Optimization of a Parametric Model with Vibration Recording Considering Uncertainties
DOI:
https://doi.org/10.70567/mc.v42.ocsid8459Keywords:
Uncertainties, Structural dynamics, Parameter optimization, Concrete beamsAbstract
Different methodologies have been developed for structural health monitoring (SHM), most of which are based on recording ambient vibrations. Dynamic parameters are identified from the acceleration-time recordings. A parametric numerical model is constructed, and the parameters are optimized so that the model prediction approximates the values identified from the recorded measurements. Uncertainties in the physical characteristics of the structure, such as dimensions, material properties, and boundary conditions, are considered, as well as uncertainties due to measurement errors and approximations of the identification methods. This work investigates a structure consisting of 16-m-span precast beams. One of the beams is excited by controlled impacts and recording the acceleration-time history in the free vibration stage. From the multiple recordings, frequency statistics are obtained using two system identification methods. In the optimization process, the square of the relative difference between the model and identified frequencies is minimized. A probabilistic model is thus constructed to study the stochastic behavior of the analyzed structure.
References
Brincker, R., Ventura, C., Introduction to operational modal analysis. John Wiley & Sons, Ltd, 2015.
Feng, Z., Lin, Y., Wang, W., Hua, X., Chen, Z. Probabilistic updating of structural models for damage assessment using approximate bayesian computation. Sensors 2020, 20, 3197; https://doi.org/10.3390/s20113197
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
Huang, S.-K., Lai, Z.-Z., Lee, H.-P., Yang, Y.-Y. Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data. Appl. Sci. 15, 7794, 2025. https://doi.org/10.3390/app15147794
Lucero, G., Möller, O., Ascheri, J.P. Identificación de parámetros dinámicos de un sistema de vigas prefabricadas mediante registro de vibraciones. Mecánica Computacional, 2025, en prensa.
Möller, O., Lucero, G., Ascheri, J.P. Probabilidad de estados de daño en estructuras utilizando parámetros dinámicos e inferencia bayesiana. Mecánica Computacional, XLI, 279-288, 2024. https://doi.org/10.70567/mc.v41i5.28
Mostafaei, H., Ghamami, M. State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives. Machines 13, 39, 2025. https://doi.org/10.3390/machines13010039
Nicoletti, V., Martini, R., Carbonari, S., Gara, F. Operational Modal Analysis as a Support for the Development of Digital Twin Models of Bridges. Infrastructures 8, 24, 2023. https://doi.org/10.3390/infrastructures8020024
Okur, F.Y., Altunisik, A.C., Okur, E.K. Development and Validation of New Methodology for Automated Operational Modal Analysis Using Modal Domain Range, Structural Control and Health Monitoring, 2025, Article ID 6267884. https://doi.org/10.1155/stc/6267884
Peeters, B., De Roeck, G., Reference-Based Stochastic Subspace Identification for Output-Only Modal Analysis. Mechanical Systems and Signal Processing. 13(6), 855-878, 1999.
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