Bayesian-Optimized Surrogate Framework for Cost-Effective Design of Composite Steel–Concrete Beams

Structural design codes provide safe procedures for verifying steel–concrete composite beams but do not directly guide engineers toward cost-effective configurations. This study aims to develop and evaluate a surrogate-assisted framework that combines a Multilayer Perceptron neural network with Bayesian Optimization for the preliminary flexural-resistance and material-cost optimization of simply supported steel–concrete composite beams designed according to the Brazilian code NBR 8800. A dataset containing 20,000 beam configurations and 15 input variables was generated using a Python-based analytical routine that implements the NBR 8800 provisions for the positive bending resistance of composite beams. The generated dataset was used to train a Multilayer Perceptron neural network to predict the design bending resistance. The trained surrogate model was then integrated with Bayesian Optimization to search a discrete design space comprising commercial steel profiles, concrete slab thicknesses, shear connector quantities, and connector diameters. The selected neural network architecture achieved validation MAE and RMSE values of 2.128 kN·m and 3.013 kN·m, respectively, with an R2 of 0.9999. In ten benchmark scenarios, the BO–MLP framework identified candidate solutions using only 60 objective-function evaluations. This corresponds to 3% of the evaluation budget adopted for GA and PSO and approximately 0.057% of the configurations examined by exhaustive search. Despite this limited sampling budget, the resulting candidate solutions presented an average optimality gap of approximately 12.1% relative to the global reference. In computational terms, GA and PSO required approximately 4.1 and 4.9 times the execution time of BO–MLP, respectively, while exhaustive search required approximately 18.4 times the execution time. Overall, the proposed framework offers a computationally efficient means of exploring discrete composite-beam configurations and identifying cost-competitive candidate solutions. Direct NBR 8800 verification showed that seven of the ten selected candidates satisfied the resistance requirement, while three presented resistance-to-demand ratios slightly below unity. Therefore, the framework should be used as a preliminary screening tool, with the selected configurations subsequently verified using the complete code-based procedure. Within the restricted structural domain investigated, the framework can support preliminary decisions related to positive bending resistance and material cost.