Adaptive network-fuzzy inferencing to estimate concrete strength using mix design

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DOIResolve DOI: http://doi.org/10.4224/20378424
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TypeTechnical Report
Physical description46 p.
Subjectfuzzy logics; adaptive neuro-fuzzy inferencing; compressive strength; concrete mix proportioning
AbstractProportioning of concrete mixes is carried out in accordance with specified code information, specifications, and past experiences. Typically, concrete mix companies use different mix designs that are used to establish tried and tested datasets. Thus, a model can be developed based on existing datasets to estimate the concrete strength of a given mix proportioning and avoid costly tests and adjustments. Inherent uncertainties encountered in the model can be handled with fuzzy based methods, which are capable of incorporating information obtained from expert knowledge and datasets. In this paper, the use of adaptive neuro-fuzzy inferencing system is proposed to train a fuzzy model and estimate concrete strength. The efficiency of the proposed method is verified using actual concrete mix proportioning datasets reported in the literature, and the corresponding coefficient of determination r2r2 range from 0.970–0.999. Further, sensitivity analysis is carried out to highlight the impact of different mix constituents on the estimate concrete strength.
Publication date
PublisherNational Research Council Canada
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LanguageEnglish
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedNo
NRC numberNRCC 49681
NRC-IRC-18678
NPARC number20378424
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Record identifier9ce54a2e-6d81-4a9c-a34f-4da10671d0a0
Record created2012-07-24
Record modified2017-06-09
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