This research study focused on the dynamic response and mechanical performance of fiber-reinforced concrete columns using hybrid numerical algorithms. Whereas test data has non-linearity, an artificial intelligence (AI) algorithm has been incorporated with different metaheuristic algorithms. About 317 datasets have been applied from the real test results to detect the promising factor of strength subjected to the seismic loads. Adaptive neuro-fuzzy inference system (ANFIS) was carried out as an AI beside the combination of particle swarm optimization (PSO) and genetic algorithm (GA). Extreme Machine Learning (ELM) was also performed in order to approve the obtained results. According to the findings, it is demonstrated that ANFIS-PSO predicts the lateral load with promising evaluation indexes [R 2 (test) = 0.86, R 2 (train) = 0.90]. Mechanical performance prediction was also carried out in this study, and the results showed that ELM predicts the compressive strength with promising evaluation indexes [R 2 (test) = 0.66, R 2 (train) = 0.86]. Finally, both ANFIS-GA and ANFIS-PSO techniques illustrated a reliable performance for prediction, which encourage scholars to replace costly and time-consuming experimental tests with predicting utilities.

This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.