A Study On Seismic Performance Of RCC-Steel Hybrid Structures
Keywords:
RCC-Steel Hybrid Structures, Seismic Performance, Structural Health Monitoring, Storey Drift and Displacement, Data-Driven Structural Analysis
Abstract
The seismic performance of RCC-Steel hybrid structures has emerged as a critical focus in structural engineering, particularly for ensuring the resilience of buildings in earthquake-prone regions. This study explores the dynamic behaviour of RCC-Steel hybrid systems under seismic loads, emphasizing their ability to combine the compressive strength of reinforced concrete with the tensile strength and ductility of steel. By analysing key structural parameters—storey shear, storey stiffness, storey drift, and storey displacement—across two models (T-20-ii and T-20-ii-PLATE), this research evaluates the impact of design modifications on seismic performance. Advanced techniques, including data-driven approaches and system identification methods, are utilized to enhance the accuracy of structural assessments. The findings indicate that the inclusion of steel plates significantly improves energy dissipation, load distribution, and overall structural stability, contributing to the development of more resilient hybrid systems. This study also highlights existing research gaps, such as the need for multi-hazard assessments and the integration of machine learning with traditional analysis methods, offering pathways for future research.
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2. Audenaert, A., Fanning, P., Sobczak, L., & Peremans, H. (2007). 2-D analysis of arch bridges using an elasto-plastic material model. Engineering Structures, 29(10), 2895–2905. https://doi.org/10.1016/j.engstruct.2007.05.018
3. Brencich, A., & Sabia, D. (2008). Experimental identification of a multi-span masonry bridge: The Tanaro Bridge. Construction and Building Materials, 22(10), 2087–2099. https://doi.org/10.1016/j.conbuildmat.2007.07.031
4. Civera, M., Calamai, G., & Zanotti Fragonara, L. (2021). System identification via Fast Relaxed Vector Fitting for the Structural Health Monitoring of masonry bridges. Structures, 30, 1198–1212. https://doi.org/10.1016/j.istruc.2020.12.073
5. Civera, M., Mugnaini, V., & Zanotti Fragonara, L. (2022). Machine learning-based automatic operational modal analysis: A structural health monitoring application to masonry arch bridges. Structural Control and Health Monitoring, 29(9), e3028. https://doi.org/10.1002/stc.3028
6. Conde, B., Ramos, L. F., Oliveira, D. V., Riveiro, B., & Solla, M. (2017). Structural assessment of masonry arch bridges by combination of non-destructive testing techniques and three-dimensional numerical modelling: Application to Vilanova bridge. Engineering Structures, 148, 621–638. https://doi.org/10.1016/j.engstruct.2017.07.011
7. Gönen, S., & Soyöz, S. (2020). Seismic analysis of a masonry arch bridge using multiple methodologies. Engineering Structures, 225, 111354. https://doi.org/10.1016/j.engstruct.2020.111354
8. Kalkan, E., & Kunnath, S.K. (2006). Adaptive Modal Combination Procedure for Nonlinear Static Analysis of Building Structures. Journal of Structural Engineering, ASCE, 132(11), 1721-1731. DOI: 10.1061/(ASCE)0733-9445(2006)132:11(1721
9. Martucci, D., Civera, M., & Surace, C. (2021). The Extreme Function Theory for Damage Detection: An Application to Civil and Aerospace Structures. Applied Sciences, 11(4), 1716. https://doi.org/10.3390/app11041716
10. Martucci, D., Civera, M., & Surace, C. (2022). Bridge monitoring: Application of the extreme function theory for damage detection on the I-40 case study. Engineering Structures, 261, 115573. https://doi.org/10.1016/j.engstruct.2022.115573
11. Mugnaini, V., Zanotti Fragonara, L., & Civera, M. (2022). A machine learning approach for automatic operational modal analysis. Mechanical Systems and Signal Processing, 181, 108813. https://doi.org/10.1016/j.ymssp.2022.108813
12. Mwafy, A. M., & Elnashai, A. S. (2001). Static pushover versus dynamic collapse analysis of RC buildings. Engineering Structures, 23(5), 407–424. https://doi.org/10.1016/S0141-0296(00)00068-7
13. Pelà, L., Aprile, A., & Benedetti, A. (2009). Seismic assessment of masonry arch bridges. Engineering Structures, 31(8), 1777–1788. https://doi.org/10.1016/j.engstruct.2009.02.012
14. Pulatsu, B., Erdogmus, E., & Lourenço, P. B. (2019). Comparison of in-plane and out-of-plane failure modes of masonry arch bridges using discontinuum analysis. Engineering Structures, 181, 93–107. https://doi.org/10.1016/j.engstruct.2018.10.016
15. Ramamoorthy, S. K., Gardoni, P., & Bracci, J. M. (2006). Probabilistic demand models and fragility curves for reinforced concrete frames. Journal of Structural Engineering, 132(10), 1563–1572. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:10(1563)
16. Rosso, M. M., Aloisio, A., Parol, J., Marano, G. C., & Quaranta, G. (2023). Intelligent automatic operational modal analysis. Mechanical Systems and Signal Processing, 200, 110669. https://doi.org/10.1016/j.ymssp.2023.110669
17. Shabani, A., Feyzabadi, M., & Kioumarsi, M. (2022). Model updating of a masonry tower based on operational modal analysis: The role of soil-structure interaction. Case Studies in Construction Materials, 17, e00957. https://doi.org/10.1016/j.cscm.2022.e00957
18. Shu, J., Zhang, C., Gao, Y., & Niu, Y. (2022). A multi-task learning-based automatic blind identification procedure for operational modal analysis. Mechanical Systems and Signal Processing, 194, 109959. https://doi.org/10.1016/j.ymssp.2022.109959
19. Soleymani, A., Jahangir, H., & Nehdi, M. L. (2023). Damage detection and monitoring in heritage masonry structures: Systematic review. Construction and Building Materials, 378, 132402. https://doi.org/10.1016/j.conbuildmat.2023.132402
20. Yun, D. Y., Shim, H. B., & Park, H. S. (2023). SSI-LSTM network for adaptive operational modal analysis of building structures. Mechanical Systems and Signal Processing, 197, 110306. https://doi.org/10.1016/j.ymssp.2023.110306
Published
2024-04-26
How to Cite
Patel Niyat Vatsal, & Dr. Abhijitsinh Parmar. (2024). A Study On Seismic Performance Of RCC-Steel Hybrid Structures. Revista Electronica De Veterinaria, 25(1), 3888-3895. https://doi.org/10.69980/redvet.v25i1.1803
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