西华大学学报（自然科学版）2024，Vol.43Issue(3)：84-90,7.DOI:10.12198/j.issn.1673-159X.4974
基于稀疏多项式混沌展开模型的钢筋混凝土结构长期挠度预测
Long-term Deflection Prediction of Reinforced Concrete Structures Based on Sparse Polynomial Chaos Expansion
摘要
Abstract
Long-term deflection prediction of reinforced concrete flexural structures is important for evaluating their serviceability and safety throughout their life cycle.Since it is difficult to consider all the influencing factors by empirical methods,in order to be able to accurately predict the long-term deflection of reinforced concrete structures.In this paper,we propose to predict the long-term deflection of reinforced concrete structures by using a sparse polynomial chaos expansion(PCE)model and perform global sensitiv-ity analysis of the parameters affecting the deflection of the structure.Sparse PCE models are built and eval-uated by using experimental datasets,compared with common surrogate models(RBF,SVR and Kriging)and common machine learning model(BP neural network).The models are trained and tested by using a ten-fold cross-validation algorithm.The results show that the sparse PCE model outperforms both common surrogate models and machine learning models in predicting the long-term deflection of reinforced con-crete structures,with correlation coefficients R2,relative average absolute error(RAAE),relative maximum absolute error(RMAE),and root-mean-square error(RMSE)of 0.970,0.108,0.537,and 0.062,respect-ively.Moreover,the sparse PCE model's RMSE value is much better than the empirical method.Finally,the parameters affecting structural deflection were ranked in order of importance based on the results of global sensitivity analysis of the sparse PCE,where the instantaneous or immediate measured deflection a(i),span-to-depth ratio l/h,and age t'concrete strength fc'are more important,and successively decreasing.The sparse PCE model can be used for long-term deflection prediction of reinforced concrete structures and can be evaluated to quantify the key factors affecting the long-term suitability of reinforced concrete struc-tures.关键词
钢筋混凝土结构/挠度预测/多项式混沌展开/代理模型/机器学习/全局灵敏度分析Key words
reinforced concrete structures/deformation prediction/polynomial chaos expansion/surrogate model/machine learning/global sensitivity analysis分类
建筑与水利引用本文复制引用
岳鑫鑫,张健,马露,于敏,常山,但文蛟..基于稀疏多项式混沌展开模型的钢筋混凝土结构长期挠度预测[J].西华大学学报（自然科学版）,2024,43(3):84-90,7.基金项目
国家自然科学基金资助项目(11872190) （11872190）
安徽高校自然科学研究重大项目(KJ2021ZD0111) （KJ2021ZD0111）
安徽省高校自然科学重点项目(KJ2021A0862) （KJ2021A0862）
安徽省高校优秀青年人才支持计划项目(gxyq2022052) （gxyq2022052）
安徽省教育厅重点项目(2023AH051841,2023AH040274) （2023AH051841,2023AH040274）
安徽科技学院引进人才项目(JZYJ202109). （JZYJ202109）
