# A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
## 5 The Hybrid Model and Results  
In this part of the study, a hybrid structure of the ANN model to evaluate RC buildings will be created using the GA, and the parameters in the model will be optimized. In a feedforward back-propagation ANN model, the outputs obtained from each neuron cell are used as input data for the neuron in the next layer. For this reason, the number of layers and the number of neuron cells in the network structure directly affect the network performance. At the same time, the activation functions and training algorithms used in neuron cells are other factors that affect the performance of the established network structure. In this framework, it is essential to create the most suitable structure for the problem when choosing the number of inputs in an ANN network structure, the number of hidden layers, the number of neurons in each layer, and the activation functions used in each layer, as well as the training algorithms. In many studies in the literature, network structures are determined by trial and error while establishing a certain success. In the network structures established by trial and error, it cannot be guaranteed that the established network is the  
Figure 14: Structure of a feedforward back-propagation ANN. most suitable or even close to it and is a successful network structure for the problem. Determining suitable structures according to the problems is almost impossible with classical methods. Even if it can be determined how well an artificial neural network has performed in experimental observations based on trial-and-error methods, determining a structure with the best performance is a separate optimization problem. The creation of the best network structure or a network structure close to the best one will only be possible by establishing a hybrid structure with optimization algorithms [65]. Validation data for the ANN network structure to train the network and obtain the optimum parameters for the suggested hybrid structure are the performance scores presented in Figure 12 that were obtained from 329 RC buildings in Bitlis, Turkey. A hybrid model was created with ANN and GA to determine the risk priorities of reinforced-concrete buildings. It is aimed that the created hybrid structure will most successfully predict the data set given in Figure 12. The flowchart of the hybrid model created for this purpose is given in Figure 15.  
As the first step in the flowchart of the hybrid structure shown in Figure 15, the initial population for GA will be created. In GA, each individual will create an ANN network structure within itself. Here, each individual consists of network parameters that directly affect the performance of the ANN. Each gene structure contains the parameters shown in Figure 15. In the individual structures presented in Figure 15, IN is the number of inputs in the network structure that will occur in each individual, HLN is the number of hidden layers in the network structure in each individual, NL is the number of neurons in the hidden layers in the network structure in each individual, AF is the activation function to be used in each layer in the network structure, and TF represents the training function to be used in each network. In the proposed hybrid structure, the initial population will be created as the first step of the GA. While generating the initial population, the IN  
Figure 15: GA-ANN hybrid model. is randomly determined among the input values given in Equation (2) for the network structures in each individual. \[\text{IN}=\left\{ \begin{array}{c}\text{Number of stories, Short column, Soft/weak story, Heavy overhang,}\\ \text{Pounding effect, Hill/slope effect, Visual quality}\end{array} \right\} \tag{2}\]  
The HLN value for each gene (G) is determined randomly according to the restriction functions given in Equation (3) and the NL values in Equation (4). \[\text{G}_{\text{a}}\text{HLN}_{\text{i}}(\text{x})=\left\{ \begin{array}{c} \text{1, x}<\text{1}\\ \text{x},\text{ 1}\leq\text{ x}\leq\text{ 10}\\ \text{10, x}>\text{20}\end{array}