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

被引:298
|
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
相关论文
共 50 条
  • [21] An ensemble approach to improve BPNN model precision for predicting compressive strength of high-performance concrete
    Tipu, Rupesh Kumar
    Panchal, V. R.
    Pandya, K. S.
    STRUCTURES, 2022, 45 : 500 - 508
  • [22] Determination of the Compressive Strength of Concrete Using Artificial Neural Network
    Palomino Ojeda, Jose Manuel
    Rosario Bocanegra, Stefano
    Quinones Huatangari, Lenin
    INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY INNOVATION, 2021, 11 (03) : 204 - 215
  • [23] Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
    Hameed M.M.
    AlOmar M.K.
    Baniya W.J.
    AlSaadi M.A.
    Asian Journal of Civil Engineering, 2021, 22 (6) : 1019 - 1031
  • [24] A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
    Suescum-Morales, David
    Salas-Morera, Lorenzo
    Jimenez, Jose Ramon
    Garcia-Hernandez, Laura
    APPLIED SCIENCES-BASEL, 2021, 11 (22):
  • [25] ESTIMATION OF CONCRETE COMPRESSIVE STRENGTH USING ARTIFICIAL NEURAL NETWORK
    Kostic, Srdan
    Vasovic, Dejan
    GRADEVNSKI MATERIJIALI I KONSTRUKCIJE-BUILDING MATERIALS AND STRUCTURES, 2015, 58 (01): : 3 - 16
  • [26] An explanatory machine learning model for forecasting compressive strength of high-performance concrete
    Yan, Guifeng
    Wu, Xu
    Zhang, Wei
    Bao, Yuping
    MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN, 2024, 7 (01) : 543 - 555
  • [27] Optimized machine learning models for predicting the tensile strength of high-performance concrete
    Kumar, Divesh Ranjan
    Kumar, Pramod
    Thangavel, Pradeep
    Wipulanusat, Warit
    Thongchom, Chanachai
    Samui, Pijush
    JOURNAL OF STRUCTURAL INTEGRITY AND MAINTENANCE, 2025, 10 (01)
  • [28] Utilization Of Metaheuristic-based Regression Analysis To Calculate The Modified High-performance Concrete's Compressive Strength
    Mu, Liming
    JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2025, 28 (08): : 1745 - 1758
  • [29] Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network
    Uysal, Mucteba
    Tanyildizi, Harun
    CONSTRUCTION AND BUILDING MATERIALS, 2012, 27 (01) : 404 - 414
  • [30] A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm
    Han, Qinghua
    Gui, Changqing
    Xu, Jie
    Lacidogna, Giuseppe
    CONSTRUCTION AND BUILDING MATERIALS, 2019, 226 : 734 - 742