Detecting damage in steel beams using modal strain energy based damage index and Artificial Neural Network

This article provides a quantitative nondestructive damage detection method through a Lamb wave technique assisted by an artificial neural network model for fiber-reinforced composite structures. For simulating damages with a variety of sizes, rectangular Teflon tapes with different lengths and widths are applied on a unidirectional carbon fiber–reinforced polymer composite plate. Two characteristic parameters, amplitude damage index and phase damage index, are defined to evaluate effects by the shape of the rectangular damage in the carbon fiber–reinforced polymer composite plate. The relationships between the amplitude damage index and phase damage index parameters and the damage sizes in the carbon fiber–reinforced polymer composite plate are quantitatively addressed using a three-layer artificial neural network model. It can be seen that a reasonable agreement is achieved between the pre-assigned damage lengths and widths and the corresponding predictions provided by the artificial neural network model. This shows the great potential of using the proposed artificial neural network model for quantitatively detecting the damage size in fiber-reinforced composite structures.

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Application of a mode shape derivative-based damage index in artificial neural network for structural damage identification in shear frame building

Journal of Civil Structural Health Monitoring ◽

10.1007/s13349-019-00342-x ◽

2019 ◽

Vol 9 (3) ◽

pp. 411-423 ◽

Cited By ~ 3

Author(s):

Animesh Paral ◽

Dilip Kr. Singha Roy ◽

Amiya K. Samanta

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mode Shape ◽

Structural Damage ◽

Damage Identification ◽

Damage Index ◽

Shape Derivative ◽

Frame Building ◽

Artificial Neural ◽

Shear Frame

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A damage detection procedure using two major signal processing techniques with the artificial neural network on a scaled jacket offshore platform

Advances in Structural Engineering ◽

10.1177/1369433220981663 ◽

2020 ◽

pp. 136943322098166

Author(s):

Nakisa Mansouri Nejad ◽

Seyed Bahram Beheshti Aval ◽

Mohammad Maldar ◽

Behrouz Asgarian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Signal Processing ◽

Damage Detection ◽

Damage Index ◽

Offshore Platform ◽

Discrete Wavelet ◽

Learning Tools ◽

Mode Decomposition ◽

Artificial Neural

With the help of Structural Health Monitoring (SHM) methods, it is possible to identify the occurrence of damage at its early stages and prevent fatality and financial damages. Great advances in signal processing methods in combination with Machine learning tools have led to better achieve this goal. In the present paper, the two major techniques, that is, Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are combined with Artificial Neural Network (ANN) through processing raw acceleration responses measured on a scaled jacket type offshore platform which was constructed and tested as a benchmark structure at K.N. Toosi University of Technology. In this way, ANN was trained by the signals obtained from EMD and DWT for three different conditions of the jacket platform to determine the relative damage severity. The envelope of the obtained signal’s energy (ENV) as an appropriate damage index was used to determine the damage location. The results of the application of this procedure on the case study indicated that DWT, compared to EMD, is a more reliable signal processing method in damage detection due to better noise reduction.

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