A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks

2021 ◽  
Vol 252 ◽  
pp. 106568
Author(s):  
Long Viet Ho ◽  
Duong Huong Nguyen ◽  
Mohsen Mousavi ◽  
Guido De Roeck ◽  
Thanh Bui-Tien ◽  
...  
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Computational Intelligence ◽  
Structural Damage ◽  
Feedforward Neural Networks ◽  
Structural Damage Detection ◽  
Marine Predator ◽  
Intelligence Approach

Structural damage detection using convolutional neural networks combining strain energy and dynamic response

Meccanica ◽  
2019 ◽  
Vol 55 (4) ◽  
pp. 945-959 ◽  
Author(s):  
Shuai Teng ◽  
Gongfa Chen ◽  
Panpan Gong ◽  
Gen Liu ◽  
Fangsen Cui
Keyword(s):  
Neural Networks ◽  
Dynamic Response ◽  
Damage Detection ◽  
Convolutional Neural Networks ◽  
Structural Damage ◽  
Strain Energy ◽  
Structural Damage Detection

Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 5475-5480
Author(s):  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Structural Damage ◽  
The State ◽  
Structural Damage Detection ◽  
Bp Neural Networks ◽  
Simply Supported ◽  
Modal Flexibility

Modals of BP neural networks with different inputs and outputs are presented for different damage detecting schemes. To identify locations of structural damages, the regular vectors of changes in modal flexibility are looked on as inputs of the networks, and the state of localized damage are as outputs. To identify extents of structural damage, parameters combined with changes in flexibility and the square changes in frequency are as inputs of the networks, and the state of damage extents are as outputs. Examples of a simply supported beam and a plate show that the BP neural network modal can detect damage of structures in quantitative terms.

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder

2020 ◽  
pp. 147592172094283 ◽  
Author(s):  
Zhiqiang Shang ◽  
Limin Sun ◽  
Ye Xia ◽  
Wei Zhang
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Deep Neural Networks ◽  
Beam Model ◽  
Structural Damage Detection ◽  
Denoising Autoencoder ◽  
Vibration Data

One of the main challenges for structural damage detection using monitoring data is to acquire features that are sensitive to damages but insensitive to noise (e.g. sensor measurement noise) as well as environmental and operational effects (e.g. temperature effect). Inspired by the capabilities of deep learning methods in representation learning, various deep neural networks have been developed to obtain effective damage features from raw vibration data. However, most of the available deep neural networks are supervised, resulting in practical difficulties owing to the lack of damage labels. This article proposes a damage detection strategy based on an unsupervised deep neural network, referred to as deep convolutional denoising autoencoder, which accepts multi-dimensional cross-correlation functions as input. The strategy aims to extract damage features from field measurements of undamaged structures under the influence of noise and temperature uncertainties. In the proposed strategy, cross-correlation functions of vibration data are first calculated as basic features; then deep convolutional denoising autoencoder is developed to reconstruct cross-correlation functions from their noise-corrupted versions to extract desired features; exponentially weighted moving average control charts are finally established for these features to identify minor structural damages. The strategy is evaluated through a numerical simply supported beam model and an experimental continuous beam model. The mechanism of deep convolutional denoising autoencoder to extract damage features is interpreted by visualizing feature maps of convolutional layers in the encoder. It is found that these layers perform rough estimations of modal properties and preserve the damage information as the general trend of these properties in multiple extra frequency bands. The results show that the proposed strategy is competent for structural damage detection under the exposed environment and worth further exploring its capabilities in applications of real bridges.

STRUCTURAL DAMAGE DETECTION BASED ON CHANGES IN DYNAMIC PARAMETERS

2017 ◽  
Vol 166 (46) ◽  
pp. 75-84
Author(s):  
Dominika Ziaja ◽  
Bartosz Miller
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Single Layer ◽  
Dynamic Parameters ◽  
Structural Damage Detection ◽  
Dynamic Excitation ◽  
Portal Frame

The article presents the idea of structural health monitoring as a supporting action to protect the environment. The preliminary proposal of a procedure enabling the damage detection of the joints is shown on the example of a two-storey portal frame. Conclusions, presented in the paper, based on the measurement of accelerations in selected points of structure, subjected to dynamic excitation. Single-layer, feed-forward artificial neural networks were used as a tool for the analysis of changes in the dynamic parameters.

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