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Adaptive Finite Element Machine for Real-time Structural Damage Detection

Nitin Pal

Abstract


Systems for monitoring structural health (SHM) are essential for guaranteeing the consistency and safety of civil infrastructure. The timely identification of structural deterioration is crucial in order to avert major malfunction and save upkeep expenses. In this research, we describe a unique real-time structural damage detection method based on Adaptive Finite Element Machine (AFEM). To locate and diagnose structural degradation in an adaptable manner, AFEM combines machine learning methods with the theories of finite element analysis. The accuracy, efficiency, and versatility of the suggested approach are noteworthy, and it may be used to a wide range of situations in the real world.


Keywords


Structural health monitoring, finite element analysis, machine learning, adaptive finite element machine, real-time damage detection

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References


Shen, N. et al. A review of global navigation satellite system (GNSS)-based dynamic monitoring technologies for structural health monitoring. Remote Sens. 11, 1001 (2019).

Lee, Y., Lee, G., Moon, D. S. & Yoon, H. Vision-based displacement measurement using a camera mounted on a structure with stationary background targets outside the structure. Struct. Control Health Monit. 29, e3095 (2022).

Messina, A., Williams, E. & Contursi, T. Structural damage detection by a sensitivity and statistical-based method. J. Sound Vib. 216, 791–808 (1998).

Ren, W.-X. & Chen, H.-B. Finite element model updating in structural dynamics by using the response surface method. Eng. Struct. 32, 2455–2465 (2010).

Ciang C, Lee J, Bang H (2008) Structural health monitoring for a wind turbine system: a review of damage detection methods. Meas Sci Technol 19(12):1–20

Siddesha H, Hegde MN (2017) Structural damage detection in framed structures using under foundation settlement/ rotation of bases. Struct Durab Health Monit (SDHM) 12(1):17–41

Mechbal N., Uribe J.S., Rébillat M. A probabilistic multi-class classifier for structural health monitoring. Mech. Syst. Signal Processing. 2015;60–61:106–123. doi: 10.1016/j.ymssp.2015.

017.

L. Chen. iFEM: An Integrated Finite Element Methods Package in MATLAB. Technical Report, University of California at Irvine, 2009




DOI: https://doi.org/10.37628/ijsmfe.v9i2.1608

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