  The advancement of machine learning algorithms has opened a wide scope for
vibration-based SHM (Structural Health Monitoring). Vibration-based SHM is
based on the fact that damage will alter the dynamic properties viz.,
structural response, frequencies, mode shapes, etc of the structure. The
responses measured using sensors, which are high dimensional in nature, can be
intelligently analyzed using machine learning techniques for damage assessment.
Neural networks employing multilayer architectures are expressive models
capable of capturing complex relationships between input-output pairs but do
not account for uncertainty in network outputs. A BNN (Bayesian Neural Network)
refers to extending standard networks with posterior inference. It is a neural
network with a prior distribution on its weights. Deep learning architectures
like CNN (Convolutional neural network) and LSTM(Long Short Term Memory) are
good candidates for representation learning from high dimensional data. The
advantage of using CNN over multi-layer neural networks is that they are good
feature extractors as well as classifiers, which eliminates the need for
generating hand-engineered features. LSTM networks are mainly used for sequence
modeling. This paper presents both a Bayesian multi-layer perceptron and deep
learning-based approach for damage detection and location identification in
beam-like structures. Raw frequency response data simulated using finite
element analysis is fed as the input of the network. As part of this, frequency
response was generated for a series of simulations in the cantilever beam
involving different damage scenarios. This case study shows the effectiveness
of the above approaches to predict bending rigidity with an acceptable error
rate.
