基于多信息融合的深基坑安全预警方法及应用
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摘要
提出了一种基于多信息融合的深基坑安全预警方法。验证了T-S模糊神经网络是适用于处理非线性、大量数学分析的多信息融合模型,并利用MATLAB软件实现了该模型的构建。通过大量样本对模型训练与检测,实现对应空间区位与事故警情的各组多信息融合模型的准确性与泛化能力,提升现有深基坑安全预警信息分析处理的智能化。最后,以黄浦新城地铁车站深基坑工程为研究实例,实地采取各信息源相应预警信息,并代入已训练好的各组多信息融合模型。结果表明,融合结果与现场实际情况相吻合,验证了该方法的可行性。可为深基坑安全预警方法提供参考。
A method based on multi-information fusion for deep foundation pit safety warning was proposed. The T-S fuzzy neural network is a multi-information fusion model which is suitable for nonlinear and massive mathematical analysis, and the model was constructed by MATLAB software. Through the training and detection of a large number of samples, the accuracy and generalization ability of each group of multi-information fusion models corresponding to spatial location and accident alert were realized, and the intelligent analysis and processing of existing deep foundation pit safety warning information were intelligently improved. Finally, taking the deep foundation pit project of Huangpu New Town subway station as the research object, the corresponding information of each information source was taken in the field, and put into the trained multi-information fusion model. The results show that, the fusion result is consistent with the actual situation on the site, and the feasibility of the method is verified. It can provide reference for the safety warning method of deep foundation pit.
A method based on multi-information fusion for deep foundation pit safety warning was proposed. The T-S fuzzy neural network is a multi-information fusion model which is suitable for nonlinear and massive mathematical analysis, and the model was constructed by MATLAB software. Through the training and detection of a large number of samples, the accuracy and generalization ability of each group of multi-information fusion models corresponding to spatial location and accident alert were realized, and the intelligent analysis and processing of existing deep foundation pit safety warning information were intelligently improved. Finally, taking the deep foundation pit project of Huangpu New Town subway station as the research object, the corresponding information of each information source was taken in the field, and put into the trained multi-information fusion model. The results show that, the fusion result is consistent with the actual situation on the site, and the feasibility of the method is verified. It can provide reference for the safety warning method of deep foundation pit.
引文
[1]周二众.基于测量机器人的深基坑安全监测预警系统研究[D].重庆:重庆大学, 2012.
[2]徐美,朱翔,刘春腊.基于RBF的湖南省土地生态安全动态预警[J].地理学报, 2012, 67(10):1411-1422.
[3]刘凯,郭勇义,吴世跃.煤矿安全监测多传感器分层数据融合模型研究[J].工矿自动化, 2014, 40(6):45-50.
[4]郭英见,吴冲.基于信息融合的商业银行信用风险评估模型研究[J].金融研究, 2009(1):95-106.
[5]戴文奎,熊智彪,宫鹤,等.既有高边坡下深基坑支护设计与施工监测分析[J].建筑结构,2013,43(24):68-71.
[6]曾庆茂.基于神经网络和模糊推理的信息融合技术[D].西安:西安科技大学, 2005.
[7]侯燕,乔蕊.基于实时数据关联分析的煤矿安全预警系统设计[J].计算机测量与控制,2013,21(10):2826-2828.
[8]王勰.地铁车站深基坑工程施工风险评价研究[D].石家庄:石家庄铁道大学, 2012.
[9]田吉永.地铁车站深基坑变形规律仿真及监测预警技术研究[D].大连:大连交通大学, 2013.
[10]王津申,葛志闪,王希.美军全球信息栅格中的信息融合研究[C]//2010国防空天信息技术前沿论坛.西安,2010.
[11]杨鹃.多传感器信息融合技术在移动机器人避障中的应用[D].哈尔滨;哈尔滨理工大学, 2005.
[12]都基安.煤矿建设项目安全预警评价体系研究[D].徐州:中国矿业大学, 2013.
[13]郭润龙.基于RBF神经网络与D-S证据理论的数据融合方法及应用研究[D].哈尔滨:东北林业大学, 2009.
[14]侯学渊,陈永福.深基坑开挖引起周围地基土沉陷的计算[J].岩土工程师, 1989(1):1-13.
[2]徐美,朱翔,刘春腊.基于RBF的湖南省土地生态安全动态预警[J].地理学报, 2012, 67(10):1411-1422.
[3]刘凯,郭勇义,吴世跃.煤矿安全监测多传感器分层数据融合模型研究[J].工矿自动化, 2014, 40(6):45-50.
[4]郭英见,吴冲.基于信息融合的商业银行信用风险评估模型研究[J].金融研究, 2009(1):95-106.
[5]戴文奎,熊智彪,宫鹤,等.既有高边坡下深基坑支护设计与施工监测分析[J].建筑结构,2013,43(24):68-71.
[6]曾庆茂.基于神经网络和模糊推理的信息融合技术[D].西安:西安科技大学, 2005.
[7]侯燕,乔蕊.基于实时数据关联分析的煤矿安全预警系统设计[J].计算机测量与控制,2013,21(10):2826-2828.
[8]王勰.地铁车站深基坑工程施工风险评价研究[D].石家庄:石家庄铁道大学, 2012.
[9]田吉永.地铁车站深基坑变形规律仿真及监测预警技术研究[D].大连:大连交通大学, 2013.
[10]王津申,葛志闪,王希.美军全球信息栅格中的信息融合研究[C]//2010国防空天信息技术前沿论坛.西安,2010.
[11]杨鹃.多传感器信息融合技术在移动机器人避障中的应用[D].哈尔滨;哈尔滨理工大学, 2005.
[12]都基安.煤矿建设项目安全预警评价体系研究[D].徐州:中国矿业大学, 2013.
[13]郭润龙.基于RBF神经网络与D-S证据理论的数据融合方法及应用研究[D].哈尔滨:东北林业大学, 2009.
[14]侯学渊,陈永福.深基坑开挖引起周围地基土沉陷的计算[J].岩土工程师, 1989(1):1-13.