STUDI PERUBAHAN AMPLITUDO DAN PERIODE FUNDAMENTAL STRUKTUR PADA STRUKTUR BANGUNAN TINGKAT TINGGI YANG MENGGUNAKAN INTERMEDIATE FLOOR ISOLATION
DOI:
https://doi.org/10.9744/duts.13.2.176-196Keywords:
Intermediate Floor Isolation, periode fundamental, amplitudo, Fast Fourier Transform, Artificial Neural Network, time historyAbstract
Aktivitas seismik tinggi di Indonesia menjadikan teknologi isolasi gempa penting bagi bangunan bertingkat. Penelitian ini menganalisis pengaruh konfigurasi IFI, redaman efektif, dan dimensi elemen struktur terhadap amplitudo dan periode fundamental pada 196 model bangunan tujuh lantai menggunakan time history ETABS 22.5.1 dengan rekaman gempa Altadena. Respons struktur diproses dengan Fast Fourier Transform (FFT), sedangkan prediksi dilakukan menggunakan model ensemble Artificial Neural Network (ANN) berbasis TensorFlow dan Random Forest Regressor. Hasil menunjukkan bahwa semakin banyak lantai dalam zona isolasi, semakin besar reduksi amplitudo (81%–95%) dibandingkan isolator satu lantai. Sebaliknya, isolator pada lantai dasar menghasilkan periode fundamental terpanjang, sedangkan isolasi seluruh lantai menghasilkan periode seragam-pendek akibat modal coupling. Dimensi elemen struktur yang lebih besar menurunkan amplitudo dan memperpendek periode, sedangkan peningkatan redaman efektif hanya berpengaruh kecil dan non-monoton. Model ensemble ANN-RF menghasilkan kesalahan prediksi didominasi di bawah 20% (96,6% data verifikasi), dengan periode fundamental menunjukkan akurasi lebih tinggi dibandingkan amplitudo.
Indonesia's high seismic activity makes seismic isolation important for high-rise building design. This study investigates the effects of Intermediate Floor Isolation (IFI) configuration, effective damping, and structural member dimensions on amplitude and fundamental period, using 196 seven-story building models analyzed via time history analysis in ETABS 22.5.1 with the Altadena earthquake record. Responses were processed using Fast Fourier Transform (FFT), while prediction used an ensemble ANN-Random Forest Regressor model based on TensorFlow. More floors within the isolation zone progressively increased amplitude reduction (81%-95%) versus isolating a single floor. Isolating only the ground floor produced the longest fundamental period, whereas isolating all floors gave a uniformly short period due to modal coupling. Larger structural members reduced amplitude and shortened the period, while increased damping had only a minor, non-monotonic effect. The ensemble model achieved prediction errors predominantly below 20% (96.6% of verification data), with period predictions more accurate than amplitude.
References
Alavi, A. H., & Gandomi, A. H. (2011). Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Computers & Structures, 89(23–24), 2176–2194. doi:10.1016/j.compstruc.2011.08.003
Athya, T., & Singh, R. (2025). Use of artificial neural network based prediction of optimum case of multistory buildings by using structural response. IJRASET Journal for Research in Applied Science and Engineering Technology.
Ayebare, M., Chavula, P., & Mugisha, S. (2025). TensorFlow-native implementation for automated crack detection in concrete structures. Mesopotamian Journal of Civil Engineering, 97–108.
Badan Standardisasi Nasional. (2019). Tata cara perencanaan ketahanan gempa untuk struktur bangunan gedung dan nongedung (SNI 1726:2019). Jakarta, Indonesia: Badan Standardisasi Nasional.
Bridgestone Corporation. (2017). Seismic isolation product line-up. Tokyo, Japan: Seismic Isolation & Vibration Control Products Business Department, Bridgestone Corporation.
Chopra, A. K. (2017). Dynamics of Structures: Theory and Applications to Earthquake Engineering (5th ed.). Upper Saddle River, NJ: Pearson Education, Inc.
