To determine the compressive strength of self-compacting recycled aggregate concrete using artificial neural network (ANN)

被引:19
作者
de-Prado-Gil, Jesus [1 ]
-Garcia, Rebeca Martinez [2 ]
Jagadesh, P. [3 ]
Juan-Valdes, Andreo [4 ]
Gonzalez-Alonso, Maria-Inmaculada [5 ]
Palencia, Covadonga [1 ]
机构
[1] Univ Leon, Dept Appl Phys, Campus Vegazana S-N, Leon 24071, Spain
[2] Univ Leon, Dept Min Technol Topog & Struct, Campus Vegazana S-N, Leon 24071, Spain
[3] Coimbatore Inst Technol, Dept Civil Engn, Coimbatore 638056, Tamil Nadu, India
[4] Univ Leon, Dept Agr Engn & Sci, Ave Portugal 41, Leon 24071, Spain
[5] Univ Leon, Dept Elect Engn & Syst & Automat, Campus Vegazana S-N, Leon 24071, Spain
关键词
Self -compacting concrete; Artificial neural network; Compressive strength; Recycled aggregate; MECHANICAL-PROPERTIES; HIGH-TEMPERATURE; FINE AGGREGATE; PREDICTION; COARSE; FIBER; GLASS; SCC; REPLACEMENT; ALGORITHM;
D O I
10.1016/j.asej.2023.102548
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Nowadays, special concrete-like self-compacting concrete (SCC) requires sustainability by introducing recycled aggregates as a partial replacement for natural aggregate. Technological development initiatives in the construction sector estimate the 28 days' concrete compressive strength before casting due to faster requirement; one method selected is an artificial neural network. From works of literature, 515 mixed design are collected and utilized for training, validation, and testing data to prepare models. Different applications of SCC require different strengths of concrete. Based on control mix compressive strength, the mix designs are grouped into three families as low, medium, and high strength, apart from a common family. The correlation between input and output variables for three different families is analyzed. ANOVA analyses are done for input parameters. Coefficient of relation (R2) is used for sensitive assessment and results for family I (R2 = 0.9299), family II (R2 = 0.824), family III (R2 = 0.8775), and family IV (R2 = 0.7991). Two further sensitivity analyses indicate that input parameters' influence varies for different families.
引用
收藏
页数:19
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共 118 条
  • [1] Predicting the ingredients of self compacting concrete using artificial neural network
    Abu Yaman, Mahmoud
    Abd Elaty, Metwally
    Taman, Mohamed
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2017, 56 (04) : 523 - 532
  • [2] Effect of nano and micro SiO2 on brittleness and fracture parameters of self-compacting lightweight concrete
    Afzali-Naniz, Oveys
    Mazloom, Moosa
    Karamloo, Mohammad
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2021, 299 (299)
  • [3] Aggarwal P, 2011, Int J Math Comput Sci, V5, P774, DOI [10.5281/ZENODO.1330501, DOI 10.5281/ZENODO.1330501]
  • [4] Experimental Research on Mechanical and Permeability Properties of Nylon Fiber Reinforced Recycled Aggregate Concrete with Mineral Admixture
    Ahmad, Jawad
    Zaid, Osama
    Perez, Carlos Lopez-Colina
    Martinez-Garcia, Rebeca
    Lopez-Gayarre, Fernando
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (02):
  • [5] A Study on the Mechanical Characteristics of Glass and Nylon Fiber Reinforced Peach Shell Lightweight Concrete
    Ahmad, Jawad
    Zaid, Osama
    Aslam, Fahid
    Shahzaib, Muhammad
    Ullah, Rahat
    Alabduljabbar, Hisham
    Khedher, Khaled Mohamed
    [J]. MATERIALS, 2021, 14 (16)
  • [6] New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation
    Ahmed, Abdulmalek
    Ali, Abdulwahab
    Elkatatny, Salaheldin
    Abdulraheem, Abdulazeez
    [J]. SUSTAINABILITY, 2019, 11 (22)
  • [7] Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete
    al-Swaidani, Aref M.
    Khwies, Waed T.
    [J]. ADVANCES IN CIVIL ENGINEERING, 2018, 2018
  • [8] Recycled glass as a partial replacement for fine aggregate in self compacting concrete
    Ali, Esraa Emam
    Al-Tersawy, Sherif H.
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2012, 35 : 785 - 791
  • [9] Hardened properties of self-compacting concrete - A statistical approach
    Almeida Filho, F. M.
    Barragan, B. E.
    Casas, J. R.
    El Debs, A. L. H. C.
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2010, 24 (09) : 1608 - 1615
  • [10] Impact of sulfate activation of rice husk ash on the performance of high strength steel fiber reinforced recycled aggregate concrete
    Althoey F.
    Zaid O.
    de-Prado-Gil J.
    Palencia C.
    Ali E.
    Hakeem I.
    Martínez-García R.
    [J]. Journal of Building Engineering, 2022, 54