Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Damage detection in steel frames connection based on Modal Assurance Criterion (MAC) and MAC flexibility

Document Type : Original Article

Author
Department of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Abstract
In this paper, an effective joint damage identification method has been proposed. Beam to columns connections modeled as a rotational mass less spring. Joint damage has been presented as a reduction in the connection rigidity or rotational stiffness factor. Because of sensitivity of mode shapes and frequencies on structural stiffness, the first mode shape and frequency of damaged frame has been used as a feature to identify damage in steel frame connections. This data is acquired by the modal analysis of damaged structure applying the finite element method (FEM). The numerical studies are carried out within the MATLAB (2016) environment, which is used for the solution of finite element problems. To identify joint damage, an optimization problem formulated in which the objective functions formulated based on Modal Assurance Criterion (MAC) and MAC flexibility. To solve the optimization problem, an effective meta-heuristic called Grey Wolf Optimizer is employed to detect damage in beam to columns connections. To evaluate the performance of presented method, three examples consists of a seven, twelve and fifteen story steel frames are chosen with two different scenarios of damage in beam to columns connections for each of them for this purpose. Results reveal that the proposed approach is effective to detect and estimate damage in steel frame connections.
Keywords

Subjects


[1] Yin, T., Jiang, Q. H., & Yuen, K. V. (2017). Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Engineering Structures, 132, pp. 260-277.
[2] Machavaram, R., & Shankar, K. (2013). Joint damage identification using Improved Radial Basis Function (IRBF) networks in frequency and time domain. Applied Soft Computing, 13(7), pp. 3366-3379.
[3] Nanda, B., Maity, D., & Maiti, D. K. (2014). Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization. Applied Mathematics and Computation, 242, pp. 407-422.
[4] Katkhuda, H., Shatarat, N. and Hyari, K. (2017). Damage detection in steel structures with semi-rigid connections using unscented Kalman filter. International Journal of Structural Integrity, 8 (2), pp. 222-239.
[5] Türker, T., Kartal, M. E., Bayraktar, A., & Muvafik, M. (2009). Assessment of semi-rigid connections in steel structures by modal testing. Journal of Constructional Steel Research, 65(7), pp. 1538-1547.
[6] Kourehli, S. (2018). Health monitoring of connections in steel frames using wavelet transform. Amirkabir Journal of Civil Engineering, 52(3), 11-11. doi: 10.22060/ceej.2018.14815.5750.
[7] Weng, J. H., Loh, C. H., & Yang, J. N. (2009). Experimental study of damage detection by data-driven subspace identification and finite-element model updating. Journal of structural engineering, 135(12), 1533-1544.
[8] Yang, J. N., Xia, Y., & Loh, C. H. (2014). Damage identification of bolt connections in a steel frame. Journal of Structural Engineering, 140(3), 04013064.
[9] Lei, Y., Li, Q., Chen, F., & Chen, Z. (2014). Damage identification of frame structures with joint damage under earthquake excitation. Advances in Structural Engineering, 17(8), 1075-1087.
[10] Kourehli, S., Amiri, G.G., Ghafory-Ashtiany M., et al. (2013). Structural damage detection based on incomplete modal data using pattern search algorithm. Journal of vibration and control, 19, pp. 821-833.
[11] Kaveh, A. and Dadras, A. (2018). Structural damage identification using an enhanced thermal exchange optimization algorithm. Engineering Optimization, 50, pp. 430-451.
[12] Dinh-Cong D, Dang-Trung H, Nguyen-Thoi T. (2018). An efficient approach for optimal sensor placement and damage identification in laminated composite structures. Advances in Engineering Software, 119, pp. 48-59.
[13] Khatir, S., Brahim, B., Capozucca, R., et al. (2018a). Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm. Composite Structures, 187, pp. 344-353.
[14] Wei, Z., Liu, J. and Lu, Z. (2018). Structural damage detection using improved particle swarm optimization. Inverse Problems in Science and Engineering, 26, pp. 792-810.
[15] Kourehli, S. (2021). A new damage identification method in beam to column connections and column base connections of steel frame. Journal of Structural and Construction Engineering, doi: 10.22065/jsce.2021.257528.2293.
[16] Yun, C. B., Yi, J. H., & Bahng, E. Y. (2001). Joint damage assessment of framed structures using a neural networks technique. Engineering structures, 23(5), pp. 425-435.
[17] Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, pp. 46-61.
[18] Faris, H., Aljarah, I., Al-Betar, M.A., et al. (2018). Grey wolf optimizer: a review of recent variants and applications. Neural Computing and Applications, 30, pp. 413-435