArticleFig(id=1179790635545542833, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=EN, label=Fig.3, caption=Partial model of the arch, figureFileSmall=yaNcKyteLpEH+DMtv74o8Q==, figureFileBig=61qnipB9LUy0HY+Pa+YUOg==, tableContent=null), ArticleFig(id=1179790635612651699, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=图3, caption=斗拱局部模型图, figureFileSmall=yaNcKyteLpEH+DMtv74o8Q==, figureFileBig=61qnipB9LUy0HY+Pa+YUOg==, tableContent=null), ArticleFig(id=1179790635688149173, 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label=图5, caption=12种模型示意图, figureFileSmall=4gIxxkmN6br+XwQ187ESfQ==, figureFileBig=wN74JFYX0zzp/HWxHHmYAw==, tableContent=null), ArticleFig(id=1179790635948196029, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=EN, label=Fig.6, caption=Working principle diagram of scanner, figureFileSmall=SpOfMQ4pwVMsb9F2iMNQEg==, figureFileBig=b5uTuMg0NbgrYvnuoSgVww==, tableContent=null), ArticleFig(id=1179790636006916287, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=图6, caption=扫描仪工作原理图

θ为垂直扫描角度;α为水平扫描角度;S为扫描仪与被测物之间的距离

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MAE' between 12 model shape distribution curves

, figureFileSmall=null, figureFileBig=null, tableContent=
模型名称 斗拱0 斗拱1 斗拱2 斗拱3 斗拱4 斗拱5 斗拱6 斗拱7 斗拱8 桌子 柱 墙
斗拱0 0 0.235 0.373 0.425 0.739 0.881 0.974 1.176 1.261 4.261 5.536 6.194
斗拱1 0 0.200 0.257 0.524 0.661 0.758 0.958 1.049 4.100 5.312 6.016
斗拱2 0 0.148 0.385 0.524 0.617 0.824 0.901 3.980 5.216 5.851
斗拱3 0 0.338 0.481 0.574 0.778 0.861 3.952 5.182 5.819
斗拱4 0 0.192 0.282 0.472 0.544 3.745 4.905 5.517
斗拱5 0 0.144 0.329 0.402 3.666 4.756 5.408
斗拱6 0 0.223 0.304 3.634 4.662 5.318
斗拱7 0 0.177 3.572 4.483 5.137
斗拱8 0 3.537 4.455 5.071
桌子 0 3.633 4.167
柱 0 3.398
墙 0
), ArticleFig(id=1179790636879331538, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=表1, caption=

12种模型形状分布曲线间的MAE'

, figureFileSmall=null, figureFileBig=null, tableContent=
模型名称 斗拱0 斗拱1 斗拱2 斗拱3 斗拱4 斗拱5 斗拱6 斗拱7 斗拱8 桌子 柱 墙
斗拱0 0 0.235 0.373 0.425 0.739 0.881 0.974 1.176 1.261 4.261 5.536 6.194
斗拱1 0 0.200 0.257 0.524 0.661 0.758 0.958 1.049 4.100 5.312 6.016
斗拱2 0 0.148 0.385 0.524 0.617 0.824 0.901 3.980 5.216 5.851
斗拱3 0 0.338 0.481 0.574 0.778 0.861 3.952 5.182 5.819
斗拱4 0 0.192 0.282 0.472 0.544 3.745 4.905 5.517
斗拱5 0 0.144 0.329 0.402 3.666 4.756 5.408
斗拱6 0 0.223 0.304 3.634 4.662 5.318
斗拱7 0 0.177 3.572 4.483 5.137
斗拱8 0 3.537 4.455 5.071
桌子 0 3.633 4.167
柱 0 3.398
墙 0
), ArticleFig(id=1179790636946440403, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=EN, label=Table 2, caption=

