An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm

Abstract This paper presents a new approach for damage detection in structures by applying a flexible combination based on an artificial neural network (ANN) and cuckoo search (CS) algorithm. ANN has become one of the most powerful tools employing computational intelligence techniques to tackle complex problems in numerous fields. However, due to the application of backpropagation algorithms based on gradient descent, a major drawback of ANN is the common problem of local minima that acts as a great hindrance to the search for the best solution. To overcome this disadvantage, we propose to combine ANN with evolutionary algorithms based on global search techniques. This paper employs CS to improve ANN training parameters (weight and bias) by minimizing the difference between real and desired outputs and then using these parameters to generate the network. Two numerical models, comprising a steel beam calibrated using experimental measurements and a large-scale truss bridge, are used to assess the robustness of the proposed approach. The results demonstrate that ANN combined with CS (ANN-CS) is accurate and requires a lower computational time than ANN, and evolutionary algorithm (EA) alone in terms of structural damage localization and quantification.

[1]  Jasmina Arifovic,et al.  Genetic algorithms and inflationary economies , 1995 .

[2]  Ali Azadeh,et al.  A flexible ANN-GA-multivariate algorithm for assessment and optimization of machinery productivity in complex production units , 2015 .

[3]  Samir Khatir,et al.  Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm , 2018, Engineering Structures.

[4]  S. M. Seyedpoor,et al.  Structural Damage Identification Using an Adaptive Multi-stage Optimization Method Based on a Modified Particle Swarm Algorithm , 2013, Journal of Optimization Theory and Applications.

[5]  Magd Abdel Wahab,et al.  Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm , 2018 .

[6]  P. K. Dash,et al.  An improved cuckoo search based extreme learning machine for medical data classification , 2015, Swarm Evol. Comput..

[7]  Antonio Ruiz,et al.  Performance assessment of multicriteria damage identification genetic algorithms , 2009 .

[8]  Roger Alimi,et al.  A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects , 2015, Sensors.

[9]  Amir H. Mohammadi,et al.  Artificial neural network, ANN-PSO and ANN-ICA for modelling the Stirling engine , 2016 .

[10]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[11]  M. M. Zdravkovich,et al.  Effect of shroud eccentricity on suppression of flow induced vibrations , 1973 .

[12]  R. Bhavani,et al.  PSO trained ANN-based differential protection scheme for power transformers , 2008, Neurocomputing.

[13]  Randall S. Sexton,et al.  Comparing backpropagation with a genetic algorithm for neural network training , 1999 .

[14]  Chyi Hwang,et al.  A simple and efficient real-coded genetic algorithm for constrained optimization , 2016, Appl. Soft Comput..

[15]  Wei-Xin Ren,et al.  Structural damage detection using a parked vehicle induced frequency variation , 2018, Engineering Structures.

[16]  A. Barr,et al.  One-dimensional theory of cracked Bernoulli-Euler beams , 1984 .

[17]  S. K. Panigrahi,et al.  Damage Identification of Multistory Shear Structure from Sparse Modal Information , 2013, J. Comput. Civ. Eng..

[18]  Mohammadreza Vafaei,et al.  Non-probabilistic wavelet method to consider uncertainties in structural damage detection , 2018, Journal of Sound and Vibration.

[19]  J. McCall,et al.  Genetic algorithms for modelling and optimisation , 2005 .

[20]  Samir Khatir,et al.  A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm , 2019, Theoretical and Applied Fracture Mechanics.

[21]  Yun-Lai Zhou,et al.  Damage detection using vibration data and dynamic transmissibility ensemble with auto-associative neural network , 2017 .

[22]  Leonardo Wayland Torres Silva,et al.  Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems , 2014, Sensors.

[23]  Ali Kaveh,et al.  Damage detection in skeletal structures based on charged system search optimization using incomplete modal data , 2014 .

[24]  Francesco Micelli,et al.  An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns , 2017 .

[25]  Samir Khatir,et al.  Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm , 2019, Engineering Fracture Mechanics.

[26]  B. Curry,et al.  Neural networks: a need for caution , 1997 .

[27]  Q. W. Yang.  A numerical technique for structural damage detection , 2009, Appl. Math. Comput..

[28]  Mehrisadat Makki Alamdari,et al.  Damage identification of a concrete arch beam based on frequency response functions and artificial neural networks , 2015 .

[29]  Damodar Maity,et al.  Damage assessment in structure from changes in static parameter using neural networks , 2004 .

[30]  Ki-Young Koo,et al.  Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix , 2016 .

[31]  Ali Kaveh,et al.  Damage detection based on MCSS and PSO using modal data , 2015 .

[32]  H. Marzouk,et al.  Crack width in concrete using artificial neural networks , 2013 .

[33]  Ömer Faruk Ertugrul,et al.  A novel type of activation function in artificial neural networks: Trained activation function , 2018, Neural Networks.

[34]  H. Tran-Ngoc,et al.  Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm , 2018, Sensors.

[35]  A. Bahrami,et al.  Using GA–ANN algorithm to optimize soft magnetic properties of nanocrystalline mechanically alloyed Fe–Si powders , 2009 .

[36]  Samir Khatir,et al.  Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis , 2019, Journal of Sound and Vibration.

[37]  Magd Abdel Wahab,et al.  A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm , 2018, Comptes Rendus Mécanique.

[38]  Ali Kaveh,et al.  Structural damage identification using an enhanced thermal exchange optimization algorithm , 2018 .

[39]  Mia Loccufier,et al.  Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization , 2018 .

[40]  François Pérès,et al.  Global methodology for damage detection and localization in civil engineering structures , 2018, Engineering Structures.