基于T-S模糊神经网络的地铁深基坑安全预警

doi:10.16265/j.cnki.issn1003-3033.2018.08.027

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (8): 161-167.doi: 10.16265/j.cnki.issn1003-3033.2018.08.027

基于T-S模糊神经网络的地铁深基坑安全预警

王乾坤¹ 教授, 年春光¹, 杨冬², 张雨峰³

1 武汉理工大学土木工程与建筑学院,湖北武汉 430070;
2 武汉地铁集团有限公司质量安全部,湖北武汉 430030;
3 保利武汉房地产开发有限公司,湖北武汉 430040

收稿日期:2018-05-10 修回日期:2018-07-09 出版日期:2018-08-28 发布日期:2020-11-25
作者简介:王乾坤(1964—),男,湖北天门人,博士,教授,主要从事工程管理信息化、地铁安全预警等方面的研究。E-mail:wangqk@whut.edu.cn;年春光(1994—),男,安徽宿州人,硕士研究生,研究方向为工程管理信息化、地铁安全预警等。E-mail:18855582891@163.com。

Early warning of risks in metro deep foundation pit constructionbased on T-S fuzzy neural network

WANG Qiankun¹, NIAN Chunguang¹, YANG Dong², ZHANG Yufeng³

1 School of Civil Engineering and Architecture,Wuhan University of Technology,Wuhan Hubei 430070,China;
2 Quality and Safety Department,Wuhan Metro Group Co.,Ltd.,Wuhan Hubei 430030,China;
3 Poly Wuhan Real Estate Development Co.,Ltd.,Wuhan Hubei 430040,China

Received:2018-05-10 Revised:2018-07-09 Online:2018-08-28 Published:2020-11-25

摘要/Abstract

摘要： 为提高地铁深基坑施工安全预警的准确性和高效性,针对传统预警信息分析处理过程中存在的单指标评判、人为随意决策、不同指向的信息错误组合等问题,提出基于T-S模糊神经网络的多信息融合模型。以黄浦新城站深基坑工程为背景,从空间区位和事故警情2个方面识别与筛选安全预警信息源;运用T-S模糊神经网络构建多信息融合模型,选取大量样本对模型进行训练与检测,以提高模型的有效性和泛化能力;融合预警信息并对融合结果进行分析。结果表明:空间区位和事故警情的融合结果与现场的警情位置和警情类型相吻合,证明该融合模型在深基坑施工安全预警中具有可行性与适用性。

关键词: T-S模糊神经网络, 地铁深基坑, 安全预警, 多源信息识别, 多源信息融合

Abstract: Accurate and quick analysis of warning information is essential to guaranteeing an effective early warning of risks in metro deep foundation pits construction.To avoid the mistakes caused by single index evaluation,arbitrariness of personal decision making,and the interference of different pieces of early-warning information,this paper was aimed at building a T-S fuzzy neural network model.Firstly,the deep excavation project of Huangpuxincheng Station in Wuhan city was investigated and sources of information on risks were identified and screened from two aspects-spatial location and accident warning.Secondly,based on T-S fuzzy neural network,a multi-information fusion model was built,and a large number of samples were selected to train and test the model to improve its effectiveness and generalization.Finally,the warning information integration was made and results were discussed.The results show that the outcome of the model is consistent with the location and the type of the alert-situation,which proves the effectiveness and applicability of the fusion model in the safety precaution of deep foundation pit construction.

Key words: T-S fuzzy neural network, metro deep foundation pit, safety early warning, multi-source information identification, multi-source information fusion

中图分类号:

X928.03

王乾坤, 年春光, 杨冬, 张雨峰. 基于T-S模糊神经网络的地铁深基坑安全预警[J]. 中国安全科学学报, 2018, 28(8): 161-167.

WANG Qiankun, NIAN Chunguang, YANG Dong, ZHANG Yufeng. Early warning of risks in metro deep foundation pit constructionbased on T-S fuzzy neural network[J]. China Safety Science Journal, 2018, 28(8): 161-167.

