A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

被引:290
|
作者
Dac-Khuong Bui [1 ]
Tuan Nguyen [1 ]
Chou, Jui-Sheng [2 ]
Nguyen-Xuan, H. [3 ]
Tuan Duc Ngo [1 ]
机构
[1] Univ Melbourne, Dept Infrastruct Engn, Melbourne, Vic, Australia
[2] Natl Taiwan Univ Sci & Technol, Dept Civil & Construct Engn, 43,Sec 4,Keelung Rd, Taipei 106, Taiwan
[3] Chi Minh City Univ Technol Hutech, Ctr Interdisciplinary Res Technol, Ho Chi Minh City, Vietnam
关键词
High-performance concrete; Data mining; Evolutionary optimization; Artificial neural network; Modified firefly algorithm; FLY-ASH; FRACTURE-TOUGHNESS; MESHFREE METHOD; MODEL; BLAST; HPC; BEHAVIOR; SLABS;
D O I
10.1016/j.conbuildmat.2018.05.201
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The compressive and tensile strength of high-performance concrete (HPC) is a highly nonlinear function of its constituents. The significance of expert frameworks for predicting the compressive and tensile strength of HPC is greatly distinguished in material technology. This study aims to develop an expert system based on the artificial neural network (ANN) model in association with a modified firefly algorithm (MFA). The ANN model is constructed from experimental data while MFA is used to optimize a set of initial weights and biases of ANN to improve the accuracy of this artificial intelligence technique. The accuracy of the proposed expert system is validated by comparing obtained results with those from the literature. The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties. The MFA-ANN is also much faster at solving problems. Therefore, the proposed approach can provide an efficient and accurate tool to predict and design HPC. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:320 / 333
页数:14
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