Model-Based Structural Health Monitoring of Box Girders

Abstract

In the recent years, interest has been expressed towards incorporating Structural Health Monitoring (SHM) systems to ship hulls in order to transition from preventive to predictive maintenance procedures. In this work, an initial approach is undertaken to investigate the capabilities of a model-based method treating damage identification as an optimization problem solved using a genetic algorithm. An idealization of the hull structure is considered based on hull girder theory that allows for lab scale experimental testing. Specifically, a box girder is considered with a circular discontinuity as the generalized damage that causes extensive stress redistribution, replicating the effect of hull damage modes of interest. A three-point bending load case is considered to emulate still water bending loads. Damage is considered to exist, and the goal of the proposed strategy is to provide a prediction on its location and magnitude (level 2 SHM). This is achieved using strain measurements obtained from sensors located on theoretical zero-strain directions as inputs to the optimization scheme treating the damage identification problem. Results from both assessment strategies highlighted the influence of measurement-related uncertainties on the method’s predictive capabilities.

A Probabilistic Reduced Order Modeling Framework for the Design of Composite Scaffolds in Bone Tissue Engineering

“…In particular, a BNN is utilized to map the parameter vectors to the extracted linearly projected data. BNNs, as part of the probabilistic machine learning, allow for the calculations of the epistemic uncertainties, related to the weights of the artificial neural networks (ANNs), avoiding the limitations of the deterministic models by providing the confidence of intervals in the ROM predictions [15].…”