A Novel Data-Driven Machine Learning Strategy for Structural Design of Reinforced Concrete Slabs in Real-World High-Rise Buildings
This paper proposes a data-driven strategy leveraging the XGBoost (Extreme Gradient Boosting) algorithm to accurately predict the required reinforcement and the deflection of reinforced concrete (RC) slabs in real-world high-rise buildings. To achieve the objective, two distinct predictive models with tailored input parameters are developed. For estimating the required reinforcement ratio, the model incorporates the span direction, location, span length, slab thickness, beam height, and the design strengths of both concrete and steel. Meanwhile, for assessing the maximum deflection of the slab, the input parameters are streamlined to the span direction, span length, concrete strength grade, slab thickness, and the reinforcement area. The model evaluation results demonstrate robust predictive performance as a fast, finite element analysis-based surrogate tool, achieving a coefficient of determination (R2) of 0.971 for reinforcement ratio estimation and 0.974 for deflection prediction when validated against code-compliant finite element analysis data from a real-world high-rise building. Furthermore, the model’s cross-project transferability is successfully verified on an independent structural project within the same design-code domain. Overall, this paper demonstrates that the proposed dual-architecture XGBoost framework effectively approximates time-consuming finite element calculations, significantly accelerating preliminary design iterations while contributing to the advancement of data-driven civil engineering workflows.