Cited by 22Structural Health Monitoring TechniquesAdvanced Sensor and Control SystemsSmart Materials for Construction

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Systematic method for the validation of long-term temperature measurements

Abdel-Jaber, H, Glisic, B·2016·Smart Materials and Structures

Abstract

Structural health monitoring (SHM) is the process of collecting and analyzing measurements of various structural and environmental parameters on a structure for the purpose of formulating conclusions on the performance and condition of the structure. Accurate long-term temperature data is critical for SHM applications as it is often used to compensate other measurements (e.g., strain), or to understand the thermal behavior of the structure. Despite the need for accurate long-term temperature data, there are currently no validation methods to ensure the accuracy of collected data. This paper researches and presents a novel method for the validation of long-term temperature measurements from any type of sensors. The method relies on modeling the dependence of temperature measurements inside a structure on the ambient temperature measurements collected from a reliable nearby weather tower. The model is then used to predict future measurements and assess whether or not future measurements conform to predictions. The paper presents both the model selection process, as well as the sensor malfunction detection process. To illustrate and validate the method, it is applied to data from a monitoring system installed on a real structure, Streicker Bridge on the Princeton University campus. Application of the method to data collected from about forty sensors over five years showed the potential of the method to categorize normal sensor function, as well as characterize sensor defect and minor drift.

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Research works printed but not linked: 21

  1. Bell, 2008, Long-term thermal performance of a CFRP-reinforced bridge deck, Struct. Congress, 2008, 1, 10.1061/41016(314)242 · DOI
  2. Catbas, 2002, Condition and damage assessment: issues and some promising indices, J. Struct. Eng., 128, 1026, 10.1061/(ASCE)0733-9445(2002)128:8(1026) · DOI
  3. Yuen, 2010, Ambient interference in long-term monitoring of buildings, Eng. Struct., 32, 2379, 10.1016/j.engstruct.2010.04.012 · DOI · Paper page
  4. Ubertini, 2017, Environmental effects on natural frequencies of the San Pietro bell tower in Perugia, Italy, and their removal for structural performance assessment, Mech. Syst. Signal Process., 82, 307, 10.1016/j.ymssp.2016.05.025 · DOI
  5. Magalhaes, 2012, On vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection, Mech. Syst. Signal Process., 28, 212, 10.1016/j.ymssp.2011.06.011 · DOI · Paper page
  6. Comanducci, 2016, On vibration-based damage detection by multivariate statistical techniques: application to a long-span arch bridge, Struct. Health Monit., 15, 505, 10.1177/1475921716650630 · DOI
  7. Beskhyroun, 2012, New methodology for the application of vibration-based damage detection techniques, Struct. Control Health Monit., 19, 632, 10.1002/stc.456 · DOI
  8. Peng, 2007, Analytical solution to temperature variations in highway concrete bridges due to solar radiation, 1536, 10.1061/40932(246)252 · DOI
  9. Kromanis, 2013, Support vector regression for anomaly detection from measurement histories, Adv. Eng. Inform., 27, 486, 10.1016/j.aei.2013.03.002 · DOI
  10. Laory, 2011, Evaluating two model-free data interpretation methods for measurements that are influenced by temperature, Adv. Eng. Inform., 25, 495, 10.1016/j.aei.2011.01.001 · DOI
  11. Kromanis, 2014, Predicting thermal response of bridges using regression models derived from measurement histories, Comput. Struct., 136, 64, 10.1016/j.compstruc.2014.01.026 · DOI
  12. Laory, 2013, Combined model-free data-interpretation methodologies for damage detection during continuous monitoring of structures, J. Comput. Civil Eng., 27, 657, 10.1061/(ASCE)CP.1943-5487.0000289 · DOI
  13. Yarnold, 2015, Temperature-based structural health monitoring baseline for long-span bridges, Eng. Struct., 86, 157, 10.1016/j.engstruct.2014.12.042 · DOI
  14. Reilly, 2016, Identification of steady-state uniform temperature distributions to facilitate a temperature driven method of Structural Health Monitoring, 9805, 980521, 10.1117/12.2219334 · DOI
  15. Pozzi, 2008, Development and laboratory validation of in-line multiplexed low-coherence interferometric sensors, Opt. Fiber Technol., 14, 281, 10.1016/j.yofte.2008.01.006 · DOI
  16. Cappello, 2015, Impact of prior perception on bridge health diagnosis, J. Civil Struct. Health Monit., 5, 509, 10.1007/s13349-015-0120-0 · DOI
  17. Zonta, 2014, On estimating the accuracy of monitoring methods using Bayesian error propagation technique, 9061, 60912B, 10.1117/12.2046409 · DOI
  18. Schwarz, 1978, Estimating the dimension of a model, Ann. Stat., 6, 461, 10.1214/aos/1176344136 · DOI · Paper page
  19. Abdel-Jaber, 2014, A method for the on-site determination of prestressing forces using long-gauge fiber optic strain sensors, Smart Mater. Struct., 23, 10.1088/0964-1726/23/7/075004 · DOI
  20. Sigurdardottir, 2015, On-site validation of fiber-optic methods for structural health monitoring: Streicker Bridge, J. Civil Struct. Health Monit., 5, 529, 10.1007/s13349-015-0123-x · DOI
  21. Hubbell, 2013, Detection and characterization of early age thermal cracks in high performance concrete, ACI Mater. J., 110, 323, 10.14359/51685666 · DOI

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Metadata

Authors
Abdel-Jaber, H., & Glisic, B.
Affiliations
Princeton University
Venue
Smart Materials and Structures
Published
2016-11-15
Volume
25
Issue
12
Pages
125025
ISSN
  • 0964-1726 (ISSN-L)
  • 1361-665X
OpenAlex
W2552652288
Cited by
22
References
0
Citations by year
2025: 1 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 2
FWCI
2.72
Type
journal article
Language
en
Open access
closed
Locations
Smart Materials and Structures (publishedVersion)
Funders
  • U.S. Department of Transportation (DTRT13-G-UTC28)
  • Division of Civil, Mechanical and Manufacturing Innovation (1362723)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Sensor and Control Systems
  • Physical Sciences › Engineering › Civil and Structural Engineering › Smart Materials for Construction
Keywords
measurement validation, structural health monitoring, sensor fault detection, sensor drift

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