Abstract
In this study, a building steel structure that adopted an equivalent shear beam structure model is analyzed, with the updating effects on this simplified analysis model discussed using sensitivity-based and artificial neural network (ANN) methods. Due to the limitations of the sensitivity-based structural model updating method, mean absolute relative error was used to evaluate the reasonable number of hidden layer nodes of the ANN multi-layer perceptron architecture to be applied to the updating equivalent shear beam structural model. A National Earthquake Engineering Research Center (Taipei) steel test structure was selected as the case study. The results reveal that the equivalent shear beam structural model updated modal parameter analysis of lower-order modes is more consistent with the modal test than the 3D finite element analysis. A comparison between the discrepancies between the sensitivity-based and ANN methods suggests that the latter outperforms the former, as indicated by its better performance in terms of predicting the first two modal natural frequencies. This finding demonstrates the applicability of the updated equivalent shear beam model and indicates that structural dynamic response analysis can be conducted using the updated stiffness values of each floor. Therefore, this simplified analysis model could be applied to the vibration analysis and design of multi-story structures (e.g., high-rise steel structures, scaffoldings, and vibrating shaking tables). Furthermore, these findings indicate that this simplified analysis model for multi-story structures could also be applied to the evaluation of old structures.
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Data Availability
The experimental data used in this study was obtained from the National Earthquake Engineering Research Center of Taipei Report no. NCREE-99-002 and other published materials (https://doi.org/10.1061/(asce)0733-9399(2000)126:7(693)).
References
Aydin, K., & Kisi, O. (2015). Damage diagnosis in beam-like structures by artificial neural networks. Journal of Civil Engineering and Management, 21(5), 591–604. https://doi.org/10.3846/13923730.2014.890663
Chen, J. C., & Garba, J. A. (1980). Analytical model improvement using modal test results. AIAA Journal, 18(6), 684–690. https://doi.org/10.2514/3.50805
Collins, J. D., Hart, G. C., Hasselman, T. K., & Kennedy, B. (1974). Statistical identification of structures. AIAA Journal, 12(2), 185–190. https://doi.org/10.2514/3.49190
FEM Tools Theoretical Manual Version 3.1. (2006). Dynamic design solutions.
Fox, R. L., & Kapoor, M. P. (1968). Rates of change of eigenvalues and eigenvectors. AIAA Journal, 6(12), 2426–2429. https://doi.org/10.2514/3.5008
Friswell, M. I., & Mottershead, J. E. (1995). Finite element model updating in structural dynamics. Kluwer Academic Publishers.
Horn, R. A., & Johnson, C. R. (1992). Matrix analysis. Cambridge University Press.
Hur, J., Althoff, E., Sezen, H., Denning, R., & Aldemir, T. (2017). Seismic assessment and performance of nonstructural components affected by structural modeling. Nuclear Engineering and Technology, 49(2), 387–394. https://doi.org/10.1016/j.net.2017.01.004
Jahn, H. A. (1948). Improvement of an approximate set of latent roots and modal columns of a matrix by methods akin to those of classical perturbation theory. Quarterly Journal of Mechanics and Applied Mathematics, 1(1), 131–144. https://doi.org/10.1093/qjmam/1.1.131
Jaishi, B., & Ren, W. X. (2006). Damage detection by finite element model updating using modal flexibility residual. Journal of Sound and Vibration, 290(1–2), 369–387. https://doi.org/10.1016/j.jsv.2005.04.006
Keye, S. (2006). Improving the performance of model-based damage detection methods through the use of an updated analytical model. Aerospace Science and Technology, 10(3), 199–206. https://doi.org/10.1016/j.ast.2005.11.011
Lin, R. M., Du, H., & Ong, J. H. (1993). Sensitivity based method for structural dynamic model improvement. Computers and Structures, 47(3), 349–369. https://doi.org/10.1016/0045-7949(93)90231-2
Liu, S. W., Huang, J. H., Sung, J. C., & Lee, C. C. (2002). Detection of cracks using neural networks and computational mechanics. Computer Methods in Applied Mechanics and Engineering, 191(25–26), 2831–2845. https://doi.org/10.1016/S0045-7825(02)00221-9
Loh, C. H., Lin, C. Y., & Huang, C. C. (2000). Time domain identification of frames under earthquake loadings. Journal of Engineering Mechanics, 126(7), 693–703. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(693)
Makay, D., Sándor, B., Bordás, B., & Blénesi, Z. (2010). From simple roof structure calculus based on 2D modelling to 3D models–case study: Reformed Church in Cluj-N., Romania. Advanced Materials Research, 133–134, 125–130. https://doi.org/10.4028/www.scientific.net/AMR.133-134.125
Marwala, T., & Hunt, H. E. M. (1999). Fault identification using finite element models and neural networks. Mechanical Systems and Signal Processing, 13(3), 475–490. https://doi.org/10.1006/mssp.1998.1218
Neelwarne, A. (1993). Convergence in response spectrum analysis. In 12th International conference of structural mechanics in reactor technology (Vol. K13) (2) (pp. 355–360).
Nikolakopoulos, P. G., Katsareas, D. E., & Papadopoulos, C. A. (1997). Crack identification in frame structures. Computers and Structures, 64(1–4), 389–406. https://doi.org/10.1016/S0045-7949(96)00120-4
Tagawa, H., Macrae, G., & Lowes, L. (2004). Evaluations of 1D simple structural models for 2D steel frame structures. In 13th World conference on earthquake engineering. Vancouver, BCN, Canada.
Wendichasky, D. A., Vélez, E. M., Chen, S. S., & Mander, J. B. (2006). Simplified versus detailed bridge models: A time and costs decision. In Proceedings of the fourth international Latin American and Caribbean conference for the engineering and technology, Mayagüez. PR.
Wu, J. R., & Li, Q. S. (2004). Finite element model updating for a high-rise structure based on ambient vibration measurements. Engineering Structures, 26(7), 979–990. https://doi.org/10.1016/j.engstruct.2004.03.002
Wu, X., Ghaboussi, J., & Garrett, J. H. (1992). Use of neural networks in detection of structural damage. Computers and Structures, 42(4), 649–659. https://doi.org/10.1016/0045-7949(92)90132-J
Ye, S., Zheng, O., & Luo, J. (1995). Building scale steel structure shaking table test analysis report, Report no. NCREE-99-002. National. Earthquake Engineering Research Center.
Yun, C. B., & Bahng, E. Y. (2000). Substructural identification using neural networks. Computers and Structures, 77(1), 41–52. https://doi.org/10.1016/S0045-7949(99)00199-6
Zapico, J. L., González, M. P., & Worden, K. (2003). Damage assessment using neural networks. Mechanical Systems and Signal Processing, 17(1), 119–125. https://doi.org/10.1006/mssp.2002.1547
Zhihao, D. (2003). System identification of subspace computing and revision of finite element model [Unpublished PhD Dissertation]. National Taiwan University.
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Lee, NL. Updating Effects on Equivalent Shear Beam Structure Models for Lower-Order Modes Based on Sensitivity-Based Methods and Artificial Neural Networks. Int J Steel Struct 23, 1357–1367 (2023). https://doi.org/10.1007/s13296-023-00774-8
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DOI: https://doi.org/10.1007/s13296-023-00774-8