Archaeoinformatics - Data Science

BA: Wave-based Damage Detection in Engineering Structures using Artificial Neural Networks

Supervisors:

Prof. Dr. Matthias Renz

Christian Beth, M.Sc.

Steffen Strohm, M.Sc.

 

ResNet18

Abstract

Structural health monitoring plays a critical role in various disciplines of engineering. The less critical structures are monitored at a specified duration, with the conventional method comprising an array of sensors to detect damage and being tedious, labour intensive, and lengthy. However, the critical infrastructure applies the idea of digital twins where various physical properties are continuously measured and processed to estimate damage location and intensity. The other technique that serves the purpose is the numerical method which offers a versatile solution for scenario forecasting of cracks with location, orientation and length. Numerical methods could generate the response of crack-wave interactions but require a respectable amount of computation power. In this study, crack wave interaction data is generated from the Lattice Element Method and used to train the neural network model to predict the location, orientation and length of the cracks. A 1D-ResNetDense50, 1D-ResNetDecoder34, 1D-ResNetDecoder18 and 1D-SimpleCNN networks have been implemented in the framework to detect cracks. The work further explores the structure of all models and other selected essential components, such as the loss functions, metric, optimizer, learning rate and threshold. Necessary steps are taken to achieve high accuracy, high precision, eliminate possible error sources to get better performance and retain more information about investigated data.