###### Abstract  
In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.  
crack widths; RC beam-column joint; drift ratio; machine learning; prediction models +
Footnote †: journal: Medical  
of cracks, and the average crack width [17,18]. In recent years, several analytical methods and neural network models have been developed for estimating the propagations of cracks. In the past decade, Artificial Neural Network (ANN) and Finite Element Modeling (FEM) have been extensively used to analyze and predict the formation and propagation of cracks [19, 20, 21, 22, 23]. [PERSON] and [PERSON] developed a theoretical model for predicting crack width based on the effect of Fiber Reinforced Polymer (FRP) numbers and thickness against the reinforcement ratio in concrete beams and prisms [24,25]. In other research, the mechanism of softened truss theory and bonding deterioration was proposed and developed using ANN and numerical modeling to estimate the crack width among reinforced concrete elements [26,27]. Such theoretical models have been able to predict features of cracks within the surface of reinforced concrete structures with reasonable accuracy.  
In a recent survey of existing research, it was illustrated that there is a need for developing models to determine the crack widening process for reinforced concrete structures exposed to seismic loads [27]. It was noted that models did not predict the cracking behavior of reinforced concrete beam-column joint members subjected to lateral cyclic loading. Therefore, a reliable approach is needed for engineering practice to develop a model to estimate a crack width for members with complex structures. In this research, a machine learning model was developed to predict the cracking behavior and estimate the crack width. Four different types of prediction models were developed using Rapid-miner machine learning software. Design parameters of RC beam-column joint, such as numbers of shear links, anchorage length, and lateral cyclic loading in terms of drift ratio, have been used as inputs for the neural network models. The outperforming model was selected based on the least error in the prediction of crack width. Further optimization has been performed using data splitting, changing the number of input parameters and performance indexes.