This paper presents the development of systematic machine learning (ML) approach to
enable explainable and rapid assessment of fire resistance and fire-induced spalling of …

The role of machine learning (ML) continues to rise in the structural fire engineering area.
Noting the widespread of supervised ML approaches, such methods are being heavily …

One of the main challenges in using reinforced concrete materials in structures is to
comprehend their fire resistance. The assessment of fire resistance can be performed in a …

Abstract Machine learning (ML) continues to rise as an effective and affordable method of
tackling engineering problems. Unlike other disciplines, the integration of ML into structural …

The exceptional behavior of concrete under fire conditions is often jeopardized by concrete's
propensity to spall. While published works seem to agree on the complexity and …

Despite ongoing research efforts, we continue to fall short of arriving at a consistent
representation of fire-induced spalling of concrete. This is often attributed to the complexity …

The structural fire engineering community has been slowly evolving over the past few
decades. While we continue to favor a classical stand toward evaluating fire resistance of …

Concrete undergoes a series of thermo-based physio-chemical changes once exposed to
elevated temperatures. Such changes adversely alter the composition of concrete and …

The interest in probabilistic methodologies to demonstrate structural fire safety has
increased significantly in recent times. However, the evaluation of the structural behavior …

The evaluation and interpretation of the behavior of construction materials under fire
conditions have been complicated. Over the last few years, artificial intelligence (AI) has …