Cost-informed dimensionality reduction for structural digital twin technologies
This work addresses the need for cost-effective asset management decisions in structural engineering, though it is incremental as it builds on existing dimensionality reduction techniques with a cost-focused twist.
The paper tackles the problem of high-dimensional feature spaces reducing predictive performance in structural digital twin classification models by proposing a decision-theoretic dimensionality reduction method that minimizes misclassification costs, demonstrated through a synthetic case study.
Classification models are a key component of structural digital twin technologies used for supporting asset management decision-making. An important consideration when developing classification models is the dimensionality of the input, or feature space, used. If the dimensionality is too high, then the `curse of dimensionality' may rear its ugly head; manifesting as reduced predictive performance. To mitigate such effects, practitioners can employ dimensionality reduction techniques. The current paper formulates a decision-theoretic approach to dimensionality reduction for structural asset management. In this approach, the aim is to keep incurred misclassification costs to a minimum, as the dimensionality is reduced and discriminatory information may be lost. This formulation is constructed as an eigenvalue problem, with separabilities between classes weighted according to the cost of misclassifying them when considered in the context of a decision process. The approach is demonstrated using a synthetic case study.