A Data-Driven Multi-Objective Approach for Predicting Mechanical Performance, Flowability, and Porosity in Ultra-High-Performance Concrete (UHPC)

TL;DR

This paper introduces a data-driven multi-objective machine learning framework, primarily using XGBoost, to accurately predict mechanical performance, flowability, and porosity of UHPC, reducing experimental testing.

Contribution

It develops a novel two-stage XGBoost-based framework with feature selection and outlier removal, enhancing prediction accuracy for UHPC properties.

Findings

XGBoost achieved the highest accuracy among tested models.

Feature selection and outlier removal improved model performance.

The framework reduces the need for extensive experimental testing.

Abstract

This study presents a data-driven, multi-objective approach to predict the mechanical performance, flow ability, and porosity of Ultra-High-Performance Concrete (UHPC). Out of 21 machine learning algorithms tested, five high-performing models are selected, with XGBoost showing the best accuracy after hyperparameter tuning using Random Search and K-Fold Cross-Validation. The framework follows a two-stage process: the initial XGBoost model is built using raw data, and once selected as the final model, the dataset is cleaned by (1) removing multicollinear features, (2) identifying outliers with Isolation Forest, and (3) selecting important features using SHAP analysis. The refined dataset as model 2 is then used to retrain XGBoost, which achieves high prediction accuracy across all outputs. A graphical user interface (GUI) is also developed to support material designers. Overall, the proposed framework significantly improves the prediction accuracy and minimizes the need for extensive experimental testing in UHPC mix design.