Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge

TL;DR

This paper introduces a causal discovery and inference framework for evaluating fire resistance in reinforced concrete columns, integrating domain knowledge and traditional methods to improve understanding and prediction of fire-related phenomena.

Contribution

It presents a novel approach combining causal discovery algorithms with domain knowledge to assess fire resistance, advancing beyond traditional machine learning methods.

Findings

Causal structure reveals key variables affecting fire resistance.

Causal inference estimates variable influence under interventions.

Comparison shows causality enhances understanding over traditional models.

Abstract

Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observations we see come to be); or simply what causes such observations. Uncovering such a process not only advances our knowledge but also provides us with the capability to be able to predict phenomena accurately. This paper presents an approach that leverages causal discovery and causal inference to evaluate the fire resistance of structural members. In this approach, causal discovery algorithms are adopted to uncover the causal structure between key variables pertaining to the fire resistance of reinforced concrete (RC) columns. Then, companion inference algorithms are applied to infer (estimate) the influence of each variable on the fire resistance given a specific intervention. Finally, this study ends by contrasting the algorithmic causal discovery with that obtained from domain knowledge and traditional machine learning. Our findings clearly show the potential and merit of adopting causality into our domain.