Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

doi:10.1007/s11709-022-0840-2

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (8) : 976-989 https://doi.org/10.1007/s11709-022-0840-2

RESEARCH ARTICLE

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network

Abdelwahhab KHATIR¹, Roberto CAPOZUCCA¹, Samir KHATIR²(

), Erica MAGAGNINI¹

¹. Structural Section DICEA, Polytechnic University of Marche, Ancona 60131, Italy
². Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City 700000, Vietnam

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Abstract

Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

Keywords damage prediction ANN BOA FEM experimental modal analysis

Corresponding Author(s): Samir KHATIR

Just Accepted Date: 23 August 2022 Online First Date: 31 October 2022 Issue Date: 02 December 2022

Cite this article:

Abdelwahhab KHATIR,Roberto CAPOZUCCA,Samir KHATIR, et al. Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural network[J]. Front. Struct. Civ. Eng., 2022, 16(8): 976-989.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0840-2
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I8/976

Fig.1 ANN structure for crack depth identification.

Tab.1 Geometrical and mechanical characteristics of beam mode

Fig.2 (a) Finite element model and (b) considered beam model with considered damage location (mm).

Tab.2 FEM and Measured Frequencies of the intact beam model

Fig.3 The first three mode shapes of the intact beam. (a) Model 1; (b) Model 2; (c) Model 3.

Tab.3 Considered cracks depths scenarios

Fig.4 Regression using different numbers of hidden layer sizes for middle crack damage case. BOA with (a) n = 4 and (b) n = 8; PSO with (c) n = 4 and (d) n = 8; GA with (e) n = 4 and (f) n = 8.

Tab.4 Predicted crack depth for middle crack damage case using ANN trained by BOA-PSO-GA

Fig.5 Real and predicted crack depth by changing the hidden layer size for middle crack damage case.

Tab.5 Predicted crack depth for side crack damage case using ANN trained by BOA-PSO-GA

Fig.6 Regression using a different number of hidden layer sizes for side crack damage case. BOA with (a) n = 4 and (b) n = 8; PSO with (c) n = 4 and (d) n = 8; GA with (e) n = 4 and (f) n = 8.

Fig.7 Real and predicted crack depth by changing the hidden layer size for side crack damage case.

Tab.6 Considered cracks depths for experimental study

Fig.8 Setup of cracked beam modal analysis. (a) Experimental setup; (b) impact and accelerometer position; (c) middle crack specimens; (d) side crack specimens.

Tab.7 Experimental frequency values for damaged beam by middle and side crack

Fig.9 FRF diagrams for middle crack damaged beam with 5 mm depth.

Fig.10 FRF diagrams for middle crack damaged beam with 25 mm depth.

Fig.11 FRF diagrams for side crack damaged beam with 10 mm depth.

Fig.12 FRF diagrams for side crack damaged beam with 20 mm depth.

Tab.8 Predicted crack depth for middle and side crack damage case using experimental data and ANN trained by BOA-PSO-GA

Fig.13 Regression study for ANN trained with (a) BOA; (b) PSO; (c) GA.

Fig.14 Experiment middle crack prediction using ANN trained by BOA-PSO-GA.

Fig.15 Experiment side crack prediction using ANN trained by BOA-PSO-GA.

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