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Due to the complex structural issues and increasing number of design variables, a rather fast optimization algorithm to lead to a global swift convergence history without multiple attempts may be of major concern. Genetic Algorithm (GA) includes random numerical technique that is inspired by nature and is used to solve optimization problems. In this study, a novel GA method based on self-adaptive operators is presented. Results show that this proposed method is faster than many other defined GA-based conventional algorithms. To investigate the efficiency of the proposed method, several famous optimization truss problems with semi-discrete variables are studied. The results reflect the good performance of the algorithm where relatively a less number of analyses is required for the global optimum solution.

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Pedestrian bridge is a structure constructed to maintain the safety of citizens in crowded and high-traffic areas. With the expansion of cities and the increase in population, the construction of bridges is necessary for easier and faster transportation, as well as the safety of pedestrians and vehicles. In this article, it is decided to consider the most economical cross-sections for these bridges according to the design regulations and codes of Practice in order to achieve the minimum weight, which will ultimately reduce the cost of construction and production and the usage of less resources. For this purpose, new GSS-PSO algorithm has been used and its results have been compared with GA and PSO algorithms, by the means of which an enhancement of PSO algorithm is seen. This enhancement on the conventional PSO technique reduces the search space more desirably and swiftly to a space close to the global optimum point. This algorithm has been implemented with MATLAB mathematical software and has been integrated with SAP2000v22 structural design software for analysis and optimum design under resistance and displacement constraints. The final results of the analyses are compared with an already designed and implemented infrastructure. In addition to a bridge optimization, a bench-mark frame optimization was also used in order for a better comparison between this algorithm and the other ones.

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Today, due to the complexity of engineering problems and at the same time the advancement of computer science, the use of machine learning (ML) methods and soft computing methods in solving engineering problems has been considered by many researchers. These methods can be used to find accurate estimates for problems in various scientific fields. This paper investigates the effectiveness of the Adaptive Network-Based Fuzzy Inference System (ANFIS) hybridized with Teaching Learning Based Optimization Algorithm (TLBO), to predict the ultimate strength of columns with square and rectangular cross-sections, confide with various fiber-reinforced polymer (FRP) sheets. In previous studies by many researchers, several experiments have been conducted on concrete columns confined by FRP sheets. The results indicate that FRP sheets effectively increase the compressive strength of concrete columns. Comparing the results of ANFIS-TLBO with the experimental findings, which were agreeably consistent, demonstrated the ability of ANFIS-TLBO to estimate the compressive strength of concrete confined by FRP. Also, the comparison of RMSE, SD, and R² for ANFIS-TLBO and the studies of different researchers show that the ANFIS-TLBO approach has a good performance in estimating compressive strength. For example, the value of R² in the proposed method was 0.92, while this parameter was 0.87 at best among the previous studies. Also, the obtained error in the prediction of the proposed model is much lower than the obtained error in the previous studies. Hence, the proposed model is more efficient and works better than other techniques.