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Damage Detection of Fixed-Fixed Beam: A Fuzzy Neuro Hybrid System Based Approach

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

Integration of Neural Networks (NN) and Fuzzy Logic (FL) have brought researchers from various scientific and engineering domains for the need of developing adaptive intelligent systems to address real time applications. The integration of NN and FL can be classified broadly into three categories namely concurrent model, cooperative model and fully fused model. In the present analysis, fuzzy logic and neural network have been adopted to form a damage identification tool for structural health monitoring for fixed-fixed beam made of steel. The proposed methodology utilizes the modal characteristics of the fixed-fixed beam structure using numerical modeling techniques and anticipates the position and severities of the damage present in the system. The robustness of the proposed technique has been realized by conducting experiments on the steel fixed-fixed beam with different damage characteristics.

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Abbreviations

a1 :

= depth of damage

A:

= cross-sectional area of the beam

Ai (i = 1 to 18) :

= unknown coefficients of matrix A

B:

= width of the beam

C11 :

= Axial compliance

C12 = C21 :

= Coupled axial and bending compliance

C22 :

= Bending compliance

C11 :

= Dimensionless form of C11

C12 = C21 :

= Dimensionless form of C12 = C21

C22 :

= Dimensionless form of C22

\( {\bar{\text{C}}}_{12} \) :

= Axial compliance for damage position

\( {\bar{\text{C}}}_{12} = {\bar{\text{C}}}_{21} \) :

= Coupled axial and bending compliance for damage position

\( {\bar{\text{C}}}_{22} \) :

= Bending compliance for damage position

E:

= young’s modulus of elasticity of the beam material

Fi (i = 1, 2) :

= experimentally determined function

i, j:

= variables

J:

= strain-energy release rate

K1, i (i = 1, 2) :

= stress intensity factors for Pi loads

Kij :

= local flexibility matrix elements

\( K^{\prime} n \) :

= Stiffness matrix for damage position

L:

= length of the beam

L1 :

= location (length) of the damage from fixed end

Pi (i=1,2) :

= axial force (i = 1), bending moment (i = 2)

ui (i=1,2) :

= normal functions (longitudinal) ui(x)

x:

= co-ordinate of the beam

y:

= co-ordinate of the beam

yi (i=1,2) :

= normal functions (transverse) yi(x)

W:

= depth of the beam

ω:

= natural circular frequency

β1 :

= relative damage location (L1/L)

ρ:

= mass-density of the beam

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Authors and Affiliations

  1. Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India

    Deepak K. Agarwalla, Amiya K. Dash, Sambit K. Bhuyan & P. S. K. Nayak

Authors
  1. Deepak K. Agarwalla
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  2. Amiya K. Dash
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  3. Sambit K. Bhuyan
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  4. P. S. K. Nayak
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Corresponding author

Correspondence to Deepak K. Agarwalla .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Agarwalla, D.K., Dash, A.K., Bhuyan, S.K., Nayak, P.S.K. (2015). Damage Detection of Fixed-Fixed Beam: A Fuzzy Neuro Hybrid System Based Approach. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_32

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

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