Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

Abstract

A pattern recognition approach for structural health monitoring (SHM) is presented that uses damageinduced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology.

Recent developments of Bayesian model class selection and applications in civil engineering

“…ANN design here is referred to the determination of the number of hidden layers and the number of neurons in each hidden layer [30,19]. Here, N I and N O are the number of the neurons in the input and output layers, respectively.…”

A Bayesian model framework for calibrating ultrasonic in-line inspection data and estimating actual external corrosion depth in buried pipeline utilizing a clustering technique

“…the model with the optimum number of clusters and the best covariance structure) achieves high similarities within each cluster and low inter-similarities between clusters [26,27]. Several studies have been performed on model selection such as the Akaike information criterion (AIC), the Bayesian information criteria (BIC) [28], neural network methods [29,30] and a Bayesian approach that is combined with Monte Carlo integration [31]. The BIC, unlike AIC, considers the dimension of input space by including the number of features as a penalty [28,32], and thus the BIC is more sensible.…”

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

“…2017, 7, 391 11 of 20 In accordance with the procedure described in Section 3.2, the AANNs are employed to extract damage features from the WPNEs. By the trial-and-error method, the optimal number of neurons in the bottleneck layer of the AANNs is determined as 5; by the rule stated in Equation (12), the number of neurons in the mapping layer as well as the remapping layer is given as 20. When the WPNEs are individually placed in the input layer and the output layer, the AANNs can be driven to train under the control of the cost function, i.e., MSE.…”

“…An active branch of vibration-based damage identification is damage pattern recognition based on artificial intelligence methods [11][12][13][14][15]. The most popular artificial intelligence method used…”