Machine Learning Assessment of Fire Resistance in Seismically Designed Reinforced Concrete Structures

This research investigates the effect of seismic loading on FRRs of RC structures using different machine learning (ML) algorithms. First, 20 portal RC frames with varying span numbers and stories are designed for seismic loads. This is then expanded to over 1760 frames by including further specifications such as span length, soil type, and seismic levels. This dataset is derived using decision tree algorithms, ensuring a robust and comprehensive analysis of the relationship between seismic design considerations and FRRs. All the models are subjected to the ISO 834 fire curve. Employing different ML algorithms indicate that the Random Forest Regression provides an accuracy of 81.88%, outperforming alternative algorithms such as Gradient Boosting and Support Vector Regression. Overall, the results suggest that structural elements designed for higher seismic demands exhibit higher FRRs. Additionally, as the number of spans increases, the associated FRRs also increase. An equation is then proposed to correlate the required sprinklers and the FRRs of seismically designed structures, making it possible to adopt a cost-reduction strategy in establishing fire protection systems. The ML-based algorithms here present a functional approach that can assist engineers in reducing structural and fire protection design costs while meeting the fire safety needs.