The similarity values S of the 12 model shape distribution curves

, figureFileSmall=null, figureFileBig=null, tableContent=
模型名称 斗拱0 斗拱1 斗拱2 斗拱3 斗拱4 斗拱5 斗拱6 斗拱7 斗拱8 桌子 柱 墙
斗拱0 1 0.765 0.627 0.575 0.261 0.119 0.026 0 0 0 0 0
斗拱1 1 0.800 0.743 0.476 0.339 0.242 0.042 0 0 0 0
斗拱2 1 0.852 0.615 0.476 0.383 0.176 0.099 0 0 0
斗拱3 1 0.662 0.519 0.426 0.222 0.139 0 0 0
斗拱4 1 0.808 0.718 0.528 0.456 0 0 0
斗拱5 1 0.856 0.671 0.598 0 0 0
斗拱6 1 0.777 0.696 0 0 0
斗拱7 1 0.823 0 0 0
斗拱8 1 0 0 0
桌子 1 0 0
柱 1 0
墙 1
), ArticleFig(id=1179790637042909396, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=表2, caption=

12种模型形状分布曲线的相似度值S

, figureFileSmall=null, figureFileBig=null, tableContent=
模型名称 斗拱0 斗拱1 斗拱2 斗拱3 斗拱4 斗拱5 斗拱6 斗拱7 斗拱8 桌子 柱 墙
斗拱0 1 0.765 0.627 0.575 0.261 0.119 0.026 0 0 0 0 0
斗拱1 1 0.800 0.743 0.476 0.339 0.242 0.042 0 0 0 0
斗拱2 1 0.852 0.615 0.476 0.383 0.176 0.099 0 0 0
斗拱3 1 0.662 0.519 0.426 0.222 0.139 0 0 0
斗拱4 1 0.808 0.718 0.528 0.456 0 0 0
斗拱5 1 0.856 0.671 0.598 0 0 0
斗拱6 1 0.777 0.696 0 0 0
斗拱7 1 0.823 0 0 0
斗拱8 1 0 0 0
桌子 1 0 0
柱 1 0
墙 1
), ArticleFig(id=1179790637105823957, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=EN, label=Table 3, caption=

Strategies of updating the digital twin model

, figureFileSmall=null, figureFileBig=null, tableContent=
S 对比结果 更新策略
0≤S≤S1 不同 更新
S1<S<0.9 相似 保留
0.9≤S≤1 相同 保留
), ArticleFig(id=1179790637168738518, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=表3, caption=

数字孪生模型的更新策略

, figureFileSmall=null, figureFileBig=null, tableContent=
S 对比结果 更新策略
0≤S≤S1 不同 更新
S1<S<0.9 相似 保留
0.9≤S≤1 相同 保留
), ArticleFig(id=1179790637227458775, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=EN, label=Table 4, caption=

Strategies of updating of digital twin model of the arch columns

, figureFileSmall=null, figureFileBig=null, tableContent=
S 对比结果 更新策略
0.00≤S≤0.802 不同 更新
0.802<S<0.900 相似 保留
0.900≤S≤1.00 相同 保留
), ArticleFig(id=1179790637307150552, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149774728384180873, language=CN, label=表4, caption=

斗拱柱的模型更新策略

, figureFileSmall=null, figureFileBig=null, tableContent=
S 对比结果 更新策略
0.00≤S≤0.802 不同 更新
0.802<S<0.900 相似 保留
0.900≤S≤1.00 相同 保留
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基于形状分布-平均绝对误差法的历史建筑数字孪生模型更新方法
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张爱琳 1, 2 , 王凯 1, * , 丁超 1, 2 , 闫泽文 3 , 郎秀颖 1
科学技术与工程 | 论文·自动化技术、计算机技术 2025,25(12): 5058-5065
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科学技术与工程 | 论文·自动化技术、计算机技术 2025, 25(12): 5058-5065
基于形状分布-平均绝对误差法的历史建筑数字孪生模型更新方法
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张爱琳1, 2 , 王凯1, * , 丁超1, 2, 闫泽文3, 郎秀颖1
作者信息
  • 1 内蒙古科技大学土木工程学院, 包头 014010
  • 2 内蒙古自治区高校智能建造与运维工程研究中心, 包头 014010
  • 3 中国电力工程顾问集团华北电力设计院有限公司, 北京 100120

    • 张爱琳(1978—),女,汉族,山东肥城人,硕士,副教授。研究方向:建筑信息化技术、智能建造与运维。E-mail:。

通讯作者:

