Negative selection algorithm based methodology for online structural health monitoring

Abstract

Even small damages in engineered systems can endanger the users or lead to significant economic costs. The detection of damage existence is only the first task within the Damage Identification strategy, nonetheless, it is still very complex, especially for Civil Engineering products. The present study is devoted to the analysis of a global methodology for damage detection based on a recently developed version of the Negative Selection Algorithm (NSA). Given any new monitored data measured from the system, the methodology addresses the one-class classification problem by labelling it as normal or abnormal, thus potentially damaged. The strategy is independent of the engineered system under investigation and of the damage-sensitive features used to assess the state, as long as only two features per time are analysed by the classifier. The methodology includes a set of features meant to tackle typical shortcomings and open issues which might prevent an effective application of NSA to damage detection. Some of them are shared with other soft computing techniques for classifications. Finally, the methodology is applied to the data collected from a scaled masonry arch built and tested at the University of Minho (Portugal). The datasets are used to validate the algorithm features, to compare the NSA with other well-known classification algorithms and to simulate a real monitoring to assess the readiness of the algorithm response.