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.