Development of a Flashover Prediction Model for Compartment Fires Using Support Vector Regression

Published

April 1, 2022

Author(s)

Wai Cheong Tam, Jun Wang, Richard D. Peacock, Paul A. Reneke, Eugene Yujun Fu, Thomas Cleary

Abstract

This report provides additional technical details to an article entitled P-Flash – A Machine Learning-based Model for Flashover Prediction using Recovered Temperature Data. Research was conducted to examine the use of Support Vector Regression (SVR) to build a model to forecast the potential occurrence of flashover in a single-floor, multi-room compartment fire. Synthetic temperature data for heat detectors in different rooms were generated, 1000 simulation cases are considered, and a total of 8 million data points are utilized for model development. An operating temperature limitation is placed on heat detectors where they fail at a fixed exposure temperature of 150 ̊C and no longer provide data to more closely follow actual performance. The forecast model, P-Flash (Prediction model for Flashover occurrence), is developed to use an array of heat detector temperature data, including in adjacent spaces, to recover temperature data from the room of fire origin and predict potential for flashover. Two special treatments, sequence segmentation and learning from fitting, are proposed to overcome the temperature limitation of heat detectors in real-life fire scenarios and to enhance prediction capabilities to determine if the flashover condition is met even with situations where there is no temperature data from all detectors. Experimental evaluation shows that P-Flash offers reliable prediction. The model performance is approximately 83 % and 81 %, respectively, for current and future flashover occurrence, considering heat detector failure at 150 ̊C. Results demonstrate that P-Flash, a new data-driven model, has potential to provide fire fighters real-time, trustworthy, and actionable information to enhance situational awareness, operational effectiveness, and safety for firefighting.

Citation

Technical Note (NIST TN) - 2163

Report Number

2163

NIST Pub Series

Technical Note (NIST TN)

Pub Type

NIST Pubs

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Keywords

Machine learning, flashover prediction, fire modeling, heat detector, smart firefighting

Citation

Tam, W. , Wang, J. , Peacock, R. , Reneke, P. , Fu, E. and Cleary, T. (2022), Development of a Flashover Prediction Model for Compartment Fires Using Support Vector Regression, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932165 (Accessed December 3, 2024)