* 王凯(2000—),男,汉族,山东德州人,硕士研究生。研究方向:数字孪生模型对比及更新、历史建筑数字化保护。E-mail:。
Updating Methods of Digital Twin Models for Historical Buildings Based on Shape Distribution-mean Absolute Error Method
Ai-lin ZHANG1, 2 , Kai WANG1, * , Chao DING1, 2, Ze-wen YAN3, Xiu-ying LANG1
Affiliations
  • 1 School of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
  • 2 Intelligent Construction and Operation and Maintenance Engineering Research Centre for Universities in Inner Mongolia Autonomous Region, Baotou 014010, China
  • 3 North China Power Engineering Co. , Ltd. of China Power Engineering Consulting Group, Beijing 100120, China
出版时间: 2025-04-28 doi: 10.12404/j.issn.1671-1815.2404259
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摘要
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针对如何实现历史建筑数字孪生模型动态更新这一问题,提出一种“构建-对比-更新”的更新思路。首先,在完成历史建筑数字孪生模型构建的基础上,借助形状分布法,绘制不同时点下构建的历史建筑数字孪生模型的形状分布曲线;其次,利用平均绝对误差法的变形实现两种模型的相似度评估;此外,根据不同的相似度评估结果,设定历史建筑构件模型的更新策略,并借助相关软件完成数字孪生模型更新;最后,以弘慈寺玉佛殿斗拱柱构件为对象进行实例验证。结果表明:所提出的历史建筑模型更新思路及其实现方法不仅为实现历史建筑数字孪生模型动态更新奠定了基础,还为历史建筑的智能化保护提供参考价值。

数字孪生模型  /  形状分布  /  平均绝对误差(MAE)  /  模型相似度  /  模型更新

Aiming at the issue of how to realize the dynamic updating of the digital twin model of historic buildings, a “build-compare-update” updating idea was proposed. Firstly, the shape distribution method was used to draw the shape distribution curve of the digital twin model of the historical building at different time points, based on completing the construction of the digital twin model of the historical building. Secondly, the mean absolute error method was used to assess the similarity of the two models. Furthermore, according to the results of the similarity assessment, the updating strategy for the model of the components of the historic building was set up and the updating of the digital twin model was completed with the help of relevant software. Finally, to validate the design, the arch column component of the Jade Buddha Hall of Hongci Temple was used as a case study. The results show that the proposed idea of updating the historical building model and its implementation method not only lay the foundation for realizing the dynamic updating of the digital twin model of historical buildings, but also provide reference value for the intelligent protection of historical buildings.

