Efektifitas Metode Stochastic Subspace Identification (SSI) Covariance dalam Structural Health Monitoring System (SHMS) Jembatan Box Girder Bentang 40 Meter
DOI:
https://doi.org/10.29103/tj.v15i2.1293Keywords:
SHSM, OMA, SSI covariance, Natural FrequencyAbstract
Abstrak
Pemantauan frekuensi alami pada jembatan box girder bentang 40 meter merupakan langkah penting dalam memastikan stabilitas dan keselamatan struktur selama kondisi layanan aktual. Penelitian ini menerapkan metode Stochastic Subspace Identification (SSI) covariance untuk mengidentifikasi karakteristik dinamis struktur berdasarkan respons getaran akibat beban operasional kendaraan yang melintas. Data diolah tanpa eksitasi buatan, sehingga mencerminkan perilaku dinamis jembatan secara realistis. Hasil analisis menunjukkan frekuensi alami yang stabil dan konsisten pada rentang 3,88–3,89 Hz, baik dari masing-masing sensor maupun analisis gabungan. Temuan ini konsisten dengan hasil pemodelan numerik, yang memperkuat validitas metode SSI covariance sebagai pendekatan non invasif dalam pemantauan kesehatan struktur. Pendekatan ini menegaskan frekuensi alami sebagai indikator utama dalam mengevaluasi kondisi struktural jembatan secara akurat dan andal di bawah beban operasional nyata.
Kata kunci: SHMS, OMA, SSI covariance, Frekuensi Alami.
Abstract
Monitoring the natural frequency of a 40-meter span box girder bridge is essential to ensure the structural stability and safety under actual service conditions. This study applies the Stochastic Subspace Identification (SSI) covariance method to identify the dynamic characteristics of the structure based on vibration responses induced by operational vehicle loads. The data were processed without artificial excitation, thereby reflecting the realistic dynamic behavior of the bridge during operation. The analysis results indicate a stable and consistent natural frequency in the range of 3.88–3.89 Hz, observed across individual sensor measurements as well as in combined analysis. These findings are in good agreement with the numerical modeling results, strengthening the validity of the SSI covariance method as a non invasive approach in structural health monitoring. This approach emphasizes the role of natural frequency as a key indicator for accurate and reliable evaluation of bridge structural conditions under real operational loads.
Keywords: SHSM, OMA, SSI covariance, Natural Frequency
References
Aloisio, A., Alaggio, R., & Fragiacomo, M. (2020). Dynamic identification and model updating of full-scale concrete box girders based on the experimental torsional response. Construction and Building Materials, 264, 120146. https://doi.org/10.1016/j.conbuildmat.2020.120146
Cantieni, R. (1983). Eidgenössische Materialprüfungs-und Versuchsanstalt Laboratoire federal d’essai des materiaux et institut de recherches Laboratorio federale di prova dei materiali ed istituto sperimentale Swiss Federal Laboratories for Materials Testing and Research Dyna. 211.
Cheng, L., Tong, F., Yang, J., & Zheng, D. (2019). Online Modal Identification of Concrete Dams Using the Subspace Tracking-Based Method. Shock and Vibration, 2019. https://doi.org/10.1155/2019/7513261
Cho, K., & Cho, J.-R. (2023). Stochastic Subspace Identification-Based Automated Operational Modal Analysis Considering Modal Uncertainty. Applied Sciences, 13(22), 12274. https://doi.org/10.3390/app132212274
Chopra, A. (2017). Dynamics of Struktures.
