Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction
Abstract
:1. Introduction
2. Methodology
2.1. Beidou Positioning Receiver
- (1)
- Theoretical Model
- (2)
- Practical Model
2.2. Bridge Health Monitoring System
- (1)
- Beidou high-precision positioning acquisition sensors and supporting equipment are installed in key parts of the bridge, and the surrounding Beidou reference station is built to establish a stable monitoring system to continuously collect and analyze the operation status and deformation information of the bridge.
- (2)
- Based on the optical fiber communication mode, transmit the collected bridge geometric spatial data and the stable reference coordinates of the Beidou reference station to the bridge cloud computer room for data processing and storage backup.
- (3)
- Implement perfect software functions, intuitive data display and publishing functions, convenient query functions, and statistical and automatic report functions to ensure the scalability and stability of the hardware system.
- (4)
- The data input interface of the bridge Internet of Things safety monitoring system combines various types of sensor monitoring data with Beidou safety monitoring data to control the operational safety of the bridge by combining macroscopic deformation and microscopic structural data.
- (5)
- Provide exclusive cloud services for the bridge to assure the operating environment and security of data processing and analysis software, realize the storage and backup of bridge data, form a bridge operation big data resource pool, and ensure the effective operation of the entire safety monitoring system.
2.3. Base Threshold
2.3.1. Generalized Extreme Value Distribution
- (1)
- Gumbel Distribution Type
- (2)
- Frechet Distribution Type
- (3)
- Weibull Distribution Type
2.3.2. Generalized Pareto Distribution(GPD)
- (1)
- Mean Excess Function
- (2)
- Return level
- (3)
- Generalized Pareto Distribution
- (4)
- Parameter estimation of generalized Pareto distribution
2.4. Temperature Effect Prediction Based on the SARIMA Model
2.4.1. Autoregressive Moving Average (ARMA)
2.4.2. SARIMA model
3. Implementation of the Prediction and Early Warning
3.1. Experiment Design and Data Collection
3.2. Determination of Baseline Threshold
3.3. Prediction of Temperature Effects
4. Discussion
5. Conclusions
- (1)
- Based on the bridge GNSS real-time monitoring data, a dynamic early warning method for the existing cable-stayed bridge safety service monitoring platform is proposed. The static threshold monitoring process has been omitted due to the weather, while the dynamic threshold monitoring system can realize the dynamic adjustment of the threshold according to the temperature change, in addition to the significant over-threshold alarm detected at 04:26:26 on 4 July 2021, local mutations at other time points can also be effectively monitored. A total of 170 over-threshold alerts were detected at other time points, and its monitoring results are 17 times higher than the static threshold. And, the dynamic monitoring results are consistent with the manual inspection.
- (2)
- When the traditional interval extreme value method is used to fit the extreme value data, the extreme value information contained cannot be fully utilized. GPD is based on the threshold method to select the extreme value, which overcomes the disadvantage that the extreme value information is not fully utilized. The selection of the threshold plays a decisive role in the fitting effect. Therefore, based on a variety of fitting test indicators, principal component analysis is used to obtain a comprehensive indicator to determine the optimal threshold. After determining the optimal threshold, the corresponding shape parameters and scale parameters are obtained by maximum likelihood estimation. The cumulative product probability density diagram and Q-Q diagram show that the extreme values are well fitted.
- (3)
- The GNSS monitoring system has a total of 46 measuring point data, and the monitoring data of the mid-span BD12 measuring point are selected as the research object. In order to realize the real-time dynamic monitoring of bridge GNSS, the temperature effect of the historical monitoring data of last month is used as the training sample. SARIMA is used to model the training samples to predict the temperature effect on the next day and dynamically adjust the baseline threshold. In the white noise test of the residual, if the residual is not white noise, a second prediction is required until the residual meets the white noise requirements. The results show that the relative error between the predicted value and the true value is 0.9932, and the prediction effect of the temperature effect is accurate.
