An Evolutionary Approach to Learning Neural Networks for Structural Health Monitoring

摘要

For widespread adoption of Structural Health Monitoring (SHM) strategies, a key challenge is to produce tools which automate the creation and learning of effective algorithms with minimal input from expert practitioners. Classification of damage in a structure is one of the key steps in constructing a useful SHM system. The artificial neural network has been shown, for a number of years, to be a powerful tool for building such a classifier. While the learning of the parameters in the network has been widely addressed in the literature, the hyperparameters of the model (i.e. the topology) remain a challenge. This paper investigates the use of an evolutionary algorithm tailored to this learning problem, namely, Neuro-Evolution of Augmenting Topologies. The effectiveness of this approach is considered with regard to classification accuracy, user input, and generalisation. A benchmark structure a representative three-storey building is used to demonstrate the use of this methodology.