Clough, R. W., & Penzien, J. (2003). Dynamics of Structures (3rd ed.). Berkeley, CA: Computers & Structures, Inc.
Computers and Structures, Inc. (2023). ETABS: Building Analysis and Design. Berkeley, CA: CSI.
Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301. doi:10.1090/S0025-5718-1965-0178586-1
Faiella, D., & Mele, E. (2019). Vibration characteristics and higher mode coupling in intermediate isolation systems (IIS): A parametric analysis. Bulletin of Earthquake Engineering, 17(8), 4347–4387. doi:10.1007/s10518-019-00637-w
Gao, L., Liu, D., Lei, M., Ding, Y., & Mu, S. (2023). Study on Shock-Absorbing Effect of a Double-Story Isolation Structure Considering Soil–Structure Interaction. Buildings, 13(11), 2677. doi:10.3390/buildings13112677
Ghosh, S. K., Kashyap, I., & Das, D. (2026). Machine learning-enhanced metaheuristic optimization of lead rubber bearings for inter-story isolated buildings under seismic load. Engineering Structure and Civil Engineering, 20, 36–79. doi:10.1007/s11709-026-1275-y
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press. Haykin, S. (2009). Neural Networks and Learning Machines (3rd ed.). Upper Saddle River,
NJ: Pearson Education.
Imam, M. H., Mohiuddin, M., Shuman, N. M., Oyshi, T. I., Debnath, B., & Liham, M. I. M. H. (2024). Prediction of seismic performance of steel frame structures: A machine learning approach. Structures, 69, 107547. doi:10.1016/j.istruc.2024.107547
Naeim, F., & Kelly, J. M. (1999). Design of Seismic Isolated Structures: From Theory to Practice. New York, NY: John Wiley & Sons.
Pawirodikromo, W. (2012). Seismologi teknik dan rekayasa kegempaan. Yogyakarta, Indonesia: Pustaka Pelajar.
Pusat Studi Gempa Nasional (Pusgen). (2017). Peta sumber dan bahaya gempa Indonesia tahun 2017. Bandung, Indonesia: Badan Penelitian dan Pengembangan, Kementerian Pekerjaan Umum dan Perumahan Rakyat.
Suan, F. R., Pah, J. J. S., & Bolla, M. E. (2025). Analisis kinerja struktur bangunan base isolation pada setiap lantai bangunan dengan variasi redaman efektif isolator [Skripsi tidak diterbitkan]. Kupang, Indonesia: Universitas Nusa Cendana.
Suryanita, R., Maizir, H., Firzal, Y., Jingga, H., & Yuniarto, E. (2019). Response prediction of multi-story building using backpropagation neural networks method. In MATEC Web of Conferences (Vol. 276, p. 1011). doi:10.1051/matecconf/201927601011
Tang, J., & Liu, D. (2024). Study on the shock-absorption performance of isolation systems in high-rise vertically irregular double-story structures: Dynamic time history evaluation. Buildings, 14(12), 3792. doi:10.3390/buildings14123792
TensorFlow. (2024). Keras: Building and training neural networks. Retrieved from https://www.tensorflow.org/guide/keras/sequential_model
Wang, S.-J., Chang, K.-C., Hwang, J.-S., & Lee, B.-H. (2011). Simplified analysis of mid-story seismically isolated buildings. Earthquake Engineering & Structural Dynamics, 40(2), 119–133. doi:10.1002/eqe.1004
Wang, S.-J., Hwang, J.-S., Chang, K.-C., Lin, M.-H., & Lee, B.-H. (2013). Analytical and experimental studies on midstory isolated buildings with modal coupling effect. Earthquake Engineering & Structural Dynamics, 42(2), 201–219. doi:10.1002/eqe.2203
Zhao, G., Zhang, L., Liu, D., & Shen, K. (2025). Seismic response analysis of double-layer isolation structures in high-rise buildings. Buildings, 15(8), 1292. doi:10.3390/buildings15081292
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