Assessment of composite beam performance using GWO-ELM metaheuristic algorithm

Composite beams (CBs) include concrete slabs jointed to the steel parts by the shear connectors, which highly popular in modern structures such as high rise buildings and bridges. This study has investigated the structural behavior of simply supported CBs in which a concrete slab is jointed to a steel beam by headed stud shear connector. Determining the behavior of CB through empirical study except its costly process can also lead to inaccurate results. In this case, AI models as metaheuristic algorithms could be effectively used for solving difficult optimization problems, such as Genetic algorithm, Differential evolution, Firefly algorithm, Cuckoo search algorithm, etc. This research has used hybrid Extreme machine learning (ELM)-Grey wolf optimizer (GWO) to determine the general behavior of CB. Two models (ELM and GWO) and a hybrid algorithm (GWO-ELM) were developed and the results were compared through the regression parameters of determination coefficient (R-2) and root mean square (RMSE). In testing phase, GWO with the RMSE value of 2.5057 and R-2 value of 1.2510, ELM with the RMSE value of 4.52 and R-2 value of 1.927, and GWO-ELM with the RMSE value of 0.9340 and R-2 value of 0.9504 have demonstrated that the hybrid of GWO-ELM could indicate better performance compared to solo ELM and GWO models. In this case, GWO-ELM could determine the general behavior of CB faster, more accurate and with the least error percentages, so the hybrid of GWO-ELM is more reliable model than ELM and GWO in this study.

Automated summary

This study investigated the structural behavior of simply supported Composite Beams (CBs) jointed with concrete slabs and steel parts. By using a hybrid Extreme machine learning-Grey wolf optimizer algorithm, the performance was compared, showing that the GWO-ELM hybrid model performed better and was more reliable than the solo ELM and GWO models.