基于CNN模型的施工现场典型安全隐患数据学习

林鹏; 魏鹏程; 樊启祥; 陈闻起

doi:10.16511/j.cnki.qhdxxb.2019.26.008

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清华大学学报（自然科学版） ›› 2019, Vol. 59 ›› Issue (8) : 628-634. DOI: 10.16511/j.cnki.qhdxxb.2019.26.008

水利水电工程

基于CNN模型的施工现场典型安全隐患数据学习

林鹏¹, 魏鹏程¹, 樊启祥², 陈闻起³

作者信息 +

CNN model for mining safety hazard data from a construction site

LIN Peng¹, WEI Pengcheng¹, FAN Qixiang², CHEN Wenqi³

Author information +

文章历史 +

摘要

该文旨在通过卷积神经网络（convolutional neural networks，CNN）模型对基础设施建设期典型安全隐患进行数据学习与挖掘，为现场智能安全管控提供方法和依据。依托于Wesafety平台实时统计出的某大型水电站现场安全隐患数据，分析了现场典型安全隐患特征，提出了基于CNN的安全隐患学习与挖掘模型，并定义了模型结构的卷积层、池化层、全连接层以及训练和测试流程，开发了相应的程序。结果表明：该方法提高了基础设施建设现场扁平-闭环安全管理的效率，为智能安全管理提供了崭新的思路，达到了机器自动识别典型隐患的目的，研究结果对建设工程安全隐患自动分类分析具有参考意义。

Abstract

A convolutional neural network model was used to mine typical safety hazard data from an infrastructure construction site for site intelligent safety control. Safety hazard data from a hydropower station during construction was analyzed to show the typical safety hazard data characteristics. The learning and mining used a convolution neural network (CNN) with the convolution layer, pooling layer, fully connected layer and training and testing processes defined here. The data mining and learning improves the safety flat-closed loop management safety for the construction site for intelligent safety management. The method then automatically identifies typical safety hazards for construction sites. The results provide guidance for automatic classification and analysis of safety hazards for a construction project.

导出引用

林鹏, 魏鹏程, 樊启祥, 陈闻起. 基于CNN模型的施工现场典型安全隐患数据学习[J]. 清华大学学报（自然科学版）. 2019, 59(8): 628-634 https://doi.org/10.16511/j.cnki.qhdxxb.2019.26.008

LIN Peng, WEI Pengcheng, FAN Qixiang, CHEN Wenqi. CNN model for mining safety hazard data from a construction site[J]. Journal of Tsinghua University(Science and Technology). 2019, 59(8): 628-634 https://doi.org/10.16511/j.cnki.qhdxxb.2019.26.008

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2018-10-07	2019-08-15
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