Curved steel structure roof sensor arrangement method based on Internet of Things technology

Abstract

The invention belongs to the field of intelligent construction, relates to a curved steel structure roof sensor arrangement method based on the Internet of Things technology, and aims to optimize a curved steel structure roof sensor arrangement scheme so as to improve the monitoring efficiency and reduce the monitoring cost. The method comprises the following steps: firstly, preliminarily determining candidate measuring points of a sensor according to a geometric shape and a finite element analysis result of the curved steel structure roof; secondly, by maximizing the data correlation among the measurement points, redundant arrangement is avoided, and it is ensured that the monitoring system can efficiently and accurately collect data reflecting the overall structure state; and finally, the selected sensors realize real-time acquisition and transmission of data through the Internet of Things technology, and remote monitoring and early warning are carried out. According to the method, the space utilization rate and the data accuracy of the curved steel structure roof monitoring system can be effectively improved, the sensor arrangement cost can be reduced, and the safety and the stability of the structure can be ensured.

Description

Technical Field

[0001] The invention belongs to the field of intelligent construction and relates to a method for arranging sensors on a curved steel structure roof based on Internet of Things technology. Background Art

[0002] In recent years, with the continuous development of construction technology, curved steel structure roofs have been widely used in large venues, exhibition centers, transportation hubs and other buildings due to their unique aesthetic design; however, due to the complex geometric shape and stress characteristics of curved steel structure roofs, the monitoring of their structural safety and health status is particularly important.

[0003] At present, traditional structural health monitoring methods usually rely on sensors arranged at key locations and realize data collection through wired connections. This method has the following problems: complex and costly wiring, incomplete data collection, insufficient real-time and intelligence. The development of Internet of Things technology has provided a new technical path for steel structure health monitoring: by combining sensors, wireless communication technology and data processing platform, real-time collection, transmission and analysis of sensor data can be realized. Therefore, a curved steel structure roof sensor layout method based on Internet of Things technology is proposed, which can optimize the sensor layout plan, save the sensor layout cost, and improve the space utilization and data accuracy of the monitoring system. Summary of the invention

[0004] The present invention aims at optimizing the arrangement scheme of sensors on a curved steel structure roof and provides a method for arranging sensors on a curved steel structure roof based on the Internet of Things technology.

[0005] The present invention is achieved through the following technical solutions:

[0006] The first step is to establish a curved steel structure roof model: a finite element analysis model is established according to the geometric shape of the curved steel structure roof to obtain the deflection curve of the standard load combination of the curved steel structure roof;

[0007] The second step is to determine the components to be tested: Calculate the deflection parameter α of each component under the standard load combination according to formula (1): f : In the formula, f max is the maximum deflection value of the component, l is the length of the straight line projected on the component plane; According to the descending order of deflection parameters, determine the component to be tested where the sensor is arranged;

[0008] The third step is to determine the candidate measuring points of the sensors: the point with the maximum deflection value in the mid-span of the component to be measured is set as the candidate measuring point of the displacement sensor, and the two ends of the component are set as the candidate measuring points of the stress sensor. The total number of candidate measuring points of the sensors is m, m=m1+m2, where m1 is the number of displacement sensors and m2 is the number of stress sensors.

[0009] The fourth step is to calculate the correlation coefficient between the candidate sensor points: Analyze the simulation data of the candidate sensor points and calculate the mean stress and displacement of each candidate point according to formula (2): Where mi is the number of sensors, D mi is the simulated data of the mi-th candidate measuring point;

[0010] Calculate the deviation M of each candidate measurement point data according to formula (3): m :

[0011] According to formula (4) and formula (5), the covariance Cov(D i , D j ) and standard deviation σD m :

[0012] According to formula (6), the correlation coefficient r(D i , D j )

[0013] Step 5: Optimize the arrangement of sensor measuring points: Determine the correlation coefficient threshold R of the sensor arrangement according to Table 1, merge and optimize the candidate sensor measuring points whose correlation exceeds the threshold, and determine the sensor measuring points;

[0014] Table 1: Correlation coefficient threshold R selection table <![CDATA[Evaluation parameter α i Interval]]> (0，0.5] (0.5，0.8] (0.8，1.0] 1.0 or above Correlation coefficient threshold R 0.85 0.90 0.95 0.98

[0015] The evaluation parameters α in Table 1 i According to the actual project, calculate according to formula (7): In the formula, A i is the project area; S i B is the effective area monitored by the sensor; i is the engineering monitoring demand parameter, which is selected according to Table 2; ω1 and ω2 are the weights of the monitoring area ratio and the monitoring demand, and the sum of ω1 and ω2 is 1.0;

[0016] Table 2: Engineering monitoring requirement parameters B i

[0017] Step 6: Set up the IoT gateway and data transmission module: upload the stress and displacement data collected by the sensor measurement points to the cloud monitoring platform; deploy the gateway for vibrating string sensors and NB-IoT transmission.

[0018] The beneficial effects achieved by the present invention are as follows: the sensor layout scheme can be optimized, thereby improving the monitoring efficiency of the curved steel structure roof under the Internet of Things technology; the space utilization rate and data accuracy of the curved steel structure roof monitoring system can be effectively improved, the sensor layout cost can be reduced, and the safety and stability of the structure can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of an embodiment of the present invention.

