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Consideration of uncertainties in bridge structural engineering is a growing topic because it provides valuable information on the reliability of bridges in time while addressing life‐cycle aspects. Utilization of nonlinear finite element method (NLFEM) and Monte Carlo (MC) type simulation is essential for modelling of the concrete bridge random behaviour and its reliability assessments. This contribution describes keystones of a complex methodology for statistical, reliability and risk analyses of concrete structures including advanced nonlinear finite element analysis, uncertainty propagation and degradation modelling. It presents virtual simulation used on the way from assessment of experimental results to reliability analysis of a bridge structure, based on randomization of nonlinear fracture mechanics finite element analysis. Keystones of complex methodology are material parameters identification, nonlinear computational modelling, uncertainties propagation, surrogate modelling, degradation modelling, and safety formats. The paper describes methodology and for a routine complex assessment utilizing developed software tools and practical application of the approach for selected case studies of concrete bridges from Austria and the Czech Republic. [ABSTRACT FROM AUTHOR]

The paper presents a part of the research aimed at developing a comprehensive experimental-computational methodology for determining the values of mechanical fracture parameters of concrete independent of the specimen size and geometry. To this end, laboratory fracture tests in three-point bending and wedge-splitting test configurations were carried out using three different specimen sizes with two well-separated initial notch depths. The test records were used to identify selected mechanical fracture parameters using inverse analysis. Identifications were performed for individual specimens of each test configuration followed by statistical evaluation for individual sizes, notch depths, test configurations and for all specimens tested. The obtained parameters were used in the numerical simulation of the tests and the resulting load vs. displacement diagrams were compared with the experimental ones. The parameters were also analysed in terms of their dependence on the size of the initial uncracked ligament. As expected, a significant dependence of the fracture parameters was confirmed. [ABSTRACT FROM AUTHOR]

The aim of the paper is to present the results of the experiment focused on the development of the mechanical fracture characteristics of alkali-activated slag (AAS) based composites within the time interval from 3 days to 2 years of ageing. Two AAS composites, which differed only in the presence of shrinkage reducing admixture (SRA), were prepared for the purpose of experiments. The composites were prepared using ground granulated blast furnace slag activated by water-glass with silicate modulus of 2.0, standardized quartzite sand with the particle grain size distribution of 0-2 mm, and water. Commercially produced SRA was added into the second mixture in an amount of 2% by weight of slag. The test specimens were not protected from drying during the whole time interval and were stored in the laboratory at an ambient temperature of 21 ± 2 °C and relative humidity of 60 ± 10%. The prism specimens made of the abovementioned composites with nominal dimensions of 40 × 40 × 160 mm with the initial central edge notch were subjected to the fracture tests in a three-point bending configuration. The load F and displacement d (deflection in the middle of the span length) were continuously recorded during the fracture tests. The obtained F-d diagrams and specimen dimensions were used as input data for identification of parameters via the inverse analysis based on the artificial neural network, which aim is to transfer the fracture test response data to the desired material parameters. In this paper, the modulus of elasticity, tensile strength, and fracture energy values were identified and subsequently compared with values obtained based on the fracture test evaluation using the effective crack model and work-of-fracture method. [ABSTRACT FROM AUTHOR]

The use of stochastic non-linear computational mechanics in real-world applications faces a fundamental obstacle: the lack of knowledge regarding the stochastic properties of material parameters involved in the problem. The paper describes the results of an experimental program which was focused on the determination of fracture-mechanical parameters and their stochastic models of different concrete types after different hardening durations. The investigated types of concrete, C40/50 and C50/60, are used industrially for the production of pre-fabricated concrete elements (e.g. double T-shaped beam elements for roofing). The fracture-mechanical parameters were determined by wedge splitting tests on cubic specimens with notch. Moreover, numerical simulations were carried out for the identification of material parameters by using artificial neural networks. All results obtained from the individual tests will here be presented, compared and discussed. Finally, recommendations for stochastic models of selected parameters of the analysed concrete will be given. [ABSTRACT FROM AUTHOR]

The experimental results for quasi-brittle materials such as concrete and fibre-reinforced concrete exhibit high variability due to the heterogeneity of their aggregates, additives and general composition. An accurate assessment of the fracture-mechanical parameters of such materials (e.g. compressive strength fc and specific fracture energy Gf) turns out to be much more difficult and problematic than for other engineering materials. The practical design of quasi-brittle material-based structures requires virtual statistical approaches, simulations and probabilistic assessment procedures in order to be able to characterize the variability of these materials. A key parameter of non-linear fracture mechanics modelling is the specific fracture energy Gf and its variability, which has been a research subject for numerous authors although we will mention only [, ] at this point. The aim of this contribution is the characterization of stochastic fracture-mechanical properties of four specific, frequently used classes of concrete on the basis of a comprehensive experimental testing programme. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge regarding the values of fracture-mechanical parameters is critical for the virtual failure modeling of elements and structures made of concrete. A key parameter in nonlinear fracture mechanics modeling is the specific fracture energy of concrete, and its variability. Three-point bending tests on notched-beam specimens are fundamental experiments for the determination of fracture-mechanical parameters. In the present paper, two basic approaches are applied to determine fracture-mechanical parameter values from these tests: (i) the effective crack model/work-of-fracture method, and (ii) inverse analysis using artificial neural networks and stochastic simulations. First, the paper describes suitable methods for the determination of fracture-mechanical parameters. Second, the FraMePID-3PB software tool, which has been developed in order to automate the whole time consuming process of inverse analysis, is described. Finally, the verification of methodology and software is presented using two illustrative examples. [Copyright &y& Elsevier]