Evaluation of the effect of ambient temperature on the natural frequency of bridge structures (Case study: Tehran)

Khooyeh, Hadiseh; Mahmoudi Sahebi, Mussa; Zayeri Baghlani Nejad, Amir

doi:10.22065/jsce.2024.433105.3316

Evaluation of the effect of ambient temperature on the natural frequency of bridge structures (Case study: Tehran)

Document Type : Original Article

Authors

Hadiseh khooyeh ¹

Mussa Mahmoudi Sahebi ²

amir zayeri baghlani nejad ³

¹ M.A student, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

² Professor, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

³ Assistant Professor, Faculty of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran

10.22065/jsce.2024.433105.3316

Abstract

In many bridge health monitoring systems, the change in natural frequency is considered as an important warning for the existence or initiation of damage in the structure. Meanwhile, the ambient temperature can have a significant effect on the natural frequency of the bridge. If the investigated bridge is located in an area with extreme temperature changes, the measured natural frequency may show different values at different times and thus cause errors in the damage detection system. This issue reveals the necessity of studying and investigating the effect of temperature on the natural frequency of bridges in different geographical regions with different climates. This article studies the effect of temperature on the natural frequency of bridges in Tehran metropolis. In this regard, the natural frequency of three steel bridges in the city and one concrete bridge located on highway Road in the outskirts of the city was measured during one year and at different hours of the day and night. To do this, the sensors embedded in smartphone and a sensitive accelerometer were used. In the next step, the frequencies obtained from the experiments were analyzed and studied statistically. Examining the results showed that the environmental temperature changes in Tehran can cause frequency changes of about 12% for steel bridges and frequency changes of about 0.5% for concrete bridges. The results of this research show that considering the effect of temperature in health monitoring systems based on modal information for bridges with steel materials is much more important than existing concrete bridges in Tehran.

Keywords

Modal analysis

Natural frequency

Bridge vibration

Temperature

Accelerometer

Subjects

Dynamic Analysis and Seismic Design of Structures

[1] Basten, T. G. H., & Schiphorst, F. B. A. (2012). Structural health monitoring with a wireless vibration sensor network. In: Proceedings of the International Conference on Noise and Vibration Engineering, ISMA (pp. 17-19).

[2] Liang, Y., Li, D., Song, G., & Feng, Q. (2018). Frequency Co-integration-based damage detection for bridges under the influence of environmental temperature variation. Measurement, 125, 163-175.

[3] Moorty, S., & Roeder, C. W. (1992). Temperature-dependent bridge movements. Journal of Structural Engineering, 118(4), 1090-1105.

[4] Doebling, S. W., & Farrar, C. R. (1997). Using statistical analysis to enhance modal-based damage identification. In: Proc. DAMAS (Vol. 97, pp. 199-210).

[5] Askegaard, V., & Mossing, P. (1988). Long term observation of RC-bridge using changes in natural frequency. nordic concrete research. publication no 7. Publication of: NORDIC CONCRETE FEDERATION.

[6] Sohn, H. (2007). Effects of environmental and operational variability on structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851), 539-560.

[7] Ni, Y. Q., Zhou, H. F., Chan, K. C., & Ko, J. M. (2008). Modal flexibility analysis of cable‐stayed Ting Kau Bridge for damage identification. Computer‐Aided Civil and Infrastructure Engineering, 23(3), 223-236.

[8] PEETERS, B. (2000). System Identification and Damage Detection in Civil Engineering. Ph.D. Dissertation, Katholieke University.

[9] Peeters, B., & De Roeck, G. (2001). One‐year monitoring of the Z24‐Bridge: environmental effects versus damage events. Earthquake engineering & structural dynamics, 30(2), 149-171.

[10] Manson, G. (2002, April). Identifying damage sensitive, environment insensitive features for damage detection. In: Proceedings of the third international conference on identification in engineering systems (pp. 187-197).

