메뉴 건너뛰기




Volumn 6, Issue 6, 2011, Pages 1325-1331

Using feed-forward back propagation (FFBP) neural networks for compressive strength prediction of lightweight concrete made with different percentage of scoria instead of sand

Author keywords

Artificial neural networks (ANNs); Compressive strength (CS); Feed forward back propagation (FFBP); Scoria

Indexed keywords


EID: 79957957835     PISSN: 19921950     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (7)

References (15)
  • 2
    • 0028467384 scopus 로고
    • Common Misconceptions about Neural Networks as Approximators
    • Carpenter WC, Barthelemy JF (1994). Common Misconceptions about Neural Networks as Approximators. ASCE J. Comput. Civil Eng., 8: 345-358.
    • (1994) ASCE J. Comput. Civil Eng. , vol.8 , pp. 345-358
    • Carpenter, W.C.1    Barthelemy, J.F.2
  • 3
    • 0034085794 scopus 로고    scopus 로고
    • Recent research advances in cold-formed steel structures
    • Davies JM (2000). Recent research advances in cold-formed steel structures. J. Construct Steel Res., p. 55.
    • (2000) J. Construct Steel Res. , pp. 55
    • Davies, J.M.1
  • 4
    • 40249113254 scopus 로고    scopus 로고
    • Predicting the compressive strength of steel fiber added lightweight concrete using neural network
    • Fatih A, Özgür K, Kamil A (2008). Predicting the compressive strength of steel fiber added lightweight concrete using neural network. Comp. Mater. Sci., 42: 2.
    • (2008) Comp. Mater. Sci. , vol.42 , pp. 2
    • Fatih, A.1    Özgür, K.2    Kamil, A.3
  • 7
    • 35348840368 scopus 로고    scopus 로고
    • Prediction of properties of waste AAC aggregate concrete using artificial neural network
    • İlker BT, Mustafa S (2007). Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput. Mater. Sci., 41: 117-125
    • (2007) Comput. Mater. Sci. , vol.41 , pp. 117-125
    • Ilker, B.T.1    Mustafa, S.2
  • 10
    • 33644678621 scopus 로고    scopus 로고
    • Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks
    • Civil Engineering Group
    • Manish AK, Rajiv G (2006). Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Civil Engineering Group. Birla Inst. Technol. Sci. Autom. Construct., 15: 374-379
    • (2006) Birla Inst. Technol. Sci. Autom. Construct. , vol.15 , pp. 374-379
    • Manish, A.K.1    Rajiv, G.2
  • 11
    • 0030244798 scopus 로고    scopus 로고
    • Drying of Lightweight Concrete Produced From Crushed Expended Clay Aggregates
    • Merikallio T, Mannonen R (1996). Drying of Lightweight Concrete Produced From Crushed Expended Clay Aggregates. Com Concer Res., 26: 1423-1433.
    • (1996) Com Concer Res. , vol.26 , pp. 1423-1433
    • Merikallio, T.1    Mannonen, R.2
  • 14
    • 0000646059 scopus 로고
    • Learning Internal Representations by Error Propagation
    • In, Rumelhart DE, and McClelland J L. The MIT Press
    • Rumelhart DE, Hinton GE, Williams RJ (1986). Learning Internal Representations by Error Propagation. In Parallel Distributed Processing Foundations. Rumelhart DE, and McClelland J L. The MIT Press. Vol. 1.
    • (1986) Parallel Distributed Processing Foundations , vol.1
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 15
    • 33748919473 scopus 로고    scopus 로고
    • Investigation of properties of low strength lightweight concrete for thermal insulation
    • Unal O, Uygunog T, Yildiz A (2007). Investigation of properties of low strength lightweight concrete for thermal insulation, Build. Environ., 42: 584-590.
    • (2007) Build. Environ. , vol.42 , pp. 584-590
    • Unal, O.1    Uygunog, T.2    Yildiz, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.