Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models
Abstract
:1. Introduction
1.1. Related Works and Challenges
1.2. Objectives
1.3. Contributions
2. Long-Span Bridges
2.1. Dashengguan Bridge
2.2. Lupu Bridge
2.3. Rainbow Bridge
3. Correlation Analysis
4. Supervised Regression Models
4.1. Linear Regression Model
4.2. Gaussian Process Regression
4.3. Supervised Vector Regression
5. Results
5.1. Dashengguan Bridge
5.2. Lupu Bridge
5.3. Rainbow Bridge
5.4. Discussions on Sufficiency of Environmental/Operational Sensors
6. Conclusions
- (1)
- When any environmental data are available, it is necessary to perform a correlation analysis to realize relationships between the structural responses. Based on the four correlation analysis methods investigated in this paper, the proposed MIC method provided more reasonable results than the other ones due to its consideration of both the linear and nonlinear correlation patterns.
- (2)
- In the problem of the Dashengguan Bridge, where the single measured environmental factor (temperature) and SAR-based displacement data had a strong linear correlation, Kendall’s correlation coefficient could not yield appropriate outputs as good as the MIC, Pearson’s, and Spearman’s correlation coefficient methods. Hence, this correlation measure can be disregarded in further applications.
- (3)
- The supervised regression techniques could perform well when there is a high correlation between the displacement and temperature data. These techniques failed in providing accurate and reliable results when the temperature and displacement data had a low correlation. This was most likely due to the fact that the other unmeasured environmental and/or operational conditions or even structural damage impacted the displacement data. Since such conditions were not incorporated into the supervised regression models, those could not properly predict the measured (real) displacement data.
- (4)
- The low correlation rates and poor prediction performances mean that the environmental/operational sensors (i.e., temperature sensors in this research) in a bridge structure are not sufficient, and one needs to consider further sensors for measuring other environmental and/or operational conditions such as humidity, wind speed and direction, traffic, etc. Moreover, it is important to investigate the possibility of existing any structural damage by visual inspection or tried-and-test techniques for early damage assessment.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Pier No. | MIC | Correlation Coefficient Metrics | ||
---|---|---|---|---|
Pearson | Spearman | Kendall | ||
4 | 1.00 | −0.9928 | −0.9931 | −0.9507 |
5 | 1.00 | −0.9899 | −0.9896 | −0.9310 |
6 | 1.00 | −0.9776 | −0.9822 | −0.9064 |
8 | 1.00 | 0.9850 | 0.9901 | 0.9359 |
9 | 1.00 | 0.9943 | 0.9940 | 0.9507 |
10 | 1.00 | 0.9877 | 0.9876 | 0.9211 |
Component | Correlation Coefficient Metrics | |||
---|---|---|---|---|
MIC | Pearson | Spearman | Kendall | |
Dome | 0.56 | −0.4555 | −0.4925 | −0.3285 |
Span | 0.90 | −0.8689 | −0.8483 | −0.6557 |
Elements | MIC | Correlation Coefficient Metrics | ||
---|---|---|---|---|
Pearson | Spearman | Kendall | ||
Pier 1 | 0.72 | 0.6081 | 0.6289 | 0.4296 |
Pier 2 | 0.61 | 0.4194 | 0.4151 | 0.2946 |
Pier 3 | 0.33 | 0.2521 | 0.2439 | 0.1625 |
Pier 4 | 0.39 | 0.3745 | 0.3612 | 0.2481 |
Span 1 | 0.75 | −0.7140 | −0.7184 | −0.5326 |
Span 2 | 0.70 | −0.6793 | −0.7005 | −0.5195 |
Span 3 | 0.74 | −0.7165 | −0.7281 | −0.5442 |
Bridge Name | Elements | Correlation Rate | Prediction Accuracy | Decision | |
---|---|---|---|---|---|
Linear | Nonlinear | ||||
Dashengguan | Piers 4–6 & 8–10 | High | High | High | Sufficient |
Lupu | Dome | Low | Low | Low | Insufficient |
Span | High | High | High | Sufficient | |
Rainbow | Pier 1 | Low | Low | Low | Insufficient |
Pier 2 | Low | Low | High * | Sufficient * | |
Pier 3 | Low | Low | High * | Sufficient * | |
Pier 4 | Low | Low | Low | Insufficient | |
Span 1 | High | High | Low | Insufficient ** | |
Span 2 | High | High | Low | Insufficient ** | |
Span 3 | High | High | Low | Insufficient ** |
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Behkamal, B.; Entezami, A.; De Michele, C.; Arslan, A.N. Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models. Remote Sens. 2023, 15, 3503. https://doi.org/10.3390/rs15143503
Behkamal B, Entezami A, De Michele C, Arslan AN. Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models. Remote Sensing. 2023; 15(14):3503. https://doi.org/10.3390/rs15143503
Chicago/Turabian StyleBehkamal, Bahareh, Alireza Entezami, Carlo De Michele, and Ali Nadir Arslan. 2023. "Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models" Remote Sensing 15, no. 14: 3503. https://doi.org/10.3390/rs15143503