铁道科学与工程学报2025，Vol.22Issue(2)：875-886,12.DOI:10.19713/j.cnki.43-1423/u.T20240592
基于HEMNG模型的混凝土抗压强度预测
Predicting concrete compressive strength based on HEMNG model
摘要
Abstract
Based on ensemble learning theory,artificial neural networks and the extreme gradient boosting algorithm were integrated for the first time,resulting in the proposal of a novel algorithm called HEMNG(Hybrid Ensemble Model based on Neural Networks and Gradient Boosting),aiming at more accurately predicting concrete compressive strength.A dataset of 303 concrete mix proportions for modeling,with five interpretable features including water-cement ratio,sand ratio,paste-aggregate ratio,fly ash replacement proportion,and curing age as model inputs,and compressive strength as the output.The HEMNG model was compared with artificial neural networks,extreme gradient boosting,support vector machines,random forests,etc.to analyze the advantages of the HEMNG model in predicting compressive strength.The model was migrated to new data to explore its generalization ability on unknown data.Based on the well-trained HEMNG model,a sensitivity study was carried out to quantify the influence of three important features on the compressive strength of the concrete.The results are as follows.The compressive strength of concrete can be reliably and accurately predicted using the HEMNG model with five explainable features.The fitting degree between the predicted and actual values in the test set is 0.961,and the root mean square error of 2.704.This is better than other models in prediction accuracy and generalization.By migrating the HEMNG model to new data,the predicted compressive strength values are relatively consistent with the actual strength values,the maximum absolute error is only 7 MPa,indicating good robustness.Based on the sensitivity study,optimal sand ratio exists,which maximizes the compressive strength;increasing the water-binder ratio decreases the compressive strength of the concrete,and the optimal ratio decreases with the water-binder ratio becoming higher.It shows an upward trend and then a downward trend as the paste-aggregate ratio increases,and the model can quantify the influence of each parameter on compressive strength.The developed HEMNG model provides new ideas and methods for evaluating the compressive strength of concrete.关键词
混凝土/抗压强度/预测/集成学习/可解释特征/敏感性分析Key words
concrete/compressive strength/prediction/ensemble learning/explainable features/sensitivity analysis分类
计算机与自动化引用本文复制引用
周继发,曾晓辉,谢友均,龙广成,唐卓,周智..基于HEMNG模型的混凝土抗压强度预测[J].铁道科学与工程学报,2025,22(2):875-886,12.基金项目
国家自然科学基金资助项目(52078490,11790283) （52078490,11790283）
国家重点研发计划项目(2023YFB2604304-2) （2023YFB2604304-2）
