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    Statistics of Model Factors and Consideration in Reliability-Based Design of Axially Loaded Helical Piles

    Source: Journal of Geotechnical and Geoenvironmental Engineering:;2018:;Volume ( 144 ):;issue: 008
    Author:
    Tang Chong;Phoon Kok-Kwang
    DOI: 10.1061/(ASCE)GT.1943-5606.0001894
    Publisher: American Society of Civil Engineers
    Abstract: Geotechnical design codes have been migrating toward reliability-based design (RBD) concepts. ISO 2394 identified the characterization of model uncertainty as one of the critical elements in the geotechnical RBD process. This paper collects a large number of field axial load tests on helical piles for model uncertainty assessment as required in developing RBD. At the ultimate limit state (ULS), the model uncertainty is represented by a model factor, which is defined as a ratio of measured resistance over calculated resistance. The measured resistance is interpreted from load test data using the methods recommended in the helical pile industry, while the existing empirical or semiempirical methods are applied for resistance calculations. A hyperbolic model with two parameters is adopted to fit the measured load-settlement data. The uncertainties within the load-settlement curves are captured by a bivariate random vector containing the hyperbolic parameters as its components. Statistical properties of the model factors such as means, coefficients of variation, and probability distributions are determined from the database. Moreover, several copulas are evaluated to establish the correlation structure within the hyperbolic parameters. Finally, the ULS model statistics are incorporated into the calibration of the resistance factor in the load and resistance factor design (LRFD) of axially loaded helical piles.
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      Statistics of Model Factors and Consideration in Reliability-Based Design of Axially Loaded Helical Piles

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4248967
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    contributor authorTang Chong;Phoon Kok-Kwang
    date accessioned2019-02-26T07:43:44Z
    date available2019-02-26T07:43:44Z
    date issued2018
    identifier other%28ASCE%29GT.1943-5606.0001894.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4248967
    description abstractGeotechnical design codes have been migrating toward reliability-based design (RBD) concepts. ISO 2394 identified the characterization of model uncertainty as one of the critical elements in the geotechnical RBD process. This paper collects a large number of field axial load tests on helical piles for model uncertainty assessment as required in developing RBD. At the ultimate limit state (ULS), the model uncertainty is represented by a model factor, which is defined as a ratio of measured resistance over calculated resistance. The measured resistance is interpreted from load test data using the methods recommended in the helical pile industry, while the existing empirical or semiempirical methods are applied for resistance calculations. A hyperbolic model with two parameters is adopted to fit the measured load-settlement data. The uncertainties within the load-settlement curves are captured by a bivariate random vector containing the hyperbolic parameters as its components. Statistical properties of the model factors such as means, coefficients of variation, and probability distributions are determined from the database. Moreover, several copulas are evaluated to establish the correlation structure within the hyperbolic parameters. Finally, the ULS model statistics are incorporated into the calibration of the resistance factor in the load and resistance factor design (LRFD) of axially loaded helical piles.
    publisherAmerican Society of Civil Engineers
    titleStatistics of Model Factors and Consideration in Reliability-Based Design of Axially Loaded Helical Piles
    typeJournal Paper
    journal volume144
    journal issue8
    journal titleJournal of Geotechnical and Geoenvironmental Engineering
    identifier doi10.1061/(ASCE)GT.1943-5606.0001894
    page4018050
    treeJournal of Geotechnical and Geoenvironmental Engineering:;2018:;Volume ( 144 ):;issue: 008
    contenttypeFulltext
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