# A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
## 1 Introduction
Inspired by biological mechanisms for operations on chromosomes, reproduction, crossover, and mutation, this algorithm can perform parallel mathematical computations, examine several input variables and their fitness simultaneously, and refine the search and find the best candidate solution that satisfies the objective function [21]. Owing to its capabilities in manipulating many variables, discrete variables, consideration of constraints on possible solutions, and simultaneous search and provision of alternative solutions for a single problem, GA has found a variety of applications in identification, multi-objective optimization, and simulation problems. The dynamic and static identification of base-isolated bridges [56], optimal sensor placement for the identification of structural parameters [57], structural damage detection [58; 59], topology and shape optimization of free-form space frames [60; 61], optimal design of a passive control strategy for seismic protection of a wall-frame system [62], and the optimization of fuzzy rule weights for the development of global damage levels of buildings [63] can be mentioned among many other applications of GA in structural and earthquake engineering. In the studies conducted in the literature, the input parameters used in the ANN network structure, the number of hidden layers, the number of neurons in the hidden layers, the activation functions used in the neurons, and the learning algorithm of the network are determined by trial-and-error methods. The network parameters found here are directly effective in producing the most successful results of the network [64]. Different ANN network structures built on the same problem produce different results. In the network structure used in this study, the network parameters form a five-dimensional solution space. Determining the parameters to produce the best results for the network within this solution space is almost impossible by trial and error because there are nearly infinite solutions in such a solution space. Even if the models established according to the network parameters determined by trial and error in the literature achieve an inevitable success, studies on whether the established network is the most successful network or a network structure close to the most successful network structure are insufficient. In such a solution space, the most successful network structure or network structure parameters close to the most successful network structure can be determined through optimization algorithms. In determining the desired ANN structure for solving a problem, establishinghybrid structures with optimization algorithms is mandatory to produce the most successful result [65]. Within the scope of this study, it aims to predict the most successful risk priorities by implementing the results obtained from 329 RC buildings using an ANN and a GA. For this purpose, all suitable parameters found in an ANN network have been determined by establishing a hybrid structure with the GA. The chromosomes in each gene in the GA constitute the parameters in the network structure. The network model established to obtain the best network parameters is a feedforward back-propagation network model. Feedforward back-propagation networks are frequently used in the literature and can achieve successful results [65]. Thanks to the capability of the hybrid structure, the necessary network parameters for the rapid evaluation of reinforced-concrete buildings and the input parameters that should be used in solving such a problem are optimized. The proposed hybrid model enables rapid evaluations of existing RC buildings while decreasing classical approaches' time, energy, and costs. In this study, a hybrid model was used for the first time for the Turkish Rapid Assessment Method, which is used to determine regional risk priorities in RC buildings. The performance score was obtained for each building using the parameters considered in this method. Since the method is a rapid evaluation method, detailed structural analyses were not needed at this stage.