Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

Jiang, Shao-Fei; Zhang, Chunming; Zhang, Shuai

doi:10.1016/j.eswa.2010.06.093

Cited by 112 publications

(56 citation statements)

References 22 publications

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“…Other damage detection methods are based on finite element model updating [7,[34][35][36], genetic algorithms [37][38][39][40][41], neural networks [4,[42][43][44], and bees algorithm [45]. An overview of damage detection methods can be found in [46,47].…”

Section: Introductionmentioning

confidence: 99%

Crack localisation in frames using natural frequency degradations

Labib

Kennedy

Featherston

2015

Computers & Structures

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Section: Introductionmentioning

confidence: 99%

Crack localisation in frames using natural frequency degradations

Labib

Kennedy

Featherston

2015

Computers & Structures

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“…One of the soft computing techniques, neural networks have been utilized increasingly for damage identification due to their excellent pattern recognition, auto-association, self-organization, self-learning and non-linear modeling capability [13][14][15][16][17]. ANNbased methods can operate on a finite element (FE) model of the structure or on real measurement data and a neural network approach can be used to identify faults in the tested structure [18].…”

Section: Introductionmentioning

confidence: 99%

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Bandara

Chan

Thambiratnam

2014

Engineering Structures

127

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“…Although successful identifications and applications have been achieved by using the above-mentioned methods, challenges still exist. The modeling uncertainties are not considered in the studies [23][24][25][26][27][28] ; however, they inevitably exist in real applications. Furthermore, when the input data (frequencies and mode shapes) are polluted by the white noise, the identification accuracy is greatly affected 23 .…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification by sparse deep belief network using uncertain and limited data

Ding

Hao

2020

Struct Control Health Monit

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Summary The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan‐based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.

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Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

Cited by 112 publications

References 22 publications

Crack localisation in frames using natural frequency degradations

Crack localisation in frames using natural frequency degradations

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Structural damage identification by sparse deep belief network using uncertain and limited data

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