Simple Visual Aids for Predicting Fire Response of RC Columns: Nomograms via Machine Learning

Abstract

Assessing the ability of reinforced concrete (RC) columns to withstand the effects of fire is a multifaceted and intricate problem due to the various factors that influence their fire response. As such, engineers may find it challenging to precisely predict such fire resistance. While some codal provisions exist and fire testing/advanced modeling can be adopted, the same methods may suffer from poor predictivity and can be costly and/or complex. In this paper, we shift focus toward machine learning techniques (by means of Nomograms) that can produce simple visual aids to assess the fire resistance of RC columns. Our analysis shows that Nomograms can be accurate, account for a series of factors currently absent from our domain knowledge and provisions, and outperform existing methods adopted in building codes. Our analysis also infers that such Nomograms could be possible candidates for adoption in standardized settings, given their simplicity, ease of use, and lack of multi-stepped procedures.