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 (R²) and root mean square (RMSE). In testing phase, GWO with the RMSE value of 2.5057 and R² value of 1.2510, ELM with the RMSE value of 4.52 and R² value of 1.927, and GWO–ELM with the RMSE value of 0.9340 and R² 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.

复合梁（CB）包括通过剪切连接器连接到钢部件上的混凝土板，该结构在高层建筑和桥梁等现代结构中非常流行。这项研究研究了简单支撑的CB的结构性能，其中混凝土板通过有头螺栓剪切连接器连接到钢梁上。通过经验研究确定CB的行为，除了其昂贵的过程外，还可能导致结果不准确。在这种情况下，可以将AI模型作为元启发式算法有效地用于解决难题，例如遗传算法，差分进化，Firefly算法，布谷鸟搜索算法等。优化器（GWO）来确定CB的一般行为。R ²）和均方根（RMSE）。在测试阶段，已证明RME值为2.5057且R ²值为1.2510的GWO，RMSE值为4.52且R ²值为1.927的ELM和RMSE值为0.9340且R ²值为0.9504的GWO–ELM与单独的ELM和GWO模型相比，GWO–ELM的混合可以显示更好的性能。在这种情况下，GWO–ELM可以更快，更准确并且以最小的错误百分比确定CB的一般行为，因此在本研究中，GWO–ELM的混合模型比ELM和GWO更可靠。