This paper provides an overview, insights, results and a classification related to development and analyses of case studies within the scientific networking project COST Action TU1402 on the value of Structural Health Monitoring (SHM) information. With an outline of the framework and approaches, a procedure on how to quantify the value of SHM information on the basis of the Bayesian decision theory is described. Various case studies with different types of structures (e.g. stadium roof, timber structures, offshore wind parks), several types of SHM systems (e.g. structural measurements, damage detection) and with diverse decision scenarios (e.g. structural system properties, SHM system properties, different SHM systems for structural service life extension) are outlined. Approaches for value of SHM information analyses visualisation and classification, both for the purposes of development of decision scenarios and for the comparison of case study results are introduced and described. Whereas the development of value of SHM information analyses is focussed on the establishment of a decision scenario, the comparison of analyses should also include the identification of optimal SHM information acquirement strategies, actions and decision rules beside an indication on which methodological and technological readiness level the analyses has been performed. The paper concludes with open fields identified when applying the visualisation and classification tools.

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 1.28f_ck,cube and coefficient of variation V_c,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.

In order to make non-linear finite element analyses applicable during assessment of the global resistance of large concrete structures, there is need for a solution strategy with a low modelling uncertainty. A solution strategy comprises choices regarding force equilibrium, kinematic compatibility and constitutive relations. Relatively large solid finite elements and a fully triaxial material model for concrete were used in the present work. Bayesian inference was applied to results from 38 benchmark analyses. The results indicated that the modelling uncertainty could be represented as a log-normally distributed random variable with mean 1.10 and standard deviation of 0.12. A new method for characterizing the failure mode was developed. The results indicated that the physical uncertainties influenced the estimated parameters of the modelling uncertainty, and that this should be considered when other uncertainties are included in a reliability assessment.