文献类型：期刊文献

英文题名：Artificial Neural Network Model for Predicting Mechanical Strengths of Economical Ultra-High-Performance Concrete Containing Coarse Aggregates: Development and Parametric Analysis

作者：Li, Ling[1];Gao, Yufei[1];Dong, Xuan[1];Han, Yongping[2]

第一作者：Li, Ling

通讯作者：Li, L[1];Han, YP[2]

机构：[1]Northeast Forestry Univ, Sch Civil Engn & Transportat, 26 Hexing Rd, Harbin 150040, Peoples R China;[2]Beijing Union Univ, Biochem Engn Coll, Fatou Xili Dist 3, Beijing 100023, Peoples R China

第一机构：Northeast Forestry Univ, Sch Civil Engn & Transportat, 26 Hexing Rd, Harbin 150040, Peoples R China

通讯机构：[1]corresponding author), Northeast Forestry Univ, Sch Civil Engn & Transportat, 26 Hexing Rd, Harbin 150040, Peoples R China;[2]corresponding author), Beijing Union Univ, Biochem Engn Coll, Fatou Xili Dist 3, Beijing 100023, Peoples R China.|[1141726]北京联合大学生物化学工程学院;[11417]北京联合大学;

年份：2024

卷号：17

期号：16

外文期刊名：MATERIALS

收录：;EI(收录号：20243516970762);Scopus(收录号：2-s2.0-85202437938);WOS:【SCI-EXPANDED(收录号:WOS:001304837100001)】；

基金：This research was funded by the National Science Foundation of China under Grant No. 52208148.

语种：英文

外文关键词：back-propagation artificial neural network; UHPC-CA; compressive and flexural strength; prediction; parametric study

摘要：Ultra-high-performance concrete with coarse aggregates (UHPC-CA) has the advantages of high strength, strong shrinkage resistance and a lower production cost, presenting a broad application prospect in civil engineering construction. In view of the difficulty in establishing a mathematical model to accurately predict the mechanical properties of UHPC-CA, the back-propagation artificial neural network (BP-ANN) method is used to fully consider the various influential factors of the compressive strength (CS) and flexural strength (FS) of UHPC-CA in this paper. By taking the content of cement (C), silica fume (SF), slag, fly ash (FA), coarse aggregate (CA), steel fiber, the water-binder ratio (w/b), the sand rate (SR), the cement type (CT), and the curing method (CM) as input variables, and the CS and FS of UHPC-CA as output objectives, the BP-ANN model with three layers has been well-trained, validated and tested with 220 experimental data in the studies published in the literature. Four evaluating indicators including the determination coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the integral absolute error (IAE) were used to evaluate the prediction accuracy of the BP-ANN model. A parametric study for the various influential factors on the CS and FS of UHPC-CA was conducted using the BP-ANN model and the corresponding influential mechanisms were analyzed. Finally, the inclusion levels for the CA, steel fiber, and the dimensionless parameters of the W/B and sand rate were recommended to obtain the optimal strength of UHPC-CA.