TY - JOUR
T1 - Probabilistic shear strength models for reinforced concrete beams without shear reinforcement
AU - Song, Junho
AU - Kim, Kang Su
AU - Kang, Won Hee
AU - Jung, Sungmoon
PY - 2010/1/10
Y1 - 2010/1/10
N2 - In order to predict the shear strengths of reinforced concrete beams, many deterministic models have been developed based on rules of mechanics and on experimental test results. While the constant and variable angle truss models are known to provide reliable bases and to give reasonable predictions for the shear strengths of members with shear reinforcement, in the case of members without shear reinforcement, even advanced models with complicated procedures may show lack of accuracy or lead to fairly different predictions from other similar models. For this reason, many research efforts have been made for more accurate predictions, which resulted in important recent publications. This paper develops probabilistic shear strength models for reinforced concrete beams without shear reinforcement based on deterministic shear strength models, understanding of shear transfer mechanisms and influential parameters, and experimental test results reported in the literature. Using a Bayesian parameter estimation method, the biases of base deterministic models are identified as algebraic functions of input parameters and the errors of the developed models remaining after the bias-correction are quantified in a stochastic manner. The proposed probabilistic models predict the shear strengths with improved accuracy and help incorporate the model uncertainties into vulnerability estimations and risk-quantified designs.
AB - In order to predict the shear strengths of reinforced concrete beams, many deterministic models have been developed based on rules of mechanics and on experimental test results. While the constant and variable angle truss models are known to provide reliable bases and to give reasonable predictions for the shear strengths of members with shear reinforcement, in the case of members without shear reinforcement, even advanced models with complicated procedures may show lack of accuracy or lead to fairly different predictions from other similar models. For this reason, many research efforts have been made for more accurate predictions, which resulted in important recent publications. This paper develops probabilistic shear strength models for reinforced concrete beams without shear reinforcement based on deterministic shear strength models, understanding of shear transfer mechanisms and influential parameters, and experimental test results reported in the literature. Using a Bayesian parameter estimation method, the biases of base deterministic models are identified as algebraic functions of input parameters and the errors of the developed models remaining after the bias-correction are quantified in a stochastic manner. The proposed probabilistic models predict the shear strengths with improved accuracy and help incorporate the model uncertainties into vulnerability estimations and risk-quantified designs.
KW - Bayesian parameter estimation
KW - Epistemic uncertainty
KW - Error analysis
KW - Model errors
KW - Probabilistic models
KW - Reinforced concrete beams
KW - Shear strength
UR - http://www.scopus.com/inward/record.url?scp=73949093090&partnerID=8YFLogxK
U2 - 10.12989/sem.2010.34.1.015
DO - 10.12989/sem.2010.34.1.015
M3 - Article
AN - SCOPUS:73949093090
SN - 1225-4568
VL - 34
SP - 15
EP - 38
JO - Structural Engineering and Mechanics
JF - Structural Engineering and Mechanics
IS - 1
ER -