An Artificial Neural Network for Wind-Induced Damage Potential to Nonengineered Buildings

Extreme winds such as hurricanes and tornadoes can be extremely destructive and result in catastrophic property losses and loss of human lives. The need to predict damage and reduce loss of life and property is becoming more important with increasing urban sprawl. Artificial Neural Networks (ANNs) provide a novel approach for representing the wind-induced damage potential prediction model. Modeled loosely after the biological neural networks of the human brain, ANNs are generally used in situations where the interactions between the input and the output variable are too complicated for an analytical solution or where there is not sufficient understanding of the problem domain. Predicting wind-induced damage potential to nonengineered buildings is not a simple task because of the complexity of construction and limited understanding of the wind effects on buildings. This research concentrates on the investigation of the applicability of ANNs to wind-induced damage potential prediction and the corresponding implementation issues. Even after years of post disaster windstorm damage investigations consistent, complete and robust damage information is not available to train the ANN. Thus, synthetic data instead of observed building damage information is used. WIND-RITE*, a knowledge based expert system for grading individual buildings in windstorms, is used to provide the necessary damage information for the synthetic data. This research shows that a feedforward multi-layer neural network with a modified backpropagation learning algorithm can be used effectively to model wind-induced damage potential predictions for nonengineered buildings. As few as four hundred building samples are sufficient to train the network to learn the underlying relationships between the features of the building and its corresponding building damage potential. During training the ANN model is able to learn the relationships between the input features and the resulting building damage grade effectively. It was also found that the ANN is able to predict reasonably for samples it has not seen before.

The approach presented in this work can be used effectively for other building categories. Also when sufficient real wind-induced building damage information is available this approach of using ANNs will give a more realistic representation of the relationships existing between the building characteristics and the resulting wind-induced damage.