digital twin model  /  shape distribution  /  mean absolute error(MAE)  /  model similarity  /  model update
张爱琳, 王凯, 丁超, 闫泽文, 郎秀颖. 基于形状分布-平均绝对误差法的历史建筑数字孪生模型更新方法. 科学技术与工程, 2025 , 25 (12) : 5058 -5065 . DOI: 10.12404/j.issn.1671-1815.2404259
Ai-lin ZHANG, Kai WANG, Chao DING, Ze-wen YAN, Xiu-ying LANG. Updating Methods of Digital Twin Models for Historical Buildings Based on Shape Distribution-mean Absolute Error Method[J]. Science Technology and Engineering, 2025 , 25 (12) : 5058 -5065 . DOI: 10.12404/j.issn.1671-1815.2404259
正文
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历史建筑集文化、社会于一体,是中华民族的宝贵财富和智慧结晶[1]。然而,在自然因素和社会因素的双重影响下,历史建筑面临着被破坏甚至摧毁的风险,对中国文化遗产造成了重大影响。因此加强对历史建筑的保护以及提高历史建筑保护的技术手段迫在眉睫。
近年来,许多学者引入信息化技术来提高历史建筑的保护手段,其中应用最为广泛的技术手段是利用逆向化信息技术[2-5]建立历史建筑的数字化模型。高溪溪等[6]将三维激光扫描与建筑信息模型(building information modeling,BIM)技术结合,为受损历史建筑的保护提供了重要技术支持。何原荣等[7]结合三维激光扫描和无人机倾斜摄影技术,为历史建筑的高精度建模提供了新的方法,同时也为历史建筑保护提供了重要技术支撑。邵慧等[8]提出了全波形的高光谱激光雷达系统,为获取历史建筑完整的空间结构和健康状态提供了重要的技术手段。数字化模型的构建不仅为历史建筑的保护提供了更加精准的数据信息,还有效避免对历史建筑的二次破坏。但是,当下数字化模型只是对历史建筑的简单记录和呈现,无法做到后续分析,智能性较差[9]。基于此,部分研究者利用数字孪生技术[10-15]在构建高保真的数字化模型(数字孪生模型[16])的基础上实现了模型的后续分析,并提高了历史建筑的智能化保护水平。虽然数字孪生模型能实现与历史建筑实体的交互,对历史建筑的智能化有着重要意义,但是随着历史建筑实体的不断变化[17],数字孪生模型无法做到高保真[18],也就无法为历史建筑的智能化保护提供建议。因此,为了保证数字孪生模型的高保真性,如何实现数字孪生模型的动态更新成为重要问题。
针对上述问题,从数字孪生模型更新的角度出发,提出“构建-分析-更新”的数字孪生模型更新思路,以期实现对历史建筑状态及性能数据的实时获取、分析与动态模拟,提高模型与实体的一致性、动态性,以实现历史建筑的智能化保护。
1 数字孪生模型构建
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数字孪生落地应用的首要任务是创建应用对象的数字孪生模型[19]。历史建筑作为已有的物理实体,目前尚无相关的信息模型,因此需要通过逆向信息采集技术来构建其数字孪生模型。逆向信息采集技术分为基于激光的探测与测量技术和基于图像的建模与绘制技术两类。综合评估两种技术的精度、适用范围、应用成本和作业时长,选择三维激光扫描仪,并采用基于三维激光扫描技术与BIM技术相结合的方式构建历史建筑数字孪生模型。
构建历史建筑数字孪生模型的关键步骤是:点云模型的生成和点云模型向BIM的转换。首先,利用三维激光扫描仪获取历史建筑的点云数据。随后,在相关的点云数据处理软件中进行配准、降噪和抽稀等操作,形成完整的点云模型。其次,将点云模型导入3DMax软件,构建历史建筑的表面模型,将表面模型转换为DWG格式并导入AutoCAD软件,进一步转换为线框模型。最后,在Revit软件中,以此线框模型为基础,创建历史建筑的实体模型,完成历史建筑的数字孪生模型的构建。上述建模过程如图1所示。
2 数字孪生模型对比更新
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为了保证数字孪生模型能实时与历史建筑有效交互,就需要使“静态”的数字孪生模型“动”起来。首先需要确认当下新建的数字孪生模型(称为新模型)和原始数字孪生模型(称为旧模型)是否存在允许外的变形。当发现两种模型出现允许外的变形时,需要对旧模型进行更新,使数字孪生模型保持“孪生”。此外,基于自然状态下的历史建筑很少发生整体的变形,选择历史建筑的某一构件来进行更新分析。详细模型更新逻辑如图2所示。