[11] Kullaa, J. (2003). Is temperature measurement essential in structural health monitoring?. In: Proceedings of the 4th International Workshop on Structural Health Monitoring, Stanford, CA., September 1517, 2003 (p. 717724).

[12] Nguyen, V. H., Mahowald, J., Schommer, S., & Maas, S. (2017). A study of temperature and aging effects on eigenfrequencies of concrete bridges for health monitoring. Engineering, 9, 396-411.

[13] Liang, Y., Li, D., Song, G., & Feng, Q. (2018). Frequency Co-integration-based damage detection for bridges under the influence of environmental temperature variation. Measurement, 125, 163-175.

[14] Zheng, W., Qian, F., Shen, J., & Xiao, F. (2020). Mitigating effects of temperature variations through probabilistic-based machine learning for vibration-based bridge scour detection. Journal of Civil Structural Health Monitoring, 10, 957-972.

[15] Borah, S., Al-Habaibeh, A., & Kromanis, R. (2021). The Effect of Temperature Variation on Bridges—A Literature Review. Energy and Sustainable Futures: Proceedings of 2nd ICESF 2020, 207-212.

[16] Yu, Y., Han, R., Zhao, X., Mao, X., Hu, W., Jiao, D., & Ou, J. (2015). Initial validation of mobile-structural health monitoring method using smartphones. International Journal of Distributed Sensor Networks, 11(2), 274391.

[17] Kong, Q., Allen, R. M., Kohler, M. D., Heaton, T. H., & Bunn, J. (2018). Structural health monitoring of buildings using smartphone sensors. Seismological Research Letters, 89(2A), 594-602.

[18] Elhattab, A., Uddin, N., & OBrien, E. (2019). Extraction of bridge fundamental frequencies utilizing a smartphone MEMS accelerometer. Sensors, 19(14), 3143.

[19] Sony, S., Laventure, S., & Sadhu, A. (2019). A literature review of next‐generation smart sensing technology in structural health monitoring. Structural Control and Health Monitoring, 26(3), e2321.

[20] Sarmadi, H., Entezami, A., Yuen, K. V., & Behkamal, B. (2023). Review on smartphone sensing technology for structural health monitoring. Measurement, 223, 113716.

[21] Halling, M. W., Ball, A., Esplin, R., & Hsieh, K. H. (2004). Modal analysis and modeling of highway bridges. In: 13th World Conference on Earthquake Engineering, Paper (No. 2996).

[22] Wenzel, H., Pichler, D. (2009). Ambient vibration monitoring. Encyclopedia of Structural Health Monitoring, John Wiley & Sons.

[23] Brincker, R., Zhang, L., & Andersen, P. (2001). Modal identification of output-only systems using frequency domain decomposition. Smart materials and structures, 10(3), 441.

[24] Peeters, B., De Roeck, G., Pollet, T., & Schueremans, L. (1995). Stochastic subspace techniques applied to parameter identification of civil engineering structures. In: Proceedings of new advances in modal synthesis of large structures: nonlinear, damped and nondeterministic cases (pp. 151-162).

[25] ARTeMIS Modal, Structural Vibration Solutions A/S, http://www.svibs.com.

[26] Zayeri Baghlani Nejad, A., & Mahmoudi Sahebi, M. (2020). Determination of the Modal Information of Structures under Impact Loads Using Proper Orthogonal Decomposition. Bulletin of Earthquake Science and Engineering, 7(4), 129-145, (In Persian).

[27] Accelerometer, Keuwlsoft, https://keuwl.com/Accelerometer.

[28] Zayeri Baghlani Nejad A, Mahmoudi, M. (2020). A new method for determining the natural frequencies of structures from their ambient vibration. Modares Civil Engineering journal, 20(5), 89-102, (In Persian).

[29] Jiang, Q., Chang, F., & Liu, C. (2021). A spectrogram based local fluctuation feature for fault diagnosis with application to rotating machines. Journal of Electrical Engineering & Technology, 16, 2167-2181.

[30] Oppenheim, A. V. (1999). Discrete-time signal processing. Prentice Hall google schola, 3, 804-