In this work, a review of current literature on artificial intelligence (AI) and more specifically machine learning (ML) is presented. ML is illustrated by two case studies where artificial neural networks are used for regression analysis of 110 spalling experiments and 81 Fire Dynamics Simulator (FDS) models of tunnel fires. Tunnel fires are often assessed by fire safety engineers using time-consuming simulation tools where a trained model has the potential to significantly reduce time and cost of these assessments.

A regression model based on a neural net is used to study small scale spalling experiments and similar accuracy compared to least-square fits are obtained. The result is a function based on 14 determining experimental parameters of spalling and result in, spalling times and depths. It is a relatively small effort to get started and set up models, comparably to regular curve fitting. In this first case study the training times are short, it is thus possible to establish how the model performs on average.

The 81 tunnel fire simulations are trained using a similar neural net however it takes considerable time to organize data, creating input, target data of the desired format and training. Here, it is also crucial to normalize the data in order to have it in a suitable format when training. 

It should be noted that ML is often an iterative process in such a way that it may be difficult to know what settings will work before starting the process. It is equally important to illustrate and get to know the data, e.g., if there are large differences or orders of magnitude differences in the data. A normalization procedure is most often practical and will give better predictions.