Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge
This work addresses fire resistance evaluation for structural engineering, but it is incremental as it applies existing causal methods to a new domain without major methodological innovations.
The paper tackled the problem of evaluating fire resistance in structural members by applying causal discovery and inference algorithms to uncover and estimate causal relationships between variables affecting reinforced concrete columns, showing the potential of causality in this domain.
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.