Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

被引:1
作者
Tian, Haozheng [1 ]
Zhang, Yunsheng [1 ,2 ]
Qiao, Hongxia [1 ,2 ]
Feng, Qiong [1 ,2 ]
Wang, Penghui [3 ]
机构
[1] Lanzhou Univ Technol, Sch Civil Engn, Lanzhou 730050, Peoples R China
[2] Lanzhou Univ Technol, Western Minist Civil Engn Disaster Prevent & Mitig, Lanzhou 730050, Peoples R China
[3] Shenzhen Univ, Coll Civil & Transportat Engn, Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China
基金
中国国家自然科学基金;
关键词
Polymer-modified mortar; Swarm intelligence algorithm; Mechanical properties; BAT-BPNN; Manufactured sand; FLY-ASH; MIX DESIGN; CONCRETE; PERFORMANCE; POWDER; OPTIMIZATION; LIMESTONE; IMPACT;
D O I
10.1016/j.jobe.2024.111093
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6-14-2. Simultaneously, three swarm intelligence algorithms were integrated-the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)-to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 x 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT-BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model,
引用
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页数:21
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