Multi-class Damage Detection on Experimental Frames through Transfer Component Analysis

Machine learning techniques have been revealed to be useful in damage identification applications, exploiting, for example, algorithms able to detect anomalies in the monitored data. However, these datasets are not always enough to train a damage classifier reliably. This happens because the lack, or the low number of labelled data, produces outcomes prone to overfitting and bias (due to the low attainable statistical significance of the dataset). To avoid this problem, Transfer Learning technique can be exploited to make up for the lack of data available on a structure (target) using data recorded on another structural system (source), more or less similar, which is rich in data referring to the same structural behavior to be classified (e.g., damage detection in columns due to crack propagation). In this work, a specific sub-application of Transfer Learning, named Transfer Component Analysis technique, is exploited on a benchmark system (numerically or experimentally) represented by a three-story aluminum scaled structure, subjected to increasing damage and mass variation over the three different floors. Special emphasis will be given to how accuracy is affected by data distribution, and in more detail, the authors will show how the increase in accuracy is related to the type of damage to be classified.