Stress Point Monitoring Algorithm for Structure of Steel Cylinder Concrete Pipes in Large Buildings

被引:0
|
作者
Yang, Huabin [1 ,2 ]
Jiang, Suo [1 ,2 ]
机构
[1] Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Xian 710049, Peoples R China
[2] Installat Engn Co Ltd, CSCEC Div 7, Zhengzhou 450000, Peoples R China
来源
SYMMETRY-BASEL | 2022年 / 14卷 / 06期
关键词
large building; concrete cylinder pipe; pipe structure; stress point monitoring; sensor node; PREDICTION; DISTANCE;
D O I
10.3390/sym14061261
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The internal structure of steel cylinder concrete pipe in large buildings is complex. Traditionally, the safety monitoring method is unable to accurately monitor the situation of every stress point in the structure. Therefore, the wireless sensor network with practical value in the field of building safety monitoring was introduced. A monitoring algorithm for stress points in pipeline structure was put forward. The distribution law of circumferential prestress produced by prestress steel wire on the pipe core concrete was analyzed. According to the influencing factors, the mechanical performance of tube concrete pipe structure was discussed, and the method of calculating prestress of pipe structure was constructed. Combined with sensor network nodes, a series of basic hypothesis information was set. Moreover, the force between node and stress point was analyzed by virtual potential field. Based on the force analysis for the centroid of the sensing area, the monitoring of the stress point in pipeline structures was completed. After that, a rectangular area to be monitored was selected and the force points were established randomly. According to the relationship between network coverage rate and monitoring efficiency, we found that the proposed algorithm had good network immunity. According to the different number of nodes, sensing radii and perception angles, the influence of index on the monitoring accuracy was discussed. Experimental results show that the accuracy of the proposed algorithm is sensitive to the change of node parameter. When the number of nodes, sensing radius and sensing angle change, the maximum fluctuation range of monitoring accuracy is 0.08-0.99. From the application effect of the algorithm, we can see that the detection effect of the algorithm has obvious advantage.
引用
收藏
页数:15
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