- (4)
- In daily monitoring, due to the interference of the environment, the equipment may not work properly, resulting in abnormal data, which may lead to errors in the analysis results. Therefore, in the follow-up study, the elimination of abnormal data can be considered.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
List of Alphabetical Symbols
Notation | Meaning |
Non-degenerate distribution function | |
Distribution function | |
Test index value | |
The actual value of function | |
Location parameter | |
Scale parameter | |
Shape parameter | |
Autoregressive coefficients | |
Error term | |
Order of the autoregressive model | |
Moving average coefficients | |
Order of the autoregressive model | |
Threshold value | |
Probability density function | |
Standardized value | |
Estimated value of fitting | |
p | Autoregressive order |
d | Non-seasonal difference order |
q | Moving average order |
P | Seasonal autoregressive order |
D | Seasonal difference order |
Q | Seasonal moving average order |
s | Period |
B | Backward shift operator |
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Characteristics | Basic Explanation |
---|---|
Standard single point positioning accuracy | Single frequency: H ≤ 5 m, V ≤ 5 m (1σ, PDOP ≤ 4) Dual frequency: H ≤ 3 m, V ≤ 3 m (1σ, PDOP ≤ 4) |
Static differential accuracy | Level: ±(2.5 + 1 × 10−6 × D) mm Vertical: ±(5 + 1 × 10−6 × D) mm |
RTK accuracy | Level: ±(8 + 0.5 × 10−6 × D) mm Vertical: ±(15 + 0.5 × 10−6 × D) mm |
E-RTK accuracy | Level: ±(200 + 1 × 10−6 × D) mm Vertical: ±(400 + 1 × 10−6 × D) mm |
Signal tracking | Channel number 440 BDS global signal support |
Accuracy/reliability | The speed measurement accuracy is 0.03 m/s, and the initial confidence level is >99.9%. signal recapture < 1.5 s (fast), <3 s (ordinary) |
Data format | Standard NMEA-0183/Supports GPGGA |
Communication protocol | RS232 serial port, TCP/IP |
Electrical indicators | Material: sturdy and lightweight metal packaging size: 209 × 145 × 78 mm |
Physical properties | Working temperature: −40 °C~+ 70 °C Storage temperature: −55 °C~+ 95 °C Humidity: 100% fully sealed, anti-condensation, floatable |
Threshold | Composite Indicator | Ranking | Threshold | Composite Indicator | Ranking |
---|---|---|---|---|---|
181 | 1.1300 | 1 | 239 | 1.0982 | 64 |
179 | 1.1289 | 2 | 233 | 1.0976 | 65 |
180 | 1.1289 | 3 | 238 | 1.0973 | 66 |
235 | 1.0985 | 63 | 296 | 0.9336 | 126 |
Measurement Point | Alarm Time | Occurrence Count | Measurement Point | Alarm Time | Occurrence Count |
---|---|---|---|---|---|
BD12 | 2021-07-11 12:24:43 | 3 | BD12 | 2021-07-23 12:39:40 | 4 |
BD12 | 2021-07-14 04:26:26 | 10 | BD12 | 2021-07-24 12:19:19 | 27 |
BD12 | 2021-07-18 17:35:22 | 35 | BD12 | 2021-07-28 12:29:02 | 1 |
BD12 | 2021-07-18 17:36:16 | 7 | BD12 | 2021-07-29 11:13:31 | 51 |
BD12 | 2021-07-18 17:41:28 | 13 | BD12 | 2021-07-31 11:09:28 | 6 |
BD12 | 2021-07-19 11:26:05 | 6 | BD12 | 2021-07-31 11:10:20 | 5 |
BD12 | 2021-07-21 14:58:11 | 2 |
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Share and Cite
Tan, D.; Guo, T.; Luo, H.; Ji, B.; Tao, Y.; Li, A. Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction. Sensors 2023, 23, 8826. https://doi.org/10.3390/s23218826
Tan D, Guo T, Luo H, Ji B, Tao Y, Li A. Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction. Sensors. 2023; 23(21):8826. https://doi.org/10.3390/s23218826
Chicago/Turabian StyleTan, Dongmei, Tai Guo, Hao Luo, Baifeng Ji, Yu Tao, and An Li. 2023. "Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction" Sensors 23, no. 21: 8826. https://doi.org/10.3390/s23218826
APA StyleTan, D., Guo, T., Luo, H., Ji, B., Tao, Y., & Li, A. (2023). Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction. Sensors, 23(21), 8826. https://doi.org/10.3390/s23218826