Structural Damage Diagnosis of Reinforced Concrete Moment-Resisting Frame using Wavelet Based-Damage Sensitive Feature

Panahi Boroujeni, Minoo; Zareei, Seyed Alireza; Birzhandi, Mohammad Sadegh; Zafarani, Mohammad Mahdi

doi:10.61186/NMCE.2312.1038

Structural Damage Diagnosis of Reinforced Concrete Moment-Resisting Frame using Wavelet Based-Damage Sensitive Feature

Document Type : Research

Authors

Minoo Panahi Boroujeni ¹

Seyed Alireza Zareei ²

Mohammad Sadegh Birzhandi ³

Mohammad Mahdi Zafarani ³

¹ Ph.D. Candidate of Structural Engineering, Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

² Associate Professor of Structural Engineering, Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

³ Assistant Professor of Structural Engineering, Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

10.61186/NMCE.2312.1038

Abstract

This paper describes the derivation of a wavelet-based damage-sensitive feature for structural damage diagnosis in a reinforced concrete moment-resisting frame. The wavelet-based damage-sensitive feature is based on the wavelet transform of the time-history acceleration response, obtained from incremental dynamic analysis of the floor absolute acceleration response. By applying mathematical relationships to the wavelet coefficients, the wavelet-based damage-sensitive feature is extracted as the energy of the wavelet coefficients at appropriate scales. Furthermore, the theoretical correlation between the output of wavelet analysis and dynamic structural behaviors that play a vital role in damage diagnosis methods was derived. The effectiveness of the wavelet-based damage-sensitive feature is evaluated by simulating a 4-story reinforced concrete frame subjected to various ground motion records using the continuous bior3.3 mother wavelet family. The wavelet-based damage-sensitive feature measures changes in wavelet energy at the natural frequency of the undamaged structure as damage progresses, and the results show that wavelet-based damage-sensitive feature values gradually decrease as damage increases.

Keywords

Damage Sensitive Feature

Incremental Dynamic Analysis

Wavelet

Reinforced Concrete Moment-Resisting Frames

Absolute Acceleration Response

Subjects

Structural Engineering

[1] Noh, H. Y., Nair, K. K., Lignos, D. G. & Kiremidjian, A. S. 2011. Use of wavelet-based damage-sensitive features for structural damage diagnosis using strong motion data. Journal of Structural Engineering, 137, 1215-1228.

[2] Yazdanpanah, O., Mohebi, B. & Yakhchalian, M. 2020. Selection of optimal wavelet-based damage-sensitive feature for seismic damage diagnosis. Measurement, 154, 107447.

[3] Mahin, S. A. 1998. Lessons from damage to steel buildings during the Northridge earthquake. Engineering structures, 20, 261-270.

[4] Nair, K. & Kiremidjian, A. 2007. Damage diagnosis algorithm for wireless structural health monitoring. Rep. No. 165. John A. Blume Earthquake Engineering Center: Department of Civil Engineering, Stanford University.

[5] Bayissa, W., Haritos, N. & Thelandersson, S. 2008. Vibration-based structural damage identification using wavelet transform. Mechanical systems and signal processing, 22, 1194-1215.

[6] Panahi Boroujeni, M., Zareei, S.A., Birzhandi, M.S. & Zafarani, M.M. 2024. Development of Seismic Fragility Functions for Reinforced Concrete Buildings Using Damage‐Sensitive Features Based on Wavelet Theory. Structural Control and Health Monitoring, 2024(1), 8754191-8754212.

[7] Fan, W. & Qiao, P. 2011. Vibration-based damage identification methods: a review and comparative study. Structural health monitoring, 10, 83-111.

[8] Hera, A. & Hou, Z. 2004. Application of wavelet approach for ASCE structural health monitoring benchmark studies. Journal of Engineering Mechanics, 130, 96-104.

[9] Nair, K. K. & Kiremidjian, A. S. 2009. Derivation of a damage sensitive feature using the Haar wavelet transform. Journal of Applied Mechanics, 76, 061015-061024.

[10] Aguirre, D. A., Gaviria, C. A. & Montejo, L. A. 2013. Wavelet-based damage detection in reinforced concrete structures subjected to seismic excitations. Journal of Earthquake Engineering, 17, 1103-1125.

[11] Balafas, K. & Kiremidjian, A. S. 2015. Development and validation of a novel earthquake damage estimation scheme based on the continuous wavelet transform of input and output acceleration measurements. Earthquake Engineering & Structural Dynamics, 44, 501-522.

[12] Mallat, S. 1999. A wavelet tour of signal processing, Elsevier.

[13] Cruz, P. J. & Salgado, R. 2009. Performance of vibration‐based damage detection methods in bridges. Computer‐Aided Civil and Infrastructure Engineering, 24, 62-79.

[14] Solís, M., Algaba, M. & Galvín, P. 2013. Continuous wavelet analysis of mode shapes differences for damage detection. Mechanical Systems and Signal Processing, 40, 645-666.

[15] Vamvatsikos, D. & Cornell, C. A. 2002. Incremental dynamic analysis. Earthquake engineering & structural dynamics, 31, 491-514.

[16] Vamvatsikos, D. 2002. Seismic performance, capacity and reliability of structures as seen through incremental dynamic analysis. Ph.D. dissertation, Stanford University.

[17] Noh, H., Nair, K., Lignos, D. & Kiremidjian, A. Application of Wavelet Coefficient Energies of Stationary and Non-Stationary Response Signals for Structural Damage Diagnosis. Proceedings of the 7th International Workshop on Structural Health Monitoring, Stanford, CA, USA, 2009. 9-11.