2.1 模型对比
对Revit模型而言,可以通过比较建筑信息模型数据交换的开放标准(industry foundation classes,IFC)的文本信息以及模型构件的ID来对比模型差异。形状分布法是一种依靠形状分布函数将三维模型转换成二维曲线的方法。该方法具有速度快、鲁棒性强等特点,可以很好地满足模型对比的需求[20-22]。因此,利用形状分布法完成数字孪生模型的对比。形状分布函数的选择对于形状分布曲线的构建至关重要,Osada等[23]提出了5种形状分布函数,并通过实验得出D2形状分布函数对模型的描述能力优于其他形状分布函数。因此选择D2形状分布函数来创建形状分布曲线。
2.1.1 绘制形状分布曲线
在绘制形状分布曲线时,选择将得到的格式为RVT(Revit文件格式)的模型转换成格式为STL(stereo lithography)的实体模型后,在得到的实体模型表面随机取点,并计算任意两个随机点之间的欧氏距离,选择其中距离的最大值,计算出每一个距离与最大距离的比值作为形状分布曲线的横坐标,计算每一个比值出现的概率作为形状分布曲线的纵坐标,最终借助得到的数据来绘制形状分布曲线。
2.1.2 确定最优采样点数
同一实体模型形状分布曲线的稳定性受采样点数的影响。因此,需要确定最合适的采样点数,即最优采样点数。以斗拱局部的模型为对象,设定10个不同的采样点数,来确定最优随机点采样点数,如图3、图4所示。
由图4可以看出,随着采样点数的增加,形状分布曲线逐渐稳定。而且在采样点数达到105点后,随着采样点数的增加,形状分布曲线几乎没有发生明显的变化,因此,选择采样点数为105点。
2.1.3 形状分布相似度计算
形状分布相似度计算的实质是分析两个形状分布曲线的相似性。分析曲线相似性的方法有欧几里得距离法、余弦相似度法、平均绝对误差法、以及皮尔逊相关系数法。综合分析常用的曲线相似性计算方法发现,相较于其他方法,平均绝对误差法不仅能定量的反映模型之间的差异性,而且计算效率高,其值越大对应得相似性越小。因此,借助平均绝对误差法来完成模型对比分析,平均绝对误差(mean absolute error,MAE)的计算公式为
MAE(yi,yj)= 1 n ∑ i , j = 1 n y i - y j
式(1)中:yi为原始形状分布曲线上的y值;yj为新形状分布曲线上的y值;n为y值的个数。
yi和yj的数值通过相关算法从两种形状分布曲线上获取。
曲线的y值代表对应x值出现的概率,是一个很小的数值,因此,为了能使最终的MAE清晰反映各模型之间的差值,将式(1)的y值扩大1 000倍,如式(2)所示。
MAE'(yi,yj)= 1000 n ∑ i , j = 1 n y i - y j
为验证上述相似度计算方法的可靠性,选取12个模型(图5),并分别计算各模型之间的MAE',计算结果如表1所示。
由表1可知,对于不同类型的模型,它们形状分布曲线之间的MAE'都大于1。对于相同类型的模型,随着模型间的差异逐渐增大,每两个模型形状分布曲线之间的MAE'是逐渐增加的,直至大于1。而对于同一个模型,经多次计算得出,不同采样次数下形成的形状分布曲线之间的差异值近似于0.1。可见,MAE'与模型实际情况相吻合,而且误差较小,因此方法可行。
考虑到MAE'是间接反映形状分布曲线间的相似度,无法使人直观地观察出来。为了使两种形状分布曲线的相似度能直观地展现出来,通过将式(2)简单变形来实现这一目的,如式(3)所示。利用式(3)来表示形状分布曲线相似度,并将其定义为S。由于两个形状分布曲线的相似度不应存在负值,将产生的负值都做归零处理,因此S的取值范围为[0,1],如表2所示。
S=1- 1000 n ∑ i , j = 1 n y i - y j
2.2 模型更新
2.2.1 制定模型更新策略
在完成历史建筑数字孪生模型对比后,需要根据不同的比对结果采取相应的更新策略[24]。然而,由于不同建筑类型、不同构件的偏差允许范围是不同的,因而无法确定一个统一且明确的S作为构件模型的更新标准。因此,仅提出在不同标准对应的S下的对比结果以及相应的更新策略,如表3所示。将不同偏差允许值对应的S统一定义为S1,而将2.1.3节中测算出来的同一模型多次相似度计算结果的差异值定义为S2,并且由2.1.3节计算结果可得,S2为0.9。
2.2.2 模型更新方法
模型的更新主要借助Revit建模软件来实现。首先在得到具体的S1和S后,将其代入表3中来确定具体的更新措施。其次,同时打开旧模型的RVT项目文件以及新模型的RVT项目文件,并打开三维视图。最后,将旧模型中的构件删除,并复制新模型中的构件到旧模型文件中,选择同标高粘贴来使旧的构件更新为新的构件,以此来完成构件级的模型的更新。
3 实证分析
收起
