Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

The data provided and the associated MATLAB code were used to build an
Artificial Neural Network Model to capture the reconstruction (recovery)
of various buildings subjected to tornado events in the State of Missouri.
The ANN model utilizes relevant tornado, societal demographic, and
structural data to determine a building’s resulting damage state from an
extreme wind event and the subsequent recovery time. Abstract for the
publication is as follows: In a companion article, previously published in
Royal Society Open Science, the authors used Graph Theory to evaluate
artificial neural network models for potential social and building
variables interactions contributing to building wind damage. The results
promisingly highlighted the importance of social variables in modeling
damage as opposed to the traditional approach of solely considering
physical characteristics of a building. Within this update article, the
same methods are used to evaluate two different artificial neural networks
for modelling building repair and/or rebuild (recovery) time. In contrast
to the damage models, the recovery models consider (A) primarily social
variables and then (B) introduce structural variables. These two models
are then evaluated using centrality and shortest path concepts of Graph
Theory as well as validated against data from the 2011 Joplin Tornado. The
results of this analysis do not show the same distinctions as were found
in the analysis of the damage models from the companion article. The
overarching lack of discernible and consistent differences in the recovery
models suggests that social variables that drive damage are not
necessarily contributions to recovery. The differences also serve to
reinforce that machine learning methods are best used when the
contributing variables are already well understood.