Article(id=1281627175661056192, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.1196, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757001600000, receivedDateStr=2025-09-05, revisedDate=1763740800000, revisedDateStr=2025-11-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493330158, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493330158, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493330158, creator=13701087609, updateTime=1783493330158, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=130, endPage=137, ext={EN=ArticleExt(id=1281627177485578433, articleId=1281627175661056192, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Study on prediction of temperature characteristic parameters for subway train with multiple lateral openings and tnnnels, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

Existing methods for predicting temperature from subway carriage and tunnel fires mainly rely on physical models and empirical methods that are valid only under narrowly-defined environmental conditions. To solve this issue, this study adopts an artificial-intelligence-based approach. A GA-BPNN network model is constructed by optimizing BPNN using a GA. The GA is employed to global optimize BPNN's weights and thresholds, after which the model is trained to predict the temperature distribution of both the subway carriage and the tunnel, thereby achieving intelligent inversion of the fire temperature field. The results show that, for subway carriage temperature prediction, GA-BPNN model yields a mean absolute error (MAE) of 8.17, a root mean square error (RMSE) of 9.76, and a coefficient of determination (R²) of 0.99. For tunnel temperature prediction, MAE is 3.95, RMSE is 5.63, and R² reaches 0.98. By comparing the results with those of the traditional BPNN, it is found that the GA-BPNN model outperforms the conventional BPNN in both prediction accuracy and generalization capability.

, authors=Zhenkun WU^{1, 2}, Min PENG^{**, 2}, Guoqing ZHU², Lu LIU², Dongzi QIN³, authorsList=Zhenkun WU, Min PENG, Guoqing ZHU, Lu LIU, Dongzi QIN, authorCompany=null, correspAuthors=Min PENG, 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=1281627178110529732, articleId=1281627175661056192, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=侧向多开口地铁列车和隧道温度特征参数预测, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为解决现有地铁列车和隧道火灾预测方法大多依赖于物理模型和经验公式而导致预测精度不足的问题，从人工智能角度出发，基于遗传算法（GA）优化反向传神经网络（BPNN），构建GA-BPNN网络模型；利用GA对BPNN的权重和阈值进行全局寻优；训练与预测车厢与隧道顶棚温度分布，智能反演火灾温度场。结果表明：GA-BPNN模型对车厢温度预测的平均绝对误差（MAE）为8.17，均方根误差（RMSE）为9.76，决定系数R²为0.99；隧道温度预测的MAE为3.95，RMSE为5.63，R²为0.98。通过对比发现，GA-BPNN模型在准确性和泛化能力上都优于传统BPNN模型。

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²中国矿业大学 安全工程学院，江苏 徐州 221116, bio={"content":"

吴振坤（1990—），男，甘肃张掖人，博士，讲师，主要从事轨道交通火灾防控及监测预警等方面的研究。E-mail:TB21120027B0@cumt.edu.cn。

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吴振坤（1990—），男，甘肃张掖人，博士，讲师，主要从事轨道交通火灾防控及监测预警等方面的研究。E-mail:TB21120027B0@cumt.edu.cn。

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