Metaheuristic-based pruning for efficient structural health monitoring

In recent decades, various techniques have been developed to support structural health monitoring (SHM). These approaches have benefited significantly from advances in artificial intelligence and big data, which have enabled the detection of structural damage with increasing accuracy. Although such methods allow for automated and real-time assessment of structural integrity, their deployment is often constrained by hardware requirements because of the high computational demands of machine learning (ML) models. These limitations may hinder the practical implementation of automated SHM systems in real-world scenarios. Although pruning techniques aimed at reducing model size by removing specific components of the neural network have been proposed to address this issue, they often suffer from limitations that may be mitigated through alternative approaches, which remain underexplored. In this study, we propose a novel pruning method that integrates metaheuristic optimization techniques to identify the optimal set of neurons to prune. The proposed approach is evaluated using a real-world case study in which the size of an ML model trained to detect structural damage is reduced. The results show that the proposed technique can reduce the model size by 45%, with only a 0.16 and 0.43% decrease in damage detection performance, measured in terms of F1 score and Area Under Curve (AUC) score, respectively.