[0020] Figure 2 It is a three-dimensional schematic diagram of the curved steel structure roof according to an embodiment of the present invention.

[0021] In the figure: 1 main beam, 2 secondary beam, 3 sensor arrangement measuring points. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] Example: Figure 1 This is a process flow of a curved steel structure roof sensor arrangement method based on Internet of Things technology described in an embodiment of the present invention.

[0024] The first step is to establish a curved steel structure roof model: in this embodiment, the roof length L = 50m, the width W = 30m; according to the finite element analysis model of the standard load combination, the maximum deflection of the roof is obtained to be 15mm.

[0025] The second step is to calculate the deflection parameter α of each component under the standard load combination according to formula (1): f Take a roof beam as an example, its length l0 = 10m, and the maximum deflection f max =15mm, then:

[0026] The third step is to determine the candidate measuring points of the sensor: according to the deflection parameter α fIn descending order, determine the 6 components where sensors are arranged, set the point with the maximum deflection value in the mid-span of the component as the candidate measuring point for the displacement sensor, and set the two ends of the component as the candidate measuring points for the stress sensor; there are 6 displacement sensors and 12 stress sensors in total, and the total number of candidate measuring points is 18.

[0027] The fourth step is to calculate the correlation coefficient between the candidate sensor measurement points: apply uniformly distributed loads of 0.3kN, 0.4kN, and 0.5kN to the finite element analysis model of the curved steel structure roof, and analyze the results of three sets of simulation data. Here, six candidate displacement sensor measurement points are taken as an example, labeled D1-D6, and the mean of the data is calculated. Deviation M m , and the covariance Cov(D i , D j ), standard deviation σD m And the correlation coefficients are listed in Table 3:

[0028] Table 3 Covariance, standard deviation and correlation coefficient of candidate measurement points of displacement sensor

[0029] Step 5: Optimize the arrangement of sensor points: In this project, A i =30m×50m=1500m 2 , S i =18×150m 2 =2700m 2 , B i =2, ω1=ω2=0.5, then the evaluation parameters According to Table 1, the correlation coefficient threshold R is 0.8, so displacement sensors D1 and D3 are eliminated, and displacement sensors D2, D4, D5, and D6 are retained;

[0030] Step 6: Set up the IoT gateway and data transmission module: upload the stress and displacement data collected by the sensor measurement points to the cloud monitoring platform; deploy the gateway for vibrating string sensors and NB-IoT transmission.

[0031] Although the specific calculation method of the present invention is described above in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Technical personnel in the relevant field should understand that various modifications or variations that can be made by technical personnel in the field without creative work on the basis of the design of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for arranging sensors on a curved steel structure roof based on Internet of Things technology, characterized in that: The following steps are included: The first step is to establish a curved steel structure roof model: a finite element analysis model is established according to the geometric shape of the curved steel structure roof to obtain the deflection curve of the standard load combination of the curved steel structure roof; The second step is to determine the components to be tested: Calculate the deflection parameter α of each component under the standard load combination according to formula (1): f : In the formula, f max is the maximum deflection value of the component, l is the length of the straight line projected on the component plane; According to the descending order of deflection parameters, determine the component to be tested where the sensor is arranged; The third step is to determine the candidate measuring points of the sensors: the point with the maximum deflection value in the mid-span of the component to be measured is set as the candidate measuring point of the displacement sensor, and the two ends of the component are set as the candidate measuring points of the stress sensor; the total number of candidate measuring points of the sensors is m, m=m1+m2, where m1 is the number of displacement sensors and m2 is the number of stress sensors; The fourth step is to calculate the correlation coefficient between the candidate sensor points: Analyze the simulation data of the candidate sensor points and calculate the mean stress and displacement of each candidate point according to formula (2): Where mi is the number of sensors, D mi is the simulated data of the mi-th candidate measuring point; Calculate the deviation M of each candidate measurement point data according to formula (3): m : According to formula (4) and formula (5), the covariance Cov(D i , D j ) and standard deviation σD m : According to formula (6), the correlation coefficient r(D i , D j ) Step 5: Optimize the arrangement of sensor measuring points: Determine the correlation coefficient threshold R of the sensor arrangement according to Table 1, merge and optimize the candidate sensor measuring points whose correlation exceeds the threshold, and determine the sensor measuring points; Table 1: Correlation coefficient threshold R selection table The evaluation parameters α in Table 1 i According to the actual project, calculate according to formula (7): In the formula, A i is the project area; S i B is the effective area monitored by the sensor; i is the engineering monitoring demand parameter, which is selected according to Table 2; ω1 and ω2 are the weights of the monitoring area ratio and the monitoring demand, and the sum of ω1 and ω2 is 1.0; Table 2: Monitoring requirement parameter selection table Step 6: Set up the IoT gateway and data transmission module: upload the stress and displacement data collected by the sensor measurement points to the cloud monitoring platform; Deployment of vibrating string sensors and gateways for NB-IoT transmission.

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