Data for assessment of damage to residential dwellings using artificial neural networks
Data files
Nov 17, 2020 version files 191.63 KB
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bldgs_targets.mat
298 B
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BuildDamageANN_50x.m
3.91 KB
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model3_inputs.mat
1.32 KB
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SI_Data_Interpreting_Block_Box_Wind_ANN_using_Graph_Theory_060120.xlsx
186.10 KB
Abstract
The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture damage to residential home 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.
- Pilkington, Stephanie; Mahmoud, Hussam (2021), Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events, , Dataset, https://doi.org/10.5061/dryad.9kd51c5jb
- Pilkington, Stephanie; Mahmoud, Hussam (2022), Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events, , Article, https://doi.org/10.5281/zenodo.5639603
- Pilkington, Stephanie; Mahmoud, Hussam (2022), Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events, , Article, https://doi.org/10.5281/zenodo.5639602
- Pilkington, Stephanie F.; Mahmoud, Hussam N. (2020), Interpreting the socio-technical interactions within a wind damage–artificial neural network model for community resilience, Royal Society Open Science, Journal-article, https://doi.org/10.1098/rsos.200922