用机器学习精准预测超高性能混凝土的强度、流动性与孔隙率。
A Data-Driven Multi-Objective Approach for Predicting Mechanical Performance, Flowability, and Porosity in Ultra-High-Performance Concrete (UHPC)
- 通过五种高精度模型筛选，选定XGBoost作为核心预测算法。
- 优化后模型在三类性能预测上均达高准确率，减少实验测试需求。
- 提供可视化界面，助力材料设计师快速调优混凝土配方。
本研究提出一种数据驱动的多目标方法，用于预测超高性能混凝土（UHPC）的力学性能、流变性与孔隙率。在21种机器学习算法中筛选出5个高性能模型，经随机搜索与K折交叉验证调优后，XGBoost表现最佳。框架采用两阶段流程：先基于原始数据构建初始XGBoost模型，再通过移除多重共线特征、使用孤立森林识别异常值、结合SHAP分析筛选关键特征，完成数据清洗并生成优化数据集（模型2），重新训练XGBoost后，各类输出预测精度显著提升。同时开发了图形化用户界面（GUI），支持材料设计人员高效应用。整体框架大幅提高预测准确性，减少对大量实验测试的依赖。
原文摘要 · Abstract (English)
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.
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