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Derivation of dynamic material properties of steel-fibre-reinforced concrete using kernel regression

Wang, Y, Hao, H and Hao, Y (2015) Derivation of dynamic material properties of steel-fibre-reinforced concrete using kernel regression In: ICPS3 2015: Design and Analysis of Protective Structures: the 3rd International Conference on Protective Structures, 2015-02-03 - 2015-02-06, The University of Newcastle, Callaghan, N.S.W., Australia.

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The reliable and efficient design of steel-fibre-reinforced concrete (SFRC) structures requires clear knowledge of material properties. Since the locations and orientations of aggregates and fibres in concrete are intrinsically random, testing results from different specimens vary, and it needs hundreds or even thousands of specimens and tests to derive the unbiased statistical distributions of material properties by using traditional statistical techniques. Therefore, few statistical studies on the SFRC material properties can be found in literature. In this study, high-rate impact test results on SFRC using split Hopkinson pressure bar are further analysed. The influences of different strain rates and various volume fractions of fibres on compressive strength of SFRC specimens under dynamic loadings will be quantified, by using kernel regression, a kernel-based nonparametric statistical method. Several kernel estimators and functions will be compared. This technique allows one to derive an unbiased statistical estimation from limited testing data. Therefore it is especially useful when the testing data is limited.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Civil Engineering
Divisions : Surrey research (other units)
Authors :
Hao, H
Hao, Y
Date : 3 February 2015
Copyright Disclaimer : ©2015, University of Newcastle
Contributors :
publisherCentre for Infrastructure Performance and Reliability, The Unive,
Uncontrolled Keywords : Steel-fibre-reinforced concrete, Kernel regression, Impact tests
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:55
Last Modified : 23 Jan 2020 18:59

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