Article(id=1282030085456770047, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.08.0125, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744041600000, receivedDateStr=2025-04-08, revisedDate=1749916800000, revisedDateStr=2025-06-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589391339, onlineDateStr=2026-07-09, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589391339, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589391339, creator=13701087609, updateTime=1783589391339, updator=13701087609, issue=Issue{id=1282030021623657005, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='8', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589376121, creator='13701087609', updateTime=1783589731222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031511155228698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031511155228699, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=213, endPage=218, ext={EN=ArticleExt(id=1282030085653902336, articleId=1282030085456770047, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Fire temperature field prediction in commercial buildings based on FDS, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

To address the complexity of fire environment and the difficulty in predicting the temperature field in modern commercial buildings, a fire temperature field prediction model was constructed by combining CNN with SVM. Firstly, FDS was used to construct a commercial building fire model, and the sequence data received by the temperature measurement points were obtained. The temperature, position coordinates, and fire duration were used as input parameters to build the dataset. Secondly, the Rime Optimization Algorithm (RIME) was introduced to optimize the number of hidden layer nodes, regularization coefficient, and learning rate in the CNN-SVM, and then the prediction model was established. Finally, experiments were conducted based on the established dataset and prediction model, and the anti-interference ability of the model under different sensor failure rates was discussed. The results show that the model performs optimally in the prediction of the temperature field plane, with an average absolute percentage error of 5.6% and a maximum relative temperature error not exceeding 25%. The anti-interference performance is the best under three working conditions, and the maximum error does not exceed 15% under extreme conditions.

, authors=Yanxi CAO¹, Hongyan MA^{**, 1, 2, 3}, Shun WANG¹, authorsList=Yanxi CAO, Hongyan MA, Shun WANG, authorCompany=null, correspAuthors=Hongyan MA, 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, fund=null), CN=ArticleExt(id=1282030086157217795, articleId=1282030085456770047, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于FDS的商业建筑火灾温度场预测, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为解决现代商业建筑火灾环境复杂、温度场预测难的问题，利用卷积神经网络（CNN）结合支持向量机（SVM）构建火灾温度场预测模型。先采用火灾动力学模拟（FDS）搭建商业建筑火灾模型，获得温度测点接收的序列数据，将温度、位置坐标和火灾持续时间作为输入参数建立数据集；再引入霜冰优化算法（RIME）对CNN-SVM中的隐藏层节点数、正则化系数和学习率进行寻优，建立预测模型，并讨论模型在不同的传感器损坏率下的抗干扰能力。结果表明：该模型在温度场平面预测上表现最优，平均绝对百分比误差为5.6%，最大相对温度误差不超过25%。在3种工况下抗干扰性能最佳，极端条件下最大误差不超过15%。

, authors=曹妍曦¹, 马鸿雁教授^{**, 1, 2, 3}, 王顺¹, authorsList=曹妍曦, 马鸿雁教授, 王顺, authorCompany=null, correspAuthors=马鸿雁教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=0uJEveuWZFaGHcIiR4aNMA==, magXml=aJSDQvsN0+4f6rsWhRf8lA==, pdfUrl=null, pdf=wroDSmuG9LqvjGTox/VSfA==, pdfFileSize=3489518, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=E5t6ue2JxpAKUcNIsCbWOA==, mapNumber=null, fund=null)}, authors=[Author(id=1282030086710865934, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030085456770047, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=2018550022004@stu.bucea.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282030086790557712, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030085456770047, authorId=1282030086710865934, language=EN, stringName=Yanxi CAO, firstName=Yanxi, middleName=null, lastName=CAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 10044, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282030086874443793, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030085456770047, authorId=1282030086710865934, language=CN, stringName=曹妍曦, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹北京建筑大学 智能科学与技术学院，北京 100044, bio={"content":"

曹妍曦（2000—），女，四川内江人，硕士研究生，研究方向为建筑火灾温度预测和火源定位。E-mail:2018550022004@stu.bucea.edu.cn。

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曹妍曦（2000—），女，四川内江人，硕士研究生，研究方向为建筑火灾温度预测和火源定位。E-mail:2018550022004@stu.bucea.edu.cn。

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