A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings

Determining the risk priorities for the building stock in highly seismic-prone regions and making the final decisions about the buildings is one of the essential precautionary measures that needs to be taken before the earthquake. This study aims to develop an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock. For this purpose, the network parameters in the network structure have been optimized by establishing a hybrid structure with the Genetic Algorithm (GA). As a result, the ANN model can make accurate predictions with maximum efficiency. The suggested ANN model is a feedforward back-propagation network model. It aims to predict the risk priorities for 329 RC buildings in the most successful way, for which the performance score was calculated using the Turkey Rapid Evaluation Method (2013). In this paper, a GA-ANN hybrid model was implemented in which the ANN, using the most successful gene revealed by the model, produced successful results in calculating the performance score. In addition, the required input parameters for obtaining more efficient results in solving such a problem and the parameters that need to be used in establishing such an ANN network structure have been optimized. With the help of such a model, the operation process will be eliminated. The created hybrid model was 98% successful in determining the risk priority in RC buildings.

Automated summary

This study develops an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings in highly seismic-prone regions. The model achieves accurate predictions with maximum efficiency and a success rate of 98% in determining risk priorities.