An intelligent extraction method of tunnel construction activity duration information based on improved HT-LCNN
Extraction of activity duration information is important for schedule analysis and decision of tunnel con-struction.A common way to record the activity durations of tunnel construction is drawing the line segment as Ga-ntt chart in the construction log.However,the traditional extraction method that relies on manual statistics is fa-cing a dilemma between efficiency and accuracy.To solve this problem,this paper adopts a line segment detec-tion method based on deep learning.An automatic extraction method of tunnel construction activity duration infor-mation based on improved Hough Transform-Line Convolutional Neural Network(HT-LCNN)is proposed.First,the preprocessing is based on homography to solve the obliquity,rotation and twist of the original photos.Second,the global context network(GCNet),which has the ability of establishing the long-distance dependence of the feature graph and the dependence between channels,is integrated into the residual module of HT-LCNN to im-prove the model's attention to the target line segment of Gantt chart,reduce the interference from the line seg-ment of the table and text,and improve the detection accuracy.Third,the coordinates of detected lines are auto-matically converted into construction activity durations.The proposed method is used to extract the activity dura-tions of a TBM diversion tunnel project from 17 months of construction logs.The case study shows that the detec-tion precision AP5,AP10 and AP15 are 94.7％,95.0％and 95.1％respectively,which are higher than the results of HT-LCNN and LCNN.Compared with manual extraction results,the mean absolute error of our automatic ex-traction method is only 1.82 min.This study provides a novel idea for extraction of tunnel construction activity du-ration information accurately and efficiently.
duration of tunnel construction activitiesinformation intelligent extractionline segment detectiondeep network with Hough transformattention mechanism
万方数据
维普
