Perilaku Struktur Jembatan Baja Pelengkung Berdasarkan Spektrum Gempa

Behavior of Curved Steel Bridge Structures Based on Earthquake Spectrum

Authors

  • Widya Apriani Universitas Lancang Kuning
  • Fadrizal Lubis Universitas Lancang Kuning
  • Reni Suryanita Universitas Riau
  • Elva Nidya Sari Universitas Lancang Kuning

DOI:

https://doi.org/10.25299/saintis.2019.vol19(2).3924

Keywords:

Curved steel bridge, structural response, Sumatra earthquake spectra.

Abstract

[ID] Perencanaan struktur jembatan baja pelengkung harus memperhatikan kemampuan respon strukturnya yang rentan terhadap deteriorasi akibat fatik, ancaman gempa bumi kuat atau angin topan, khususnya diwilayah sumatera yang mempunyai resiko gempa yang tinggi. Penelitian ini fokus memprediksi  struktur jembatan pelengkung baja dengan analisis repons spectra dengan bantuan software analisis struktur gempa berdasarkan SNI 1726-2012. Percepatan gempa yang diambil berasal dari beberapa kota seperti Kota Aceh, Padang, Tanjung Pinang, dan Pekanbaru yang memliki karakteristik. Hasil analisis menunjukkan respon struktur jembatan terbesar terjadi di Padang dengan nilai perpindahan sebesar 0,016267 m dan percepatan sebesar 0,0235 m. Sementara itu, respons struktur terkecil terjadi di kota tanjung pinang dengan nilai perpindahan sebesar 0,01552 m dan nilai percepatan sebesar 0,0208 m. Diharapkan dengan diketahuinya hasil prediksi kesehatan struktur jembatan dapat digunakan sebagai referensi/masukan bagi pemerintah dan pihak yang terkait dalam usaha memperbaiki jembatan dengan tepat, sehingga diharapkan dapat mencegah terjadinya keruntuhan struktur jembatan.

[EN] Curved steel bridge structure planning must pay attention to the responsiveness of the structure that is vulnerable to deterioration due to fatigue, the threat of strong earthquakes or hurricanes, especially in the region of Sumatra which has a high earthquake risk. This study focuses on predicting the structure of steel curved bridges with spectral response analysis with the help of earthquake structure analysis software based on SNI 1726-2012. The earthquake acceleration taken came from several cities such as Aceh City, Padang, Tanjung Pinang, and Pekanbaru which have characteristics. The analysis shows the largest bridge structure response occurred in Padang with a displacement value of 0.016267 and acceleration of 0.0235. Meanwhile, the smallest structural response occurred in Tanjung Pinang city with a displacement value of 0.01552 and an acceleration value of 0.0208. It is expected that by knowing the results of the bridge structure health predictions can be used as a reference / input for the government and related parties in an effort to repair the bridge appropriately, so that it is expected to prevent the collapse of the bridge structure.