[18] Nair, K. K. & Kiremidjian, A. S. 2009. Derivation of a damage sensitive feature using the Haar wavelet transform.

[19] Hou, Z., Noori, M. N. & St Amand, R. 2000. Wavelet-based approach for structural damage detection. Journal of Engineering mechanics, 126, 677.

[20] Ghanem, R. & Romeo, F. 2000. A wavelet-based approach for the identification of linear time-varying dynamical systems. Journal of sound and vibration, 234, 555-576.

[21] Jamshidi Chenari, R. & Izadi., A. 2019. Discussion of “An Analytical Probabilistic Analysis of Slopes Based on Limit Equilibrium Methods” by A. Johari, S. Mousavi, November 2018. Bulletin of Engineering Geology and the Environment, 78, 5511–5515.

[22] Noh, H., Nair, K., Lignos, D. & Kiremidjian, A. Application of Wavelet Coefficient Energies of Stationary and Non-Stationary Response Signals for Structural Damage Diagnosis. Proceedings of the 7th International Workshop on Structural Health Monitoring, 2009 Stanford, CA, USA. 9-11.

[23] Mikami, S., Beskhyroun, S. & Oshima, T. 2011. Wavelet packet based damage detection in beam-like structures without baseline modal parameters. Structure and Infrastructure Engineering, 7, 211-227.

[24] Hwang, S.-H. & Lignos, D. G. 2018. Assessment of structural damage detection methods for steel structures using full-scale experimental data and nonlinear analysis. Bulletin of Earthquake Engineering, 16, 2971-2999.

[25] Noh, H. 2011. Damage diagnosis algorithms using statistical pattern recognition for civil structures subjected to earthquakes, Stanford University.

[26] Noh, H., Krishnan Nair, K., Lignos, D. G. & Kiremidjian, A. S. 2011. Use of wavelet-based damage-sensitive features for structural damage diagnosis using strong motion data. Journal of Structural Engineering, 137, 1215-1228.

[27] Yazdanpanah, O., Mohebi, B. & Yakhchalian, M. 2020. Seismic damage assessment using improved wavelet-based damage-sensitive features. Journal of Building Engineering, 31, 101311.

[28] Lignos, D. 2008. Sidesway collapse of deteriorating structural systems under seismic excitations, Stanford University.

[29] Noh, H. Y., Lignos, D. G., Nair, K. K. & Kiremidjian, A. S. 2012. Development of fragility functions as a damage classification/prediction method for steel moment‐resisting frames using a wavelet‐based damage sensitive feature. Earthquake Engineering & Structural Dynamics, 41, 681-696.

[30] Hwang, S.-H. & Lignos, D. G. 2018. Nonmodel-based framework for rapid seismic risk and loss assessment of instrumented steel buildings. Engineering Structures, 156, 417-432.

[31] Noh, H. Y. 2013. Damage diagnosis algorithms using statistical pattern recognition for civil structures subjected to earthquakes. Ph.D. dissertation, Stanford University.

[32] Haselton, C. B. & Deierlein, G. G. 2008. Assessing seismic collapse safety of modern reinforced concrete moment frame buildings. PEER Report 2007/08. Pacific Earthquake Engineering Research Center College of Engineering: University of California.

[33] ICC 2003. International Building Code 2003. International Code Council, University of Michigan, VA.

[34] ASCE 2002. Minimum Design Loads for Buildings and Other Structures; ASCE-7-02. American Society of Civil Engineers, Reston, VA.

[35] ACI 2002. Building Code Requirements for Structural Concrete (ACI 318-02) and Commentary (ACI 318R-02). American Concrete Institute, Farmington Hills, MI.

[36] McKenna, F. 2011. OpenSees: a framework for earthquake engineering simulation. Computing in Science & Engineering, 13, 58-66.

[37] Ibarra, L. F. 2004. Global collapse of frame structures under seismic excitations, Stanford University.

[38] Medina, R. A. & Krawinkler, H. 2005. Evaluation of drift demands for the seismic performance assessment of frames. Journal of Structural Engineering, 131, 1003-1013.

[39] Moehle, J. P., Mahin, S. & Bozorgnia, Y. 2010. Modeling and acceptance criteria for seismic design and analysis of tall buildings. PEER Report 2010, 111.

[40] Lignos, D. & Krawinkler, H. 2009. Sidesway collapse of deteriorating structural systems under seismic excitations. Report No. TB 172, John A. Blume Earthquake Engineering Research Center, Department of Civil and Environmental Engineering. Department of Civil and Environmental Engineering.

[41] Pakzad, S. N. & Fenves, G. L. 2009. Statistical analysis of vibration modes of a suspension bridge using spatially dense wireless sensor network. Journal of Structural Engineering, 135, 863-872.

[42] Hwang, S.-H. 2017. Framework for earthquake-induced loss assessment of steel frame buildings-from building-specific to city-scale approaches. Ph.D. dissertation, McGill University (Canada).

[43] Pappa, R. S., Elliott, K. B. & Schenk, A. 1993. Consistent-mode indicator for the eigensystem realization algorithm. Journal of Guidance, Control, and Dynamics, 16, 852-858.

[44] Agency, F. E. M. 2012. Multi-hazard loss estimation methodology HAZUS–MH 2.1 advanced engineering building module (AEBM) technical and user’s manual. Federal Emergency Management Agency Washington, DC.