TY - JOUR
T1 - Predictive strength of ready-mixed concrete
T2 - Exemplified using data from the Norwegian market
AU - Engen, Morten
AU - Hendriks, Max A.N.
AU - Köhler, Jochen
AU - Øverli, Jan Arve
AU - Åldstedt, Erik
AU - Mørtsell, Ernst
AU - Sæter, Øyvind
AU - Vigre, Roar
PY - 2017/11/27
Y1 - 2017/11/27
N2 - A hierarchical model for the variability of material properties in ready-mixed concrete is formulated. The model distinguishes between variation on the batch, recipe, plant, producer, durability class, strength class, and regional standard level. By considering Bayesian inference and maximum likelihood estimators, the contributions from the different hierarchical levels to the variability can be estimated. The methodology is demonstrated by considering more than 14,000 compressive strength recordings from Norwegian ready-mixed concrete plants. The results suggest that the compressive cube strength of lab-cured specimens can be represented by a log-normally distributed variable with mean 1.28fck,cube and coefficient of variation Vc,cube=0.13. Prior parameters for Bayesian updating are given for a range of strength and durability classes. The application of the results is demonstrated in two examples. Since the durability class gives a required maximum water-binder ratio, and the strength of the concrete is governed by the water-binder ratio, the durability class introduces a strength potential if the concrete is subject to strict durability requirements and low-strength requirements. It is suggested that the designer should specify a strength class that utilizes this strength potential, and it is expected that a closer collaboration between the designer, contractor, and producer will result in improved concrete specifications.
AB - A hierarchical model for the variability of material properties in ready-mixed concrete is formulated. The model distinguishes between variation on the batch, recipe, plant, producer, durability class, strength class, and regional standard level. By considering Bayesian inference and maximum likelihood estimators, the contributions from the different hierarchical levels to the variability can be estimated. The methodology is demonstrated by considering more than 14,000 compressive strength recordings from Norwegian ready-mixed concrete plants. The results suggest that the compressive cube strength of lab-cured specimens can be represented by a log-normally distributed variable with mean 1.28fck,cube and coefficient of variation Vc,cube=0.13. Prior parameters for Bayesian updating are given for a range of strength and durability classes. The application of the results is demonstrated in two examples. Since the durability class gives a required maximum water-binder ratio, and the strength of the concrete is governed by the water-binder ratio, the durability class introduces a strength potential if the concrete is subject to strict durability requirements and low-strength requirements. It is suggested that the designer should specify a strength class that utilizes this strength potential, and it is expected that a closer collaboration between the designer, contractor, and producer will result in improved concrete specifications.
KW - Bayesian inference
KW - Code calibration
KW - Concrete compressive strength
KW - Hierarchical model for variability
KW - Informative prior distribution
KW - Maximum likelihood estimators
KW - Structural reliability
UR - http://www.scopus.com/inward/record.url?scp=85035211720&partnerID=8YFLogxK
U2 - 10.1002/suco.201700950
DO - 10.1002/suco.201700950
M3 - Article
VL - 19
SP - 806
EP - 819
JO - Structural Concrete
JF - Structural Concrete
SN - 1464-4177
IS - 3
ER -