CNN model for mining safety hazard data from a construction site
LIN Peng1, WEI Pengcheng1, FAN Qixiang2, CHEN Wenqi3
1. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China; 2. China Huaneng Group Co., Ltd., Beijing 100031, China; 3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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.
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.
LIN Peng, WEI Pengcheng, FAN Qixiang, CHEN Wenqi. CNN model for mining safety hazard data from a construction site. Journal of Tsinghua University(Science and Technology), 2019, 59(8): 628-634.
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2019, Vol. 59 
