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    Application of Probabilistic Neural Networks for Prediction of Concrete Strength

    Source: Journal of Materials in Civil Engineering:;2005:;Volume ( 017 ):;issue: 003
    Author:
    Doo Kie Kim
    ,
    Jong Jae Lee
    ,
    Jong Han Lee
    ,
    Seong Kyu Chang
    DOI: 10.1061/(ASCE)0899-1561(2005)17:3(353)
    Publisher: American Society of Civil Engineers
    Abstract: The compressive strength of concrete is a commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time consuming. More importantly, it is too late to make improvements even if the test result does not satisfy the required strength, since the test is usually performed on the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is very important. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is performed using the probabilistic neural network which is an effective tool for the pattern classification problem and provides a probabilistic viewpoint as well as a deterministic classification result. Application of probabilistic neural networks in the compressive strength estimation of concrete is performed using the mix proportion data and test results of two concrete companies. It has been found that the present methods are very efficient and reasonable in predicting the compressive strength of concrete probabilistically.
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      Application of Probabilistic Neural Networks for Prediction of Concrete Strength

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    http://yetl.yabesh.ir/yetl1/handle/yetl/46038
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    contributor authorDoo Kie Kim
    contributor authorJong Jae Lee
    contributor authorJong Han Lee
    contributor authorSeong Kyu Chang
    date accessioned2017-05-08T21:17:50Z
    date available2017-05-08T21:17:50Z
    date copyrightJune 2005
    date issued2005
    identifier other%28asce%290899-1561%282005%2917%3A3%28353%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/46038
    description abstractThe compressive strength of concrete is a commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time consuming. More importantly, it is too late to make improvements even if the test result does not satisfy the required strength, since the test is usually performed on the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is very important. This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions. The estimation of the strength is performed using the probabilistic neural network which is an effective tool for the pattern classification problem and provides a probabilistic viewpoint as well as a deterministic classification result. Application of probabilistic neural networks in the compressive strength estimation of concrete is performed using the mix proportion data and test results of two concrete companies. It has been found that the present methods are very efficient and reasonable in predicting the compressive strength of concrete probabilistically.
    publisherAmerican Society of Civil Engineers
    titleApplication of Probabilistic Neural Networks for Prediction of Concrete Strength
    typeJournal Paper
    journal volume17
    journal issue3
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/(ASCE)0899-1561(2005)17:3(353)
    treeJournal of Materials in Civil Engineering:;2005:;Volume ( 017 ):;issue: 003
    contenttypeFulltext
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