Optimization Method of Wavelet Neural Network for Suspension Bridge Damage Identification

In order to improve the accuracy of damage source identification in concrete based on acoustic emission testing (AE) and neural networks, and locating and repairing the damage in a practical roller compacted concrete (RCC) dam, a multilevel AE processing platform based on wavelet energy spectrum analysis, principal component analysis (PCA), and a neural network is proposed. Two data sets of 15 basic AE parameters and 23 AE parameters added on the basis of the 15 basic AE parameters were selected as the input vectors of a basic parameter neural network and a wavelet neural network, respectively. Taking the measured tensile data of an RCC prism sample as an example, the results show that compared with the basic parameter neural network, the wavelet neural network achieves a higher accuracy and faster damage source identification, with an average recognition rate of 8.2% and training speed of about 33%.

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Refinement of damage identification capability of neural network techniques in application to a suspension bridge

Structural Monitoring and Maintenance ◽

10.12989/smm.2015.2.1.077 ◽

2015 ◽

Vol 2 (1) ◽

pp. 77-93 ◽

Cited By ~ 2

Author(s):

J.Y. Wang ◽

Y.Q. Ni

Keyword(s):

Neural Network ◽

Damage Identification ◽

Suspension Bridge

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Coal Demand Prediction in Shandong Province Based on Artificial Firefly Wavelet Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.1931 ◽

2014 ◽

Vol 962-965 ◽

pp. 1931-1935

Author(s):

Jing Hong Yang ◽

Chang You Wu ◽

Gui Mei Zhang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Optimization Method ◽

Wavelet Neural Network ◽

Systematic Research ◽

Optimal Solutions ◽

Demand Forecast ◽

Demand Prediction ◽

Firefly Optimization

On the basis of the existing research results, after a systematic research of the wavelet neural network model, we found that the slow convergence and easily get into local optimal solutions. To solve this problem, using artificial firefly optimization method to optimize the parameters in wavelet neural network, and Artificial Firefly Wavelet neural network model is established. Apply this model to the Shandong coal demand forecast achieve better results, proved that establishing artificial Firefly Wavelet neural network model is scientific and feasible.

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Bridge damage identification using deep learning-based Convolutional Neural Networks (CNNs)

10.31224/osf.io/ahtpm ◽

2021 ◽

Author(s):

Sandeep Sony

Keyword(s):

Neural Network ◽

Damage Identification ◽

Severe Damage ◽

Acceleration Time ◽

One Dimensional ◽

Damage Scenarios ◽

Benchmark Data ◽

Bridge Damage ◽

Novel Method ◽

Damage Types

In this paper, a novel method is proposed based on windowed-one dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time-series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to 1D CNN model, and the resulting class probabilities are used to identify the location of the damage. The proposed method is validated using Z24 bridge benchmark data for multiclass classification for two damage scenarios. The results show that the proposed 1D CNN methods performs with superior accuracy for severe damage cases and works well with different type of damage types.

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Research on Damage Identification of Suspension Bridge by Wavelet Analysis of the Strain Mode

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.193-194.976 ◽

2012 ◽

Vol 193-194 ◽

pp. 976-979 ◽

Cited By ~ 1

Author(s):

De Qing Guan ◽

Qi Tang ◽

Hong Wei Ying

Keyword(s):

Wavelet Analysis ◽

Damage Identification ◽

Suspension Bridge ◽

Lanczos Method ◽

Suspension Bridges ◽

Strain Mode ◽

Analysis Theory ◽

Damaged Zone ◽

Bridge Damage ◽

Object Of Study

The suspension bridge contained damage was made as the object of study. Three different damage conditions for suspension bridge were calculated(conditions 1, the left midspan contains a damaged zone, condition 2, the left end bay contains two damaged zone, condition 3, both sides midspan contains a damaged zone). Using the wavelet analysis theory, solving strain modal parameters of the suspension bridge with cracks by means of Lanczos method. Then the cracks location of the suspension bridges could be identified by the maximum of wavelet coefficients. It can be concluded that the method using wavelet analysis of strain mode is more accurate and more effective through the calculation and analysis of the suspension bridge damage identification. The method can be useful in the suspension bridge damage identification and diagnosis.

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