Machine learning for fire evacuation assessment in road tunnel fires

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0199745" target="_blank" >RIV/00216305:26110/26:0199745 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning for fire evacuation assessment in road tunnel fires

Popis výsledku v původním jazyce

Machine Learning (ML) is a promising tool to perform evacuation predictions in relatively short time. Nevertheless, to date, many algorithms exist and present different strengths and weaknesses in terms of key features such as their complexity and ability to predict given outcomes. This study investigates the use of ML to predict human consequences of road tunnel fires. Given the exploratory nature of this work, nine algorithms were trained on synthetic stochastic datasets to develop the predictive models. Key outputs adopted to evaluate the model performance included total evacuation times and number of people affected by fire. A feature removal process was also conducted to inform the development of efficient, interpretable, generalizable, and accurate predictions for supporting fire risk assessments in road tunnels.

Název v anglickém jazyce

Machine learning for fire evacuation assessment in road tunnel fires

Popis výsledku anglicky

Machine Learning (ML) is a promising tool to perform evacuation predictions in relatively short time. Nevertheless, to date, many algorithms exist and present different strengths and weaknesses in terms of key features such as their complexity and ability to predict given outcomes. This study investigates the use of ML to predict human consequences of road tunnel fires. Given the exploratory nature of this work, nine algorithms were trained on synthetic stochastic datasets to develop the predictive models. Key outputs adopted to evaluate the model performance included total evacuation times and number of people affected by fire. A feature removal process was also conducted to inform the development of efficient, interpretable, generalizable, and accurate predictions for supporting fire risk assessments in road tunnels.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20104 - Transport engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů