## 1 Introduction
### Artificial Intelligence (AI) Techniques and Metaheuristic Algorithms  
There are different available techniques for data validations and predictions such as employing artificial neural networks [33; 34; 35; 36; 37; 38; 39], the finite element method (FEM), and the finite strip method (FSM) [40; 41]. FEM is a well-known method that has been developed by ABAQUS and ANSYS programs and conducted on a variety of experimental studies to either validate or predict the structural behavior of the specific specimens [42]. Although FEM has been widely used for prediction, it still has shortcomings such as considerable execution time, variation of outputs, and a large amount of data requisition [43; 44]. Artificial intelligence (AI) techniques are recently playing a major role in advancing engineering goals [45; 46; 47; 48; 49; 50]. Artificial neural networks (ANNs), as a sub-branch of AI techniques, are capable of solving three different types of problems comprising: (1) function approximation, (2) classification, and (3) time-series prediction [51; 52; 53; 54]. In all of the cases, a raw model of ANN is generally developed and trained by optimization techniques. Classic algorithms such as backpropagation algorithms have been basically suggested to train ANNs [55]. However, disadvantages such as being stuck in local extremums and having trouble in crossing plateaus of the error function landscape are the deficiencies of the classic algorithms [56; 57]. To address these drawbacks, metaheuristic optimization algorithms such as genetic algorithm (GA) [58], particle swarm optimization (PSO) [59], and imperialist competitive algorithm [36; 60] can be used. The global search feature of these algorithms can result in improving the performance of ANN in some cases. Over the past years, several researchers have used artificial neural networks (ANNs) and optimization techniques to solve nonlinear and sophisticated engineering problems. [PERSON], [PERSON] [61] predicted the compressive strength of concrete-filled steel tubes (CCFT) using ANN and compared their results with those of the experimental results. Reliable predictions were obtained from ANN, and it was suggested as an efficient tool in the compressive strength prediction of CCFT. The ductility of reinforced concrete (RC) beams was estimated by [PERSON], [PERSON] [62]. They concluded that ANN models could culminate in predictions that are less scattered than statistical methods. [PERSON], [PERSON] [63] and [PERSON], [PERSON] [64], [PERSON], [PERSON] [65] investigated the application of ANN in the modeling of compaction-dilation data and evaluating the performance of unconventional oil reservoirs. Satisfactory results and the great potential of ANNs for the performance evaluation of oil reservoirs were also declared throughout these investigations. [PERSON], [PERSON] [51] studied strain in the tie section of high-strength self-compacting (HSSCC) deep beams by developing an ANN model and linear regression (LR) model. In this study, the ANN model could reach performance indices that were 90 times better than those of the LR model. [PERSON] and [PERSON] [66] employed an ANN model for the rapid numerical simulation and seismic performance prediction of RC columns. Excellent agreement between the model and test results validated the application of the ANN model in this area, too. To assess the applicability of ANN in compressive strength prediction by P-, S-, and R-wave velocities, [PERSON], [PERSON] [67] conducted an investigation and concluded that considering the P-, S-, and R-wave velocities together can lead to more accurate results than only the P-wave velocity. [PERSON], [PERSON] [68] utilized a hybrid ANN-PSO model in the prediction of shear strength of squat RC walls. A comparison of this model with other predictive models revealed the superior performance of the ANN-PSO. In another interesting research, [PERSON], [PERSON] [69] employed hybrid ANN-GA and ANN-ICA models to control and secure retaining walls in dynamic conditions.