参考文献

[1] 李皓燃,李启明,陆莹.2002—2016年我国地铁施工安全事故规律性的统计分析[J].都市快轨交通,2017,30(1):12-19.
LI Haoran,LI Qiming,LU Ying.Statistical analysis on regularity of subway construction accidents from 2002 to 2016 in China[J].Urban Express Rail Transit,2017,30(1):12-19.
[2] 吴贤国,陈跃庆,张立茂,等.地铁工程施工安全监控预警管理及评价标准研究[J].铁道工程学报,2013,30(5):107-111.
WU Xianguo,CHEN Yueqing,ZHANG Limao,et al.Research on the standards for safety monitoring and forewarning management assessment in metro construction[J].Journal of Railway Engineering,2013,30(5):107-111.
[3] 吴锋波,金淮,谢谟文,等.城市轨道交通基坑工程监测预警标准研究[J].施工技术,2013,42(20):103-107.
WU Fengbo,JIN Huai,XIE Mowen,et al.Monitoring measurement early warning standard of urban rail transit foundation excavation engineering[J].Construction Technology,2013,42(20):103-107.
[4] 罗春贺,宋永发.基于物联网技术的地铁安全监控研究[J].工程管理学报,2013,27(2):35-39.
LUO Chunhe,SONG Yongfa.Subway security monitoring based on internet of things[J].Journal of Engineering Management,2013,27(2):35-39.
[5] DING Lieyun,ZHOU Cheng,DENG Qingxu,et al.Real-time safety early warning system for cross passage construction in Yangtze riverbed metro tunnel based on the internet of things[J].Automation in Construction,2013,36(12):25-37.
[6] 刘文,赵挺生,张亚静,等.地铁盾构施工安全风险规律分析与对策[J].中国安全科学学报,2017,27(10):130-136.
LIU Wen,ZHAO Tingsheng,ZHANG Yajing,et al.Analysis of risk factors and countermeasures for metro shield construction[J].China Safety Science Journal,2017,27(10):130-136.
[7] 吴贤国,吴克宝,沈梅芳,等.基于N-K模型的地铁施工安全风险耦合研究[J].中国安全科学学报,2016,26(4):96-101.
WU Xianguo,WU Kebao,SHEN Meifang,et al.Research on coupling of safety risks in metro construction based on N-K model[J].China Safety Science Journal,2016,26(4):96-101.
[8] DING Lieyun,ZHOU Cheng.Development of web-based system for safety risk early warning in urban metro construction[J].Automation in Construction,2013,34(13):45-55.
[9] XIE Yi,LIU Jia.Analysis of early-warning threshold for metro construction collapse risk based on D-S evidence theory and rough set[J].Wuhan University Journal of Natural Sciences,2017,22(6):510-516.
[10] 李俊辉,黎新华,谢小星.基于多信息融合的城市轨道交通车站安全预警研究[J].城市轨道交通研究,2015,18(12):59-63.
LI Junhui,LI Xinhua,XIE Xiaoxing.Urban rail transit station security warning based on multi-information fusion[J].Urban Transit Research,2015,18(12):59-63.
[11] 张文,王莉.基于T-S 模糊神经网络的颍河水质时空变化特征分析[J].环境科学与技术,2015,38(12):254-261.
ZHANG Wen,WANG Li.Analysis of characteristics of Ying river water quality change of time and space based on T-S fuzzy neural network[J].Environmental Science & Technology,2015,38(12):254-261.
[12] 齐昕,刘嘉夫.基于T-S模糊神经网络模型对西部村镇集雨窖水水质的评价研究[J].节水灌溉,2015,28(3):28-30.
QI Xin,LIU Jiafu.Rain-collection pit water quality evaluation in west villages based on T-S fuzzy neural network[J].Water Saving Irrigation,2015,28(3):28-30.
[13] 张广平,张晨晓,谢忠.基于T-S模糊神经网络的模型在台风灾情预测中的应用:以海南为例[J].灾害学,2013,28(2):86-89.
ZHANG Guangping,ZHANG Chenxiao,XIE Zhong.Application of model based on t-s fuzzy neural network in typhoon disaster prediction:taking Hainan as an example[J].Journal of Catastrophology,2013,28(2):86-89.
[14] 毛颜.城市轨道交通项目施工阶段安全风险预警研究[D].徐州:中国矿业大学,2015.
MAO Yan.Study on safety risk prewarning of urban rail transit project in construction phase[D].Xuzhou:China University of Mining and Technology,2015.
[15] 黄宏伟,顾雷雨,王怀忠.城市地下空间深开挖施工风险预警[M].上海:同济大学出版社,2014:14-19.
[16] 孙增圻,徐红兵.基于T-S模型的模糊神经网络[J].清华大学学报:自然科学版,1997,37(3):76-80.
SUN Zengqi,XU Hongbing.Fuzzy neural network based on T-S model[J].Journal of Tsinghua University:Natural Science Edition,1997,37(3):76-80.

基于T-S模糊神经网络的地铁深基坑安全预警

Early warning of risks in metro deep foundation pit constructionbased on T-S fuzzy neural network

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