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The advancement of machine learning algorithms has opened a wide scope forvibration-based SHM (Structural Health Monitoring). Vibration-based SHM isbased on the fact that damage will alter the dynamic properties viz.,structural response, frequencies, mode shapes, etc of the structure. Theresponses measured using sensors, which are high dimensional in nature, can beintelligently analyzed using machine learning techniques for damage assessment.Neural networks employing multilayer architectures are expressive modelscapable of capturing complex relationships between input-output pairs but donot account for uncertainty in network outputs. A BNN (Bayesian Neural Network)refers to extending standard networks with posterior inference. It is a neuralnetwork with a prior distribution on its weights. Deep learning architectureslike CNN (Convolutional neural network) and LSTM(Long Short Term Memory) aregood candidates for representation learning from high dimensional data. Theadvantage of using CNN over multi-layer neural networks is that they are goodfeature extractors as well as classifiers, which eliminates the need forgenerating hand-engineered features. LSTM networks are mainly used for sequencemodeling. This paper presents both a Bayesian multi-layer perceptron and deeplearning-based approach for damage detection and location identification inbeam-like structures. Raw frequency response data simulated using finiteelement analysis is fed as the input of the network. As part of this, frequencyresponse was generated for a series of simulations in the cantilever beaminvolving different damage scenarios. This case study shows the effectivenessof the above approaches to predict bending rigidity with an acceptable errorrate.

Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning