Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses

Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced constructions material known for its exceptional mechanical properties and durability. Recently, machine learning (ML) methods play a pivotal role in predicting the compressive strength (CS) of UHPC and evaluating the dominant input parameters for a suitable mix design. This research, three hybrid machine learning models were utilized: Random Forest (RF), AdaBoost (AB), and Gradient Boosting (GB) algorithms with particle swarm optimization (PSO), namely AB-PSO, RF-PSO, and GB-PSO, to predict compressive strength and perform SHAP (Shapley additive explanation) analysis. To build predictive hybrid ML models, a dataset of 810 experimental data points was collected for compressive strength (CS) from published literature. Additionally, SHAP interaction plots were generated to visualize the impact of each…