为了验证模型构建以及对比更新方法的有效性,以内蒙古自治区呼和浩特市的弘慈寺玉佛殿为例,验证上述历史建筑数字孪生模型构建方法的可行性。弘慈寺是一座建于公元1580年的大藏传佛教寺院,历史文化底蕴深厚,在内蒙古地区有着深厚的影响。
3.1 玉佛殿点云模型生成
3.1.1 点云数据获取与处理
采用FAROFocus3DS350三维激光扫描仪来完成玉佛殿的点云数据收集,其工作原理如图6所示。由于玉佛殿本身是彩色的(图7),因此,点云数据采集过程中需要采用彩色扫描模式来获取周围事物的点云信息和颜色信息。在完成玉佛殿外部和内部三维激光扫描后得到关于玉佛殿的点云数据。点云数据共有34站,每站约有2.4×107的数据量,共采集8×108点三维点云。
为了提高点云模型的精度,需要对点云数据进行优化处理。通过SCENE软件完成点云赋色、全自动拼接、去噪过滤、查找标靶等处理并保存。
3.1.2 点云模型生成
在处理完成后,要检查自动拼接的质量。之后打开整体点云三维视图查看点云赋色和去噪情况,并借助裁剪框来将玉佛殿的点云部分从整体点云中裁剪出来,如图8所示。
3.2 玉佛殿数字孪生模型构建
将得到的点云模型导入3DMax软件中得到其相应的线框模型,并最终得到其玉佛殿的数字模型,如图9所示。
3.3 玉佛殿数字孪生模型对比更新
为验证模型对比更新方法的合理性,选择玉佛殿里的斗拱柱构件[图10(a)]来完成模型对比更新。但由于历史建筑短时间内的稳定性较好,因此,假设斗拱柱发生一定的位移变形[图10(b)],利用发生位移前后的两个模型完成模型对比更新的实证分析。
3.3.1 形状分布曲线构建
位移前后两个斗拱柱模型的形状分布曲线图如图11所示。
3.3.2 形状分布相似度计算
借助式(2)和式(3)计算位移前后两个模型的MAE'和相似度S。经计算得出,两个模型的MAE'和S分别为0.211和0.789。
3.3.3 模型更新
基于所选取的古建筑构件属于木结构,因此,参考《古建筑木结构维护与加固技术标准》(GB/T 50165—2020)中对应的偏差许可范围来确定具体的模型更新策略。根据《古建筑木结构维护与加固技术标准》(GB/T 50165—2020)可得,柱与柱础之间的错位量与柱子直径的比值ρd应满足ρd<1/6。所选斗拱柱的直径为300 mm,因此柱础错位量应小于50 mm。将柱础错位量设为50 mm时,计算得出MAE'和S1分别为0.198和0.802。因此,针对斗拱柱模型的更新策略如表4所示,可得当S大于0.802时,证明发生的位移变形在许可范围内。而根据3.3.2节可得,S为0.789,可见位移变形超出了许可范围,位移前后模型的对比结果为不同,需要对位移前的斗拱柱模型进行更新操作,详细更新操作见2.3.2节。
通过上述实证分析可知,所提出的更新思路是切实可行的。
4 结论
收起
针对数字孪生模型会存在与物理实体不一致的问题,提出“构建-对比-更新”的数字孪生模型更新思路,得出以下的结论。
(1)形状分布法的使用将三维模型转换成二维曲线,极大降低了模型对比的难度,较好地完成了三维模型的对比。
(2)平均绝对误差法的使用不但能高效率的对不同模型间的相似性进行定量的评价,还能增加模型对比结果的可靠性。
(3)针对不同模型设定的不同对比结果下模型更新策略,为模型更新操作提供了重要支持。
(4)所提方法可以有效地实现数字孪生模型的更新,弥补数字孪生模型会与物理实体不一致的问题,丰富了数字孪生模型的理论。
基金
收起
  • 内蒙古自治区自然科学基金(2022MS07017)
  • 内蒙古科技大学2024年基本业务费资助项目(2024YXXS043)
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2025年第25卷第12期
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文章信息
doi: 10.12404/j.issn.1671-1815.2404259
  • 接收时间：2024-06-07
  • 首发时间：2025-07-09
  • 出版时间：2025-04-28
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出版历史
  • 收稿日期：2024-06-07
  • 修回日期：2025-02-10
基金
内蒙古自治区自然科学基金(2022MS07017)
内蒙古科技大学2024年基本业务费资助项目(2024YXXS043)
作者信息
    1 内蒙古科技大学土木工程学院, 包头 014010
    2 内蒙古自治区高校智能建造与运维工程研究中心, 包头 014010
    3 中国电力工程顾问集团华北电力设计院有限公司, 北京 100120

通讯作者:

* 王凯(2000—),男,汉族,山东德州人,硕士研究生。研究方向:数字孪生模型对比及更新、历史建筑数字化保护。E-mail:。
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