Sameer Gopali

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

Committee Chair

Huaming Zhang

Committee Member

Gang Wang

Committee Member

Deepak Acharya

Research Advisor

Huaming Zhang


Deep learning (Machine learning), Lamb waves, Cantilevers--Nondestructive testing, Structural health monitoring


Guided Lamb waves offer a promising solution for the early detection of internal damages in structures due to their high sensitivity to small damages. However, noise can adversely impact the development of data-driven models for damage detection, leading to inaccurate monitoring systems. This thesis explores deep learning techniques to robustly predict the location and severity of damage in cantilevered beams using noisy guided wave responses. Initially, Multi-Layer Perceptron (MLP) is trained with frequency domain features to achieve robust performance against noisy data. Further performance improvement is achieved using end-to-end learning models, which include autoencoder and one-dimensional Convolutional Neural Network (CNN). The autoencoder demonstrates better dimensionality reduction compared to frequency-based feature extraction while also exhibiting better performance. The one-dimensional CNN model outperforms other techniques, achieving an R2 score of 0.9908 in the highest noise level settings. These results facilitate the development of robust structural health monitoring using deep learning techniques.



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