Enhancing assessment of in-situ beam-column strength through probing and machine learning

Beam-columns are designed to withstand the concurrent action of both axial and bending stresses. Therefore, when assessing the structural health of an in-situ beam-column, both of these load effects must be considered. Probing, having been shown recently to be an effective methodology for predicting the in-situ health of prestressed-stayed columns under axial compression, is applied presently for predicting the in-situ health of beam-columns. While the probing stiffness was sufficient for predicting the health of prestressed stayed columns, additional data is required to predict both the moment and the axial utilisation ratios. It is shown that the initial lateral deflection is a suitable measure considered alongside the probing stiffness measured at various probing locations within a revised Machine Learning (ML) framework. The inclusion of both terms in the ML framework is shown to produce an almost exact prediction of both the aforementioned utilisation ratios for various design combinations, thereby demonstrating that the probing framework proposed herein is an appropriate methodology for evaluating the structural strength reserves of beam-columns.