Abstract
The study proposes assessing the seismic vulnerability of G + 15-storey reinforced concrete (RC) buildings using finite element analysis (FEA) software (ETABS) and artificial neural networks (ANNs). The study utilizes finite element models of G + 15 buildings subjected to recent earthquake data, analysing them for seismic vulnerability and incorporating various retrofitting techniques, such as fluid viscous dampers (FVD). The damper locations are varied in the structure for the entire earthquake data considered to study the seismic vulnerability in storey displacements, storey shear, and storey drift. Key structural characteristics were systematically modified, and their impact on seismic response was evaluated through modal dynamic and non-linear time history analyses. The FEA results are used to train an ANN algorithm, creating a function that can predict the seismic behaviour of similar RC structures. This approach offers a fast and potentially generalizable method for seismic vulnerability assessment.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig12_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42107-024-01116-7/MediaObjects/42107_2024_1116_Fig13_HTML.png)
Data availability
No datasets were generated or analysed during the current study.
References
Abbaszadeh, A., & Chaallal, O. (2022). Enhancing resilience and self-centering of existing RC coupled and single shear walls using EB-FRP: state-of-the-art review and research needs. J Compos Sci, 6(10), 301. https://doi.org/10.3390/jcs6100301
Alanani, M., & Elshaer, A. (2023). ANN-based optimization framework for the design of wind load resisting system of tall buildings. Engineering Structures, 285, 116032. https://doi.org/10.1016/j.engstruct.2023.116032
Asgarkhani, N., Kazemi, F., Jakubczyk-Gałczyńska, A., Mohebi, B., & Jankowski, R. (2024). Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods. Engineering Applications of Artificial Intelligence, 128, 107388. https://doi.org/10.1016/j.engappai.2023.107388
Birky, D., Ladd, J., Guardiola, I., & Young, A. (2022). Predicting the dynamic response of a structure using an artificial neural network. J Low-Freq Noise Vib Active Control, 41(1), 182–195. https://doi.org/10.1177/14613484211038408
Erdem Çerçevik, A., Avşar, O., & Dilsiz, A. (2021). Optimal placement of viscous wall dampers in RC moment resisting frames using metaheuristic search methods. Engineering Structures, 249, 113108. https://doi.org/10.1016/j.engstruct.2021.113108
Fu, D., Wang, L., Lv, G., Shen, Z., Zhu, H., & Zhu, W. D. (2023). Advances in dynamic load identification based on data-driven techniques. Engineering Applications of Artificial Intelligence, 126, 106871. https://doi.org/10.1016/j.engappai.2023.106871
Hait, P., Sil, A., & Choudhury, S. (2020). Seismic damage assessment and prediction using an artificial neural network of RC building considering irregularities. J Struct Integr Maint, 5(1), 51–69. https://doi.org/10.1080/24705314.2019.1692167
IS 1893 (Part 1) (2016) Criteria for Earthquake resistance design of structures
Jbury, N. A. A., & Hejazi, F. (2023). Development of hybrid performance-based optimization algorithm for structures equipped with vibration damper devices. Arch Civ Mech Eng. https://doi.org/10.1007/s43452-023-00665-z
Kalamkar, A., Pitale, N. H., & Patil, P. B. (2021). Controlling seismic excitation in the RCC building with a tuned mass damper. IOP Conf Ser Mater Sci Eng, 1197(1), 012039. https://doi.org/10.1088/1757-899x/1197/1/012039
Kaveh, A. (2017). Applications of metaheuristic optimization algorithms in civil engineering. Springer.
Kaveh, A. (2024). Applications of artificial neural networks and machine learning in civil engineering, studies in computational intelligence (Vol. 1168). Springer.
Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256–272. https://doi.org/10.1016/j.istruc.2023.03.178
Kaveh, A., Javadi, S. M., & Mahdipour Moghanni, R. (2020). Optimal structural control of tall buildings using tuned mass dampers via chaotic optimization algorithm. Structures, 28, 2704–2713. https://doi.org/10.1016/j.istruc.2020.11.002
Kazemi, F., Asgarkhani, N., & Jankowski, R. (2023). Machine learning-based seismic response and performance assessment of reinforced concrete buildings. Arch Civ Mech En. https://doi.org/10.1007/s43452-023-00631-9
Sajjan P, Biradar P (2018) Study on the effect of viscous damper for RCC frame structure. http://ijret.esatjournals.org
Sharma, K. V., Parmar, V., Gautam, L., Choudhary, S., & Gohil, J. (2023). Modelling efficiency of fluid viscous dampers positioning for increasing tall buildings’ Resilience to earthquakes induced structural vibrations. Soil Dynamics and Earthquake Engineering, 173, 108108. https://doi.org/10.1016/j.soildyn.2023.108108
Sudeep, Y. H., Ujwal, M. S., Sridhar, H. N., Sathvik, S., Shiva Kumar, G., & Ramaraju, H. K. (2024). Comparative study of step-back and step-back setback configurations of multi-story buildings with varying height on sloped terrain. Asian J Civ Eng. https://doi.org/10.1007/s42107-024-01099-5
Stefanini, L., Badini, L., Mochi, G., Predari, G., & Ferrante, A. (2022). Neural networks for the rapid seismic assessment of existing moment-frame RC buildings. Int J Disaster Risk Reduct, 67, 102677. https://doi.org/10.1016/j.ijdrr.2021.102677
Ujwal, M. S., Shiva Kumar, G., Sathvik, S., & Ramaraju, H. K. (2023). Effect of soft story conditions on the seismic performance of tall concrete structures. Asian J Civ Eng, 25, 3141–3419. https://doi.org/10.1007/s42107-023-00968-9
Vaidyanathan, C. V., Kamatchi, P., & Ravichandran, R. (2005). Artificial neural networks for predicting the response of structural systems with viscoelastic dampers. Comput Aided Civ Infrastruct Eng, 20, 294–302.
Yucel, M., Bekdaş, G., Nigdeli, S. M., & Sevgen, S. (2019). Estimation of optimum tuned mass damper parameters via machine learning. J Build Eng, 26, 100847. https://doi.org/10.1016/j.jobe.2019.100847
Funding
The authors declare that no funds, grants or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
Author 1-Rizwan J Kudari: Original Draft Preparation, Project Administration, Data Curation, Software, Validation. Author 2-Geetha L: Conceptualization, Methodology, Review & Editing. Author 3-Ashwini Satyanarayana: Conceptualization, Methodology, Review & Editing.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kudari, R.J., Geetha, L. & Satyanarayana, A. Assessing seismic vulnerability of structures with damper using an ANN-based approach. Asian J Civ Eng (2024). https://doi.org/10.1007/s42107-024-01116-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42107-024-01116-7