Identification of Dynamic Parameters of a Precast Beam System by Vibration Recording

Authors

  • Germán N. Lucero Universidad Nacional de Rosario, Facultad Ciencias Exactas, Ingeniería y Agrimensura, Instituto de Mecánica Aplicada y Estructuras (IMAE). Rosario, Argentina.
  • Juan P. Ascheri Universidad Nacional de Rosario, Facultad Ciencias Exactas, Ingeniería y Agrimensura, Instituto de Mecánica Aplicada y Estructuras (IMAE). Rosario, Argentina.
  • Oscar Möller Universidad Nacional de Rosario, Facultad Ciencias Exactas, Ingeniería y Agrimensura, Instituto de Mecánica Aplicada y Estructuras (IMAE). Rosario, Argentina.

DOI:

https://doi.org/10.70567/mc.v42.ocsid8441

Keywords:

Structural dynamics, System identification, Physical model, Dynamic parameters

Abstract

In tests to identify the dynamic parameters of structures, it is generally necessary to determine natural frequencies and detailed mode shapes using a limited number of sensors. One strategy is to implement multiple measurement configurations, each covering a portion of the structure. The mode shapes identified in each configuration are subsequently assembled to obtain the vibration modes of the entire structure. In this work, a 16-m-span precast concrete beam excited by controlled impacts is studied, recording the acceleration over time during the free vibration phase. Measurements are made with three accelerometers. Dynamic properties are identified using time- and frequency-domain methods. The components of each measurement configuration are subsequently assembled, obtaining the mode shapes of the entire structure. This work discusses the identified mode shapes and random combinations between different impacts, comparing them with the MAC index.

References

Au, S.K., Zhang, F.L. Fast Bayesian Ambient Modal Identification Incorporating Multiple Setups. J. Eng. Mech. 138 - pp. 800-815, 2012 https//doi.org/10.1061/(ASCE)EM.1943-7889.0000385

Brincker, R., Ventura, C. E. & Andersen, P. Damping Estimation by Frequency Domain Decomposition. Society for Experimental Mechanics, Vol. 19 - pp. 698-703, 2001.

Brincker, R., Ventura, C. Introduction to operational modal analysis. John Wiley & Sons, Ltd,2015. https//doi.org/10.1002/9781118535141

Brincker, R., Zhang, L., & Andersen, P. Modal Identification from Ambient Responses using Frequency Domain Decomposition. Smart Materials and Structures. Vol.10 - pp. 441-445, 2000. https//doi.org/10.1088/0964-1726/10/3/303

Cara, F.J., Juan, J., Alarcón, E. Estimating the modal parameters from multiple measurement setups using a joint state space model. Mech. Syst. Signal Process. 43 - pp. 171-191, 2014. https//doi.org/10.1016/j.ymssp.2013.09.012

Döhler, M., Andersen, P., Mevel, L. Data Merging for Multi-Setup Operational Modal Analysis with Data-Driven SSI, 28th Int. Modal Anal. Conf. Fr. Eur. HAL CCSD, Jacksonville, Florida USA, 2010 https//doi.org/10.1007/978-1-4419-9834-7_42

Hizal, C. (2021). "Frequency domain data merging in operational modal analysis based on least squares approach". Measurement. Vol. 170, 108742. https//doi.org/10.1016/j.measurement.2020.108742

Lucero, G., Möller, O. & Ascheri, J. Identificación de propiedades dinámicas de un modelo físico reducido en un puente losa mediante registro de vibraciones. Mecánica Computacional. Vol 41 Núm 9, 2024. https//doi.org/10.70567/mc.v41i9.44

Mevel, L., Basseville, M., Benveniste, A., Goursat, M. Merging sensor data from multiple measurement set-ups for non-stationary subspace-based modal analysis. J. Sound Vib. 249 - pp. 719-741, 2002. https//doi.org/10.1006/jsvi.2001.3880

Peeters, B. System Identification and Damage Detection in Civil Engineering. Katholieke Universiteit Leuven - Faculteit Toegepaste Wetenschappen, 2000.

Peeters, B., De Roeck, G. Reference-Based Stochastic Subspace Identification For output-Only Modal Analysis. Mechanical Systems and Signal Processing. 13(6) - pp. 855-878, 1999. https//doi.org/10.1006/mssp.1999.1249

Yan, W., Papadimitriou, C., Katafygiotis, L.S., Chronopoulos, D. An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly. Mech. Syst. Signal Process. 135, 106376, 2020 https//doi.org/10.1016/j.ymssp.2019.106376

Van Overschee, P., De Moor, B. Subspace identification for linear systems - Theory - Implementation - Applications. Katholieke Universiteit Leuven, 1996. https//doi.org/10.1007/978-1-4613-0465-4

O' Connel, B. & Rogers, T. A robust probabilist approach to stochastic subspace identification. J. Sound Vib. 581 - 118381, 2024 https//doi.org/10.1016/j.jsv.2024.118381

Cho, K. & Cho, J.-R. Stochastic Subspace Identification-Based Automated Operational Modal Analysis Considering Modal Uncertainty. Appl. Sci. 13, 12274, 2023. https//doi.org/10.3390/app132212274

Mostafaei, H. Modal Identification Techniques for Concrete Dams: A Comprehensive Review and Application. Sci 6(3), 40, 2024. https//doi.org/10.3390/sci6030040

Okur, F., Altunisik, A. & Okur, E. Development and Validation of New Methodology for Automated Operational Modal Analysis Using Modal Domain Range. Structural Control and Health Monitoring, Article ID 6267884, 2025. https//doi.org/10.1155/stc/6267884

Published

2025-12-01

Issue

Section

Conference Papers in MECOM 2025

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