Development of a Machine Learning based model for Damage Detection, Localization and Quantification to extend Structure Life

Abstract

Structural Health Monitoring (SHM) has been researched for a long time and continues to be an active area of research. Initial work on SHM involved identification of hand-crafted features and predictive models relied on statistical methods. The recent improvements in computing capabilities, coupled with better integration of sensor data, has led to the emergence of more effective techniques in terms of scalability and predictive power. Machine learning offers a solution through automatic feature extraction algorithms, and scalable and noise robust models. Convolutional Neural Networks (CNN) have been used as state-of-art classifiers for images as well as for text. This paper proposes the use of the monitored structure’s transmissibility functions for the structure under observation, which can be fed into a novel composite architecture consisting of Deep CNN followed by multivariate linear regressors to detect, localize, and quantify the damage extent in a system. The proposed method was tested on the Los Alamos’ Eight degree-of-freedom (DOF) structure, and the Structural Beam Data from Laboratory of Mechanical Vibrations and Rotor Dynamics, University of Chile. This study on damage localization and quantification can be leveraged to comment on the safety and soundness of the structure under inspection and can help in making more informed inferences. It is expected that, in general, this will lead to extended structure life, which not only improves the resource utilization in terms of structure maintenance and its longevity but also decreases the carbon footprint and capital expenditure.