# A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings
## 1 Introduction
Some examples of ANN applications in the field of structural engineering include developing prediction models for the compressive strength and slump of concrete [27; 28], prediction of the strength capacities of reinforced concrete members under different internal stresses [29; 30], shear strength of reinforced masonry walls [31], a prediction model for lateral-torsional buckling resistance of slender steel cellular beams [32], structural crack detection [33], design and compression capacity prediction of concrete-filled steel tubular columns [34; 35], compressive strength prediction of self-compacting and high-performance concrete [36], predicting the mechanical behaviour of semi-rigid steel structure connections [37], predicting cyclic hysteresis behaviour of reduced beam sections in steel buildings [38], damage assessment of pre-stressed concrete beams [39], fire resistance prediction of RC T-beams strengthened with carbon fibre reinforced polymers [40], structural health monitoring and damage identification of bridges [41; 42], seismic damage prediction of RC buildings [43], and establishment of prediction models for the damage state, damage ratio, and seismic vulnerability assessment of buildings [44; 45; 46; 47]. Additionally, [PERSON] and [PERSON] (2021) determined that the most influential parameters resulting from machine learning are floor area, building height, lateral-resisting frame weight, and average beam section dimensions by making simulation-based seismic and environmental assessments of 720 mid-rise reinforced-concrete office buildings in South Carolina [48]. [PERSON] et al. (2022) proposed a framework that is sensitive to the uncertainties found in the optimization of engineering structures. The algorithm proposed for optimal shape design for gravity dams in this study includes a series of local and global time-varying/invariant performance indices [49]. [PERSON] et al. (2022) created a database to support post-earthquake damage and recovery modelling by considering 3695 buildings affected by the 2014 South Napa, California earthquake. The Napa dataset indicated the usability of different modelling techniques to validate post-earthquake recovery simulation methodologies and predict damage [50]. [PERSON] et al. (2020) have created an open-access Inventory of Seismic Structural Evaluations, Performance Functions, and Taxonomies for Buildings (INSSEPT) that includes evaluations in terms of performance-based earthquake engineering for 222 buildings. The study provides data to determine the regional seismic risk analysis for buildings [51]. [PERSON] et al. (2022) developed a new stacked long-short-term memory (LSTM) network for fragility curves that vary depending on many properties. In this network, where overlapping data with the previous stack is taken into account to connect each stack, the temporal dimension and time for training are shortened. The flexible model can be used in different structures [52]. [PERSON] et al. (2022) developed a multivariate classifier with multiple IMs to estimate earthquake intensity and diversity in a fragility estimation. The generating classifier, seismic fragility, and earthquake damage estimations can be made [53]. [PERSON] et al. (2022) proposed a massive method to reveal the seismic vulnerability of the existing building stock. With the help of neural networks, the capacity curves of low-rise RC buildings can be obtained with a minimum error [54]. [PERSON] and [PERSON] (2022) proposed a deep neural network (DNN)-based framework to determine post-earthquake structural damage. In the study, a steel structure was chosen as a sample building and two different earthquake records were applied to this structure. With the proposed framework, the damage that may occur after the earthquake is correctly defined in time [55]. On the other hand, GA is a subset of evolutionary computing techniques that are metaheuristic algorithms with key characteristics of being bio-inspired, population-based, and stochastic.