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The paper deals with selected alkali-activated aluminosilicate (AAAS) composites based on ceramic precursors in terms of characterization by mechanical fracture parameters. Two composites made of brick dust as a precursor and an alkaline activator with a silicate modulus Ms = 1.0 were investigated. The composites differed in the fineness of grinding of the precursor – in the first set it was 0 to 1 mm, in the second set 0 to 0.3 mm. The filler was crushed brick. The test specimens had nominal dimensions of 40 × 40 × 160 mm and were provided with notches in the middle of the span up to 1/3 of the depth of the specimens after 28 days of hardening. Five to six specimens from each composite set were tested. The specimens were subjected to three-point bending tests, in which force vs. displacement (deflection in the middle of the span) diagrams (F–d diagrams) and force vs. crack mouth opening displacement (F–CMOD) diagrams were recorded. After correction of these diagrams, the values of static modulus of elasticity, effective fracture toughness, effective toughness and specific fracture energy were determined using the Effective Crack Model and the Work-of-Fracture method. After the fracture experiments, the values of informative compressive strength were determined on one of the fractured parts. At the same time, the values of static modulus of elasticity, tensile strength and specific fracture energy were identified using artificial neural networks and F–d diagrams measured and simulated in the ATENA FEM software. All evaluations included the determination of basic statistics of parameters.

Four sets of alkali-activated aluminosilicate (AAAS) composites based on ceramic precursors were studied in terms of their characterization by mechanical fracture parameters as a basis for considerations of durability. AAAS composites made of brick powder as a precursor and alkaline activator with various silicate moduli (Ms = 0.8, 1.0, 1.2, 1.4, and 1.6) were investigated. The sets of AAAS composites differed in terms of the used filler: quartz sand or brick rubble. Two different precursor particle size ranges of 0–1 mm and 0–0.3 mm were used for both types of filler. The test specimens had nominal dimensions of 40 × 40 × 160 mm and were provided with a notch at midspan after 28 days of hardening. The notches were cut up to 1/3 of the height of the specimens. The specimens were subjected to three-point bending fracture tests during which force vs. deflection (F–d) and force vs. crack mouth opening displacement (F–CMOD) diagrams were recorded. Tensile strength ft,ID and specific fracture energy GF,ID values were identified using the inverse method based on a neural network ensemble. The obtained F–CMOD diagrams were subsequently evaluated using the double-K fracture model supported by the ft,ID and GF,ID values. The double-K model allows the quantification of two different levels of crack propagation: initiation, which corresponds to the beginning of stable crack growth, and unstable crack propagation.

Článek představuje výsledky části výzkumu zaměřeného na vývoj komplexní experimentálně-výpočtové metodiky pro stanovení hodnot lomově-mechanických parametrů betonu nezávisle na velikosti a geometrii vzorku. K tomuto účelu byly provedeny laboratorní zkoušky štípání klínem na vzorcích třech různých velikostí se dvěma rozdílnými hloubkami zářezů. Na základě experimentálně získaných odezev zkušebních vzorků byla provedena identifikace lomově-mechanických parametrů všech vzorků pomocí inverzní analýzy. Získané parametry byly použity při numerické simulaci zkoušek a analyzovány z hlediska jejich závislosti na velikosti počátečního neporušeného ligamentu. The paper presents the results of a part of the research focused on the development of a complex experimental–computational methodology for determining the values of mechanical fracture parameters of concrete independent of the specimen size and geometry. For this purpose, laboratory wedge splitting tests were carried out on specimens of three different sizes with two different notch depths. Based on the experimentally obtained responses of the test specimens, the fracture mechanical parameters of all specimens were identified by inverse analysis. The obtained parameters were used in the numerical simulation of the tests and analysed in terms of their dependence on the size of the initial uncracked ligament.

A typical example of an alternative binder to commonly used Portland cement is alkali-activated binders that have high potential as a part of a toolkit for sustainable construction materials. One group of these materials is alkali-activated slag. There is a lack of information about its long-term properties. In addition, its mechanical properties are characterized most often in terms of compressive strength, however, it is not sensitive enough to sufficiently cover the changes in microstructure such as microcracking, and thus, it poses a potential risk for practical utilization. Consequently, the present study deals with the determination of long-term mechanical fracture and fatigue parameters of the fine-grained composites based on this interesting binder. The mechanical fracture parameters are primarily obtained through the direct evaluation of fracture test data via the effective crack model, the work-of-fracture method, the double-K fracture model, and complemented by parameter identification using the inverse analysis. The outcome of cyclic/fatigue fracture tests is represented by a W&ouml, hler curve. The results presented in this article represent the complex information about material behavior and valuable input parameters for material models used for numerical simulations of crack propagation in this quasi-brittle material.

The paper describes development of a soft-computing-based identification software FRCID-4PB for material parameter determination of ultra-high-performance fiber-reinforced concrete composite material. Such a determination is performed with the help of experimental data from four-point bending tests used in inverse analysis based on artificial neural networks and nonlinear computational modelling of tests. A new tensile softening model for studied composite material has been proposed and tested with the help of sensitivity analysis. The procedure also utilizes stratified statistical simulation method for the preparation of a training set for the artificial neural network. Trained network is then implemented into the software allowing routine and user-friendly identification of material parameters from the test results. The main aspects of the software implementation, its testing and application is described and discussed in the paper.

The paper deals with the experimental and numerical determination of mechanical fracture parameters of fine-grained composites based on the alkali-activated slag (AAS) at different ages of hardening. Two AAS composites, which differed only in the presence of shrinkage reducing admixture, were studied. The prismatic specimens with the nominal dimensions of 40 × 40 × 160 mm and initial central edge notch were subjected to fracture tests in a three-point bending configuration. The results of the fracture tests in the form load F versus deflection d diagrams were used as input data for the identification of parameters via the inverse analysis based on the artificial neural network whose aim is to transfer the fracture test response data to the desired material parameters. The modulus of elasticity, tensile strength, and fracture energy values were identified and subsequently compared with values obtained based on the direct fracture test evaluation using the effective crack model and work-of-fracture method.