Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

• Published in: Fire technology Vol. 58; no. 5; pp. 2871 - 2898
• Main Authors: Tapeh, Arash Teymori Gharah, Naser, M. Z.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-09-2022; Springer Nature B.V
• Subjects: Applications programs; Artificial intelligence; Characterization and Evaluation of Materials; Civil Engineering; Classical Mechanics; Concrete; Concrete mixes; Engineering; Explainable artificial intelligence; Heating rate; Heuristic; Mixtures; Moisture content; Moisture effects; Nomograms; Physics; Problem solving; Spalling; Water content; Explainable artificial intelligence; Spalling; Concrete; Nomogram; Fire
• ISSN: 0015-2684; 1572-8099
• DOI: 10.1007/s10694-022-01290-7

Fire-induced spalling of concrete continues to be an intriguing and intricate research problem. A deep dive into the open literature highlights the alarming discrepancy and inconsistency of existing theories, as well as the complexity of predicting spalling. This brings new challenges to creating fire-safe concretes and primes an opportunity to explore modern methods of investigation to tackle the spalling phenomenon. Thus, this paper leverages the latest advancements in explainable Artificial Intelligence (XAI) to vet existing theories on fire-induced spalling and to discover solutions/heuristics to predict spalling of concrete mixtures. The developed heuristics are in the form of
graphs
and
nomograms
. The proposed solutions allow interested researchers and engineers to graphically identify the propensity of a given concrete mixture to spalling directly and with ease. For example, we report that concrete mixtures with a combination of moderate water/binder ratio (of about 0.3), low heating rate (less than 2.5°C/min), moderate rise in temperature (less than 500°C), and have moisture content (less than 3%) are expected to be less prone to spalling. Further, findings from this research showcase the potential for developing simple (i.e., one-shot) and graphical (coding-free and formula-free) XAI-based solutions and web applications to address decades-long problems in the area of concrete research.