This study describes the relationship between the physio-mechanical and chemical properties of sand-lime materials which have undergone hydrothermal treatment, and which were modified through the introduction of glass components (90% glass sand, GS). Process parameters such as temperature, pressure and saturation vapor pressure were found to have a significant impact on the series of chemical reactions as well as on the formation and transformation of solid hydrates. During the stirring process of sand-lime mass, the temperature of the reaction between lime and water in the presence of quartz sand (QS) was determined to be 83 °C. In the presence of glass sand, measured temperature was only 42 °C. Thermodynamic equilibrium-based modelling was applied to predict stable phase assemblages in the studied systems. It was found that compositional modification along with the application of the autoclaving process resulted in the formation of two crystalline phases: natrolite and gyrolite. Compressive strength and density were also assessed. The strength of fresh laboratory samples was found to be greater than their traditional analogues by 15 MPa. In addition to experimental characterization, sand-lime materials were also modeled using neural networks (backpropagation neural network, BPNN) which serve as a universal approximation method capable of modelling complex functions.

The objective of the article involves presenting two approaches to the structure reliability analysis. The primary research method was the First Order Reliability Method (FORM). The Hasofer–Lind reliability index β in conjunction with transformation method in the FORM was adopted as the measure of reliability. The first proposal was combining NUMPRESS software with the non-commercial KRATA program. In this case, the implicit form of the random variables function was created. Limit state function was symbolically given in the standard math notation as a function of the basic random and external variables. The second analysis proposed a hybrid approach enabling the introduction of explicit forms of limit state functions to the reliability program. To create the descriptions of this formula, the neural networks were used and our own original FEM module. The combination of conventional and neural computing can be seen as a hybrid system. The explicit functions were implemented into NUMPRESS software. The values of the reliability index for different descriptions of the mathematical model of the structure were determined. The proposed hybrid approach allowed us to obtain similar results to the results from the reference method.

The present study considers the problems of stability and reliability of spatial truss susceptible to stability loss from the condition of node snapping. In the reliability analysis of structure, uncertain parameters, such us load magnitudes, cross-sectional area, modulus of elasticity are represented by random variables. Random variables are not correlated. The criterion of structural failure is expressed by the condition of non-exceeding the admissible load multiplier. In the performed analyses explicit form of the random variables function were used. To formulate explicit limit state functions the neural networks is used. In the paper only the time independent component reliability analysis problems are considered. The NUMPRESS software, created at the IFTR PAS, was used in the reliability analysis. The Hasofer-Lind index in conjunction with transformation method in the FORM was used as a reliability measure. The primary research method is the FORM method. In order to verify the correctness of the calculation SORM and Monte Carlo methods are used. The values of reliability index for different descriptions of mathematical model of the structure were determined. The sensitivity of reliability index to the random variables is defined.

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There are situations in the thin-walled steel girders with box intersection, in which the internal wall of compression flange is elastically restrained in the webs of section and along its length occurs the change of normal stresses. Such a wall was modelled, as a bilateral elastically restrained internal plate variably loaded at the length. Explicit formulation of neural formula on buckling coefficient of internal plate at non-linear distribution of longitudinal stress was discussed in this paper. A few structures of neural networks (NN) were envisaged in order to obtain the best of the numerical effectiveness of the final formula. The results noted from the literature were used for the evaluation of neural prediction of coefficient.