Günday, F. (2025). Structural Health Monitoring of Steel Garage Model with Stochastic Subspace Identification–Covariance Variance Analysis. Buildings, 15(6). https://doi.org/10.3390/buildings15060944
Guo, G., Zhong, S., Zhang, Q., Zhong, J., & Liu, D. (2023). Effect of Additional Mass on Natural Frequencies of Weight-Sensing Structures. Sensors, 23(17). https://doi.org/10.3390/s23177585
Ibrahim, W. S., Aminullah, A., Awaludin, A., Suhendro, B., Supriyadi, B., & Krishnamoorthy, R. R. (2022). Dynamic Properties Comparison of 1D, 2D, and 3D Model for Concrete Box-Girder Bridge of 40-meter Span. Journal of the Civil Engineering Forum, 8(September), 321–328. https://doi.org/10.22146/jcef.4148
Javanmardi, A., Abadi, R., Marsono, A. K., Md Tap, M., Ibrahim, Z., & Ahmad, A. (2015). Correlation of Stiffness and Natural Frequency of Precast Frame System. Applied Mechanics and Materials, 735(February), 141–144. https://doi.org/10.4028/www.scientific.net/amm.735.141
Komarizadehasl, S., Huguenet, P., Lozano, F., Lozano-Galant, J. A., & Turmo, J. (2022). Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers. Sensors, 22(24). https://doi.org/10.3390/s22249808
Kvåle, K. A., Øiseth, O., & Rönnquist, A. (2017). Covariance-driven stochastic subspace identification of an end-supported pontoon bridge under varying environmental conditions. Conference Proceedings of the Society for Experimental Mechanics Series, 2 Part F2, 107–115. https://doi.org/10.1007/978-3-319-54777-0_14
Li, W., Vu, V. H., Liu, Z., Thomas, M., & Hazel, B. (2018). Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification. JVC/Journal of Vibration and Control, 24(20), 4781–4796. https://doi.org/10.1177/1077546317734670
Li, Z., Fu, J., Liang, Q., Mao, H., & He, Y. (2019). Modal identification of civil structures via covariance-driven stochastic subspace method. Mathematical Biosciences and Engineering, 16(5), 5709–5728. https://doi.org/10.3934/mbe.2019285
Prakash, V., Debono, C. J., Musarat, M. A., Borg, R. P., Seychell, D., Ding, W., & Shu, J. (2025). Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review. Applied Sciences (Switzerland), 15(9), 1–35. https://doi.org/10.3390/app15094855
Regression, S. V. (2023). Correlation Analysis of Large-Span Cable-Stayed Bridge.
Sun, S., Yang, B., Zhang, Q., Wüchner, R., Pan, L., & Zhu, H. (2023). Fast online implementation of covariance-driven stochastic subspace identification. Mechanical Systems and Signal Processing, 197(April 2022), 110326. https://doi.org/10.1016/j.ymssp.2023.110326
Tomassini, E., García-Macías, E., & Ubertini, F. (2024). Fast Stochastic Subspace Identification of Densely Instrumented Bridges Using Randomized SVD. Mechanical Systems and Signal Processing, 225(December 2024), 112264. https://doi.org/10.1016/j.ymssp.2024.112264
Vaza Herry , Rulli R. Irawan, Gatot Sukmara, A. R. C. (2022). Penentuan Nilai Kondisi & Tingkat Kenyamanan Jembatan Korelasi terhadap Degradasi Frekuensi Alami (Vol. 16, Nomor 1).
Wang, Z. W., Xu, L. P., Ding, Y. L., You, R. Z., Zhu, J. Bin, & Shan, S. (2024). Structural Health Monitoring System for a Complicated Long-Span Continuous Girder Bridge: Implementation and Demonstration. International Journal of Distributed Sensor Networks, 2024. https://doi.org/10.1155/2024/6051389
Wei, Q., Shen, L., Kövesdi, B., Dunai, L., & Cao, M. (2024). A lightweight stochastic subspace identification-based modal parameters identification method of time-varying structural systems. Journal of Sound and Vibration, 570(March 2023), 1–25. https://doi.org/10.1016/j.jsv.2023.118092
Yang, J., Hou, P., Yang, C., Yang, N., & Li, K. (2021). Damage identification method of box girder bridges based on distributed long-gauge strain influence line under moving load. Sensors (Switzerland), 21(3), 1–20. https://doi.org/10.3390/s21030915
Zhou, S. Da, Liu, L., Yang, W., & Ma, Z. S. (2015). Operational modal identification of time-varying structures via a vector multistage recursive approach in hybrid time and frequency domain. Shock and Vibration, 2015. https://doi.org/10.1155/2015/397364
Zulkifli, R. R., Aminullah, A., & Satyarno, I. (2023). Long-Term Health Monitoring Data Processing on Post-Tensioned Concrete Box-Girder Bridge by Wavelet-Based. INERSIA lnformasi dan Ekspose Hasil Riset Teknik Sipil dan Arsitektur, 19(1), 103–112. https://doi.org/10.21831/inersia.v19i1.54918
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Copyright (c) Muhammad Akbar, Akhmad Aminullah, Ali AwaludinAuthors retain copyright and grant the journal right of first publication and this work is licensed under a Creative Commons Attribution-ShareAlike 4.0 that allows others to share the work with an acknowledgement of the works authorship and initial publication in this journal.
All articles in this journal may be disseminated by listing valid sources and the title of the article should not be omitted. The content of the article is liable to the author.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
In the dissemination of articles by the author must declare the Teras Jurnal as the first party to publish the article.
