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Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic

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Title: Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic
Authors: Garzón-Roca, Julio1, Marco, Creu Obrer1, Adam, Jose M. joadmar@cst.upv.es
Source: Engineering Structures. Mar2013, Vol. 48, p21-27. 7p.
Subjects: Compressive strength, Masonry, Bricks, Cement, Mortar, Estimation theory, Artificial neural networks, Fuzzy logic, Mathematical models
Abstract: Abstract: The use of mathematical tools such as Artificial Neural Networks and Fuzzy Logic has been shown to be useful for solving complex engineering problems, without the need to reproduce the phenomenon under study, when the only information available consists of the parameters of the problem and the desired results. Based on a collection of 96 laboratory tests, this paper uses Artificial Neural Networks and Fuzzy Logic to determine the compressive strength of a masonry structure composed of clay bricks and cement mortar, by using only two parameters: the compressive strength of the mortar and that of the bricks. These mathematical techniques are an alternative to the complex analytical formulas dependent on a large number of parameters and to empirical formulas, which, even though simple, often give unrealistic values. The results obtained are compared to the calculation methods proposed by other authors and other standards and demonstrate the suitability of using Neural Networks and Fuzzy Logic to predict the compressive strength of masonry. [Copyright &y& Elsevier]
Copyright of Engineering Structures is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 85419865
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic
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  Data: <searchLink fieldCode="AR" term="%22Garzón-Roca%2C+Julio%22">Garzón-Roca, Julio</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Marco%2C+Creu+Obrer%22">Marco, Creu Obrer</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Adam%2C+Jose+M%2E%22">Adam, Jose M.</searchLink><i> joadmar@cst.upv.es</i>
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Structures%22">Engineering Structures</searchLink>. Mar2013, Vol. 48, p21-27. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Masonry%22">Masonry</searchLink><br /><searchLink fieldCode="DE" term="%22Bricks%22">Bricks</searchLink><br /><searchLink fieldCode="DE" term="%22Cement%22">Cement</searchLink><br /><searchLink fieldCode="DE" term="%22Mortar%22">Mortar</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink>
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  Data: Abstract: The use of mathematical tools such as Artificial Neural Networks and Fuzzy Logic has been shown to be useful for solving complex engineering problems, without the need to reproduce the phenomenon under study, when the only information available consists of the parameters of the problem and the desired results. Based on a collection of 96 laboratory tests, this paper uses Artificial Neural Networks and Fuzzy Logic to determine the compressive strength of a masonry structure composed of clay bricks and cement mortar, by using only two parameters: the compressive strength of the mortar and that of the bricks. These mathematical techniques are an alternative to the complex analytical formulas dependent on a large number of parameters and to empirical formulas, which, even though simple, often give unrealistic values. The results obtained are compared to the calculation methods proposed by other authors and other standards and demonstrate the suitability of using Neural Networks and Fuzzy Logic to predict the compressive strength of masonry. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Structures is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.engstruct.2012.09.029
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
        StartPage: 21
    Subjects:
      – SubjectFull: Compressive strength
        Type: general
      – SubjectFull: Masonry
        Type: general
      – SubjectFull: Bricks
        Type: general
      – SubjectFull: Cement
        Type: general
      – SubjectFull: Mortar
        Type: general
      – SubjectFull: Estimation theory
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Fuzzy logic
        Type: general
      – SubjectFull: Mathematical models
        Type: general
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      – TitleFull: Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic
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            NameFull: Garzón-Roca, Julio
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            NameFull: Marco, Creu Obrer
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            NameFull: Adam, Jose M.
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              M: 03
              Text: Mar2013
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              Y: 2013
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              Value: 48
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