Multi-Objective Optimization Design of FRP Reinforced Flat Slabs under Punching Shear by Using NGBoost-Based Surrogate Model

Multi-objective optimization problems (MOPs) in structural engineering arise as a significant challenge in achieving a balance between prediction accuracy and efficiency of the surrogate models, which are conventionally adopted as mechanics-driven models or numerical models. Data-driven models, such as machine learning models, can be instrumental in resolving intricate structural engineering issues that cannot be tackled through mechanics-driven models. This study aims to address the challenges of multi-objective optimization punching shear design of fiber-reinforced polymer (FRP) reinforced flat slabs by using a data-driven surrogate model. Firstly, this study employs an advanced machine learning model, namely Natural Gradient Boosting (NGBoost), to predict the punching shear resistance of FRP reinforced flat slabs. The comparisons with other machine learning models, design provisions and empirical theory models illustrate that the NGBoost model has higher accuracy in predicting the punching shear resistance. Additionally, the NGBoost model is explained with Shapley Additive Explanation (SHAP), revealing that the slab's effective depth is the primary factor affecting the punching shear resistance. Then, the formulated NGBoost model is adopted as a surrogate model in conjunction with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm for multi-objective optimization design of FRP reinforced flat slabs subjected to punching shear. Through a case study, it is demonstrated that the Pareto-optimal set of the punching shear resistance and cost of the FRP reinforced flat slabs can be successfully obtained. By discussing the effects of design parameter changes on the results, it is also shown that increasing the slab's effective depth is a relatively effective way to achieve higher punching shear resistance of FRP reinforced flat slabs.