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Fundamental period estimation of RC buildings by considering structural and non-structural damage distributions through neural network

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Abstract

The aim of this study is to develop a machine learning network to estimate the fundamental vibration period values of existing reinforced concrete (RC) buildings with damaged structural and non-structural elements. By considering the proposed machine learning network, changes in the fundamental vibration period of RC buildings due to potential damage states on structural members and infill walls are estimated. In this context, first of all, the level of reduction in stiffness caused by different damage levels in different types of structural elements is determined. Afterwards, an extensive database composed of 16,000 different building simulations with varying geometrical and mechanical properties is generated. 3D numerical models of these simulations are formed, and the fundamental vibration period values of the generated numerical models are determined. For each numerical model, a variant model at a certain damage state is also created by assigning predefined damage parameters to both structural and non-structural components. To this end, damage factor coefficients are used in stiffness matrices. An artificial neural network model is developed, and the created database is used in training and testing the artificial neural network model. The performance of the proposed artificial neural network (ANN) is determined using ambient vibration tests conducted on both undamaged buildings from the literature and damaged buildings during the Samos earthquake (2020) in the scope of this study. As a result, it has been shown that the proposed ANN is quite successful and can be used as an alternative method for determining the period values of undamaged—damaged RC buildings without the need to generate complex 3D numerical models.

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Data availability

Data, models and/or codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank the Ministry of Environment, Urbanization and Climate Change for its kind support during the database formation and funding. This publication is a part of doctoral dissertation work by the first author under the supervision of the second author in the Academic Program of Civil Engineering, Institute of Science, Hacettepe University.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Hacettepe University, Beytepe, Ankara, Turkey

    Omer Faruk Cinar

  2. Department of Civil Engineering, Hacettepe University, Ankara, Turkey

    Alper Aldemir, Mustafa Sahmaran & Mustafa Kerem Kockar

  3. Mitas Industry Inc., Middle East Technical University, Technopolis, Titanium Block 37/4, 06800, Ankara, Turkey

    Altan Zervent & Omer Burak Yucel

  4. Department of Civil Engineering, Middle East Technical University, Ankara, Turkey

    Murat Altug Erberik & Aysegul Askan

  5. Department of Civil Engineering, Gazi University, Ankara, Turkey

    Ozgur Anil

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  1. Omer Faruk Cinar
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Contributions

OFC performed investigation, visualization and writing—original draft; AA contributed to conceptualization, visualization, writing—review and editing, supervision and project administration; AZ and OBY performed investigation and visualization; MAE, OA, MKK and AA performed writing—review and editing and visualization; MS performed writing—review and editing, visualization and project administration.

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Correspondence to Alper Aldemir.

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Appendix

Appendix

See Tables 4, 5, 6, 7 and 8.

Table 4 Stiffness reduction factors for beam members
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Table 5 Stiffness reduction factors for column members
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Table 6 Stiffness reduction factors of shear wall members
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Table 7 Damage stiffness ratios for non-structural (infill) walls
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Table 8 Numerical representations of structural parameters that are employed for period estimation
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Cinar, O.F., Aldemir, A., Zervent, A. et al. Fundamental period estimation of RC buildings by considering structural and non-structural damage distributions through neural network. Neural Comput & Applic 36, 1329–1350 (2024). https://doi.org/10.1007/s00521-023-09107-6

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