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References

[1] T. BMS, “Bridge Management System,” p. 1, 1993.
[2] W. Apriani, S. W. Megasari, W. Alrisa, and P. Loka, “Penilaian Jembatan Rangka Baja Transfield Australia Dengan Metode Fracture Critical Member ( Studi Kasus : Jembatan Siak 2 Pekanbaru ),” no. September, pp. 18–19, 2018.
[3] R. Suryanita and A. Adnan, “Application of Neural Networks in Bridge Health Prediction based on Acceleration and Displacement Data Domain Application of Neural Networks in Bridge Health Prediction based on Acceleration and Displacement Data Domain,” vol. I, no. February 2016, pp. 4–9, 2013.
[4] Mardiyono, R. Suryanita, and A. Adnan, “Intelligent monitoring system on prediction of building damage index using neural-network,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 10, no. 1, pp. 155–164, 2012.
[5] R. Suryanita, “The Application of Artificial Neural Networks in Predicting Structural Response of Multistory Building in The Region of Sumatra Island,” KnE Eng., vol. 1, no. 2015, pp. 1–6, 2016.
[6] R. Suryanita, H. Maizir, and H. Jingga, “Prediction of Structural Response due to Earthquake Load using Artificial Neural Networks,” Int. Conf. Eng. Technol. Comput. Basic Appl. Sci. ECBA, 2016, Osaka, Japan, vol. 182, no. 4, 2016.
[7] Mardiyono, R. Suryanita, and A. Adnan, “Intelligent monitoring system on prediction of building damage index using neural-network,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 10, no. 1, pp. 155–164, 2012.
[8] J. Brownjohn, “Structural Health Monitoring of the Tamar Bridge,” Vce.At, pp. 465–490, 1961.
[9] J.-J. Lee and C.-B. Yun, “Damage localization for bridges using probabilistic neural networks,” KSCE J. Civ. Eng., vol. 11, no. 2, pp. 111–120, 2008.
[10] S. Tohidi and Y. Sharifi, “A new predictive model for restrained distortional buckling strength of half-through bridge girders using artificial neural network,” KSCE J. Civ. Eng., vol. 20, no. 4, pp. 1392–1403, 2016.
[11] P. H. a Nababan, “Structural Health Monitoring System Alat Bantu Mempertahankan Usia Teknis Jembatan,” Constr. Maint. main span Suramadu Bridg., pp. 1–2, 2008.
[12] N. M. Apaydin, A. C. Zulfikar, and H. Alcik, “Introduction of Bogazici Suspension Bridge Structural Health Monitoring System,” 15th World Conf. Earthq. Eng., 2012.
[13] M. Mehrjoo, N. Khaji, H. Moharrami, and A. Bahreininejad, “Damage detection of truss bridge joints using Artificial Neural Networks,” Expert Syst. Appl., vol. 35, no. 3, pp. 1122–1131, 2008.
[14] Z. Chen, X. Zhou, X. Wang, L. Dong, and Y. Qian, “Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study,” Sensors (Switzerland), vol. 17, no. 9, 2017.
[15] D. S. Shan, P. Yan, and Z. H. Wang, “Intelligent Health Monitoring System for a Railway Cable-Stayed Bridge,” Adv. Mater. Res., vol. 148–149, no. 1, pp. 1390–1393, 2010.
[16] N. D. Lagaros and M. Papadrakakis, “Neural network based prediction schemes of the non-linear seismic response of 3D buildings,” Adv. Eng. Softw., vol. 44, no. 1, pp. 92–115, 2012.
[17] D. Shyam, G. B. L. Chowdary, and D. R. Mahapatra, “Structural Damage Identification Using Artificial Neural Network and Synthetic data.”
[18] S. Ok, W. Son, and Y. M. Lim, “A study of the use of artificial neural networks to estimate dynamic displacements due to dynamic loads in bridges,” J. Phys. Conf. Ser., vol. 382, no. 1, 2012.
[19] A. S. Fahmy, M. E. T. El-Madawy, and Y. Atef Gobran, “Using artificial neural networks in the design of orthotropic bridge decks,” Alexandria Eng. J., vol. 55, no. 4, pp. 3195–3203, 2016.
[20] S. Kim, “Experimental investigation of local damage detection on a 1/15 scale model of a suspension bridge deck,” KSCE J. Civ. Eng., vol. 7, no. 4, pp. 461–468, 2008.
[21] W. F. Darmawan, R. Suryanita, and Z. Djauhari, “Evaluasi Kesehatan Struktur Bangunan berdasarkan Respon Dinamik Berbasiskan Data Akselerometer,” Media Komun. Tek. Sipil, vol. 23, no. 2, p. 142, 2017.
[22] R. Suryanita, “Prediksi Kerusakan Model Jembatan Beton Bertulang Berdasarkan Mutu Beton dengan Metode Jaringan Saraf Tiruan,” no. November, pp. 368–375, 2015.
[23] A. C. Neves, I. González, J. Leander, and R. Karoumi, “Structural health monitoring of bridges: a model-free ANN-based approach to damage detection,” J. Civ. Struct. Heal. Monit., vol. 7, no. 5, pp. 689–702, Nov. 2017.
[24] M. Lydon, S. E. Taylor, D. Robinson, A. Mufti, and E. J. O. Brien, “Recent developments in bridge weigh in motion (B-WIM),” J. Civ. Struct. Heal. Monit., vol. 6, no. 1, pp. 69–81, 2016.
[25] T. PUPERA, “Aplikasi Weight in Motion Kementrian Pekerjaan Umum dan Perumahan Rakyat,” Jakarta, 2017.
[26] Septinurriandiani, Sistem Monitoring Kesehatan Struktur - Penilaian Kondisi dan Kriteria Peralatan Monitoring, 1st ed. Jakarta: Pusat Penelitian dan Pengembangan jalan dan Jembatan, 2011.

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Published

2019-11-28

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