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Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
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  • Korea Citation Index (KCI)

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  3. 2024-12 (Vol.28 No.6)
  4. 10.11112/jksmi.2024.28.6.69
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References

1 
Huth, O., Feltrin, G., Maeck, J., Kilic, N., and Motavalli, M., (2005), Damage identification using modal data: Experiences on a prestressed concrete bridge, Journal of Structural Engineering, 131(12), 1898-1910.DOI
2 
Shadan, F., Khoshnoudian, F., and Esfandiari, A., (2016), A frequency response‐based structural damage identification using model updating method, Structural Control and Health Monitoring, 23(2), 286-302.DOI
3 
Azim, M. R., and Gül, M., (2019), Damage detection of steel girder railway bridges utilizing operational vibration response, Structural Control and Health Monitoring, 26(11), e2447.DOI
4 
Ghorbani, E., Buyukozturk, O., and Cha, Y. J., (2020), Hybrid output-only structural system identification using random decrement and Kalman filter, Mechanical Systems and Signal Processing, 144 ,106977.DOI
5 
Entezami, A., Mariani, S., and Shariatmadar, H., (2022), Damage Detectionin Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology, Sensors, 22(4), 1400.DOI
6 
Meixedo, A., Santos, J., Ribeiro, D., Calçada, R., and Todd, M. D., (2022), Online unsupervised detection of structural changes using train–induced dynamic responses, Mechanical Systems and Signal Processing, 165, 108268.DOI
7 
Salawu, O. S., (1997), Detection of structural damage through changes in frequency: a review, Engineering structures, 19(9), 718-723.DOI
8 
Mousavi, Z., Ettefagh, M. M., Sadeghi, M. H., and Razavi, S. N., (2020), Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state, Applied Acoustics, 168, 107402.DOI
9 
Seventekidis, P., Giagopoulos, D., and Koutsoupakis, J., (2023), Simulation Error Influence on Damage Identification Classifiers Trained by Numerical Data, Society for Experimental Mechanics Annual Conference and Exposition, 11-25.DOI
10 
Rastin, Z., Ghodrati Amiri, G., and Darvishan, E., (2021), Unsupervised structural damage detection technique based on a deep convolutional autoencoder, Shock and Vibration, 2021(1), 6658575.DOI
11 
Kim, B., (2023), Deep Learning-Based Assessment of Civil Structure: Inspection using Instance Segmentation and Monitoring using Semi-Supervised Learning [Ph.D. Dissertation, University of Seoul]. University of Seoul.URL