Prototype Design Of Adaptive Composite Springs As An Anti-Seismic Infrastructure System For Multi-Story Buildings

Authors

  • Auni Zahratu Syifa MAN Insan Cendekia OKI
  • Naziha Alqiara MAN Insan Cendekia OKI
  • Muhammad Fakhri MAN Insan Cendekia OKI
  • Muhammad Ghaziyan Faizan MAN Insan Cendekia OKI
  • Afryansyah Afryansyah MAN Insan Cendekia OKI

DOI:

https://doi.org/10.70340/jirsi.v5i2.384

Keywords:

Earthquake, Infrastructure, Earthquake Damper, Epoxy Resin, Carbon Fiber

Abstract

This study aims to design and formulate earthquake dampers composed of carbon fiber and epoxy resin, employing a specialized configuration to produce anti-seismic devices that are corrosion-resistant, adaptively flexible, and capable of supporting building loads effectively. A quantitative design-based methodology was applied, encompassing prototype design, material selection, and performance testing against vertical loads and seismic vibrations. Evaluation was conducted using a hydraulic press to determine maximum vertical load capacity and a shake table to simulate horizontal and vertical seismic activity according to the Richter scale. The results indicate that the composite dampers can absorb seismic energy bidirectionally, maintain structural integrity without significant material degradation, and require minimal maintenance. These findings demonstrate the potential of carbon fiber and epoxy resin-based dampers as adaptive seismic isolation systems that are robust, durable, and suitable for multi-story buildings, while meeting the demands for economical, efficient, and sustainable infrastructure solutions.

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References

F. Zhang, F. Jiang, and Q. Yu, “GIS-based Classification of Earthquake-related Losses in Yunnan,” in 2022 29th International Conference on Geoinformatics, 2022, pp. 1–4. doi: 10.1109/Geoinformatics57846.2022.9963869.

S. Zhang, B. Ku, and H. Ko, “Learnable Maximum Amplitude Structure for Earthquake Event Classification,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–5, 2022, doi: 10.1109/LGRS.2022.3145387.

F. Afiadi and R. F. Sari, “Performance Comparison of PhaseNet and Blockly Earthquake Transformer in Automatic First Arrival Picking on the Cianjur Earthquake,” in 2024 IEEE 6th Symposium on Computers & Informatics (ISCI), 2024, pp. 72–77. doi: 10.1109/ISCI62787.2024.10667833.

J. Zhu et al., “Rapid Earthquake Magnitude Classification Using Single Station Data Based on the Machine Learning,” IEEE Geosci. Remote Sens. Lett., vol. 21, pp. 1–5, 2024, doi: 10.1109/LGRS.2023.3346655.

M. Hong et al., “Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification,” IEEE Access, vol. 13, pp. 56144–56159, 2025, doi: 10.1109/ACCESS.2025.3552127.

Y. Zhang, J. Li, Y. Lei, and T. Yan, “Explore the Approaches to Corporate Social Responsibility Implemented by E-commerce Platforms in China during the Early Stage of COVID-19: A Mixed-methods Content Analysis,” Disaster Med. Public Health Prep., 2021, doi: 10.1017/dmp.2021.346.

P. He, Z.-H. Lu, and W.-L. Hsu, “Construction of Evaluation System of Smart Living Space for the Elderly Based on Analytic Network Process,” in 2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), 2020, pp. 35–38. doi: 10.1109/ECBIOS50299.2020.9203756.

M. Petrich and U. Kletzin, “Lifetime-Centric Engineering Approach for Fiber-Reinforced Polymer Springs Regarding Fatigue and Material Degradation,” Eng. Proc., vol. 119, no. 1, 2025, doi: 10.3390/engproc2025119024.

M. Petrich, U. Kletzin, T.-L. Krehan, J. Feld, and C. Otto, “Design methodology for fiber-reinforced polymer composite springs and experimental study on a volute spring,” Compos. Adv. Mater., vol. 33, 2024, doi: 10.1177/26349833241245134.

L. Wu, L. Chen, H. Fu, Q. Jiang, X. Wu, and Y. Tang, “Carbon fiber composite multistrand helical springs with adjustable spring constant: design and mechanism studies,” J. Mater. Res. Technol., vol. 9, no. 3, pp. 5067–5076, 2020, doi: https://doi.org/10.1016/j.jmrt.2020.03.024.

B. Azam et al., “Aircraft detection in satellite imagery using deep learning-based object detectors,” Microprocess. Microsyst., vol. 94, 2022, doi: 10.1016/j.micpro.2022.104630.

F. S. Wardani, M. Idhom, and Aviolla Terza Damaliana, “Comparative Analysis of Deep Learning Models for Wind Speed Prediction Using LSTM, TCN and RBFNN,” J. Inf. Syst. Technol. Res., vol. 4, no. 3, pp. 163–176, Sep. 2025, doi: 10.55537/jistr.v4i3.1298.

I. Yakin, U. Supriatna, S. Rusdian, and M. Global Akademia, METODOLOGI PENELITIAN (KUANTITATIF & KUALITATIF). 2023.

O. Abiodun-Oyebanji, “RESEARCH VARIABLES: TYPES, USES AND DEFINITION OF TERMS,” 2017, pp. 43–54.

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Published

2026-05-31

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