Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144100
Title: Probabilistic calibration for development length models of deformed reinforcing bar
Authors: Yu, Bo
Tang, Ruikai
Li, Bing
Keywords: Engineering::Civil engineering
Issue Date: 2018
Source: Yu, B., Tang, R., & Li, B. (2019). Probabilistic calibration for development length models of deformed reinforcing bar. Engineering Structures, 182, 279-289. doi:10.1016/j.engstruct.2018.12.047
Journal: Engineering Structures
Abstract: In order to provide a scientific basis for choosing the appropriate development length models, a probabilistic calibration method was developed to comprehensively evaluate the accuracy and applicability of seven typical deterministic development length models for deformed reinforcing bars (rebar) in normal and high-strength concrete. The influences of important factors (including compressive strength of concrete, concrete cover thickness, rebar diameter, tensile stress of reinforcement bar, residual tensile strength of cracked concrete, friction on bearing surface and effective bearing angle) on development length were investigated based on the partly cracked thick-walled cylinder model. Then a probabilistic development length model involving both aleatory and epistemic uncertainties was proposed based on the Bayesian theory and the Markov Chain Monte Carlo (MCMC) method. Meanwhile, key probabilistic characteristics of development length were presented and a probabilistic method was suggested to calibrate available deterministic development length models based on the confidence interval and the confidence level. Finally, the accuracy and applicability of available deterministic development length models under different conditions were calibrated comprehensively.
URI: https://hdl.handle.net/10356/144100
ISSN: 0141-0296
DOI: 10.1016/j.engstruct.2018.12.047
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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