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    Probabilistic Analysis and Time Variation Model of Concrete Compressive Strength in Existing Buildings

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2023:;Volume ( 009 ):;issue: 003::page 04023026-1
    Author:
    Qingxia Yue
    ,
    Weina Wang
    ,
    Xin Zhang
    DOI: 10.1061/AJRUA6.RUENG-1071
    Publisher: ASCE
    Abstract: Probabilistic analysis and time variation model of concrete compressive strength in existing buildings were conducted based on relevant site testing data. The data were collected from rebound hammer (RH) method and core test method. There were 73,840 of RH test data and 4,149 of core test data. First, the data were normalized through dividing by the cube strength, to eliminate the concrete grade effect. The probability distribution analysis showed that the normal distribution is more suitable than the log-normal distribution. Moreover, it was found that the mean value of the core test data was higher than that of the RH data. Further, regression analysis on the data was carried out, and two quadratic functions were adopted to capture the time variation models of the compressive strength based on the RH data, and the core test data, respectively. Furthermore, some more data were obtained by the Genetic algorithm-back propagation (GA-BP) neural network method, and the time variation models were accordingly updated including the new data. Finally, the relationship between the RH model and core test model was discussed, and two approximately linear curves existed from 0 to 30 years and 31 to 60 years. Hopefully, this probabilistic analysis and the time variation model can be helpful in the assessment of the bearing capacity and evaluation of structure reliability in concrete structures.
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      Probabilistic Analysis and Time Variation Model of Concrete Compressive Strength in Existing Buildings

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorQingxia Yue
    contributor authorWeina Wang
    contributor authorXin Zhang
    date accessioned2023-11-27T23:09:49Z
    date available2023-11-27T23:09:49Z
    date issued7/12/2023 12:00:00 AM
    date issued2023-07-12
    identifier otherAJRUA6.RUENG-1071.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4293346
    description abstractProbabilistic analysis and time variation model of concrete compressive strength in existing buildings were conducted based on relevant site testing data. The data were collected from rebound hammer (RH) method and core test method. There were 73,840 of RH test data and 4,149 of core test data. First, the data were normalized through dividing by the cube strength, to eliminate the concrete grade effect. The probability distribution analysis showed that the normal distribution is more suitable than the log-normal distribution. Moreover, it was found that the mean value of the core test data was higher than that of the RH data. Further, regression analysis on the data was carried out, and two quadratic functions were adopted to capture the time variation models of the compressive strength based on the RH data, and the core test data, respectively. Furthermore, some more data were obtained by the Genetic algorithm-back propagation (GA-BP) neural network method, and the time variation models were accordingly updated including the new data. Finally, the relationship between the RH model and core test model was discussed, and two approximately linear curves existed from 0 to 30 years and 31 to 60 years. Hopefully, this probabilistic analysis and the time variation model can be helpful in the assessment of the bearing capacity and evaluation of structure reliability in concrete structures.
    publisherASCE
    titleProbabilistic Analysis and Time Variation Model of Concrete Compressive Strength in Existing Buildings
    typeJournal Article
    journal volume9
    journal issue3
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1071
    journal fristpage04023026-1
    journal lastpage04023026-9
    page9
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2023:;Volume ( 009 ):;issue: 003
    contenttypeFulltext
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