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.

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 paper presents an example of reliability analysis of shell structures susceptible to stability loss from the condition of node snapping. In the reliability analysis of the structure, uncertain parameters of the task are represented by uncorrelated random variables. The approach used in the...

The indentation test is a popular method for the investigation of the mechanical properties of materials. The technique, which combines traditional indentation tests with mapping the shape of the imprint, provides more data describing the material parameters. In this paper, such methodology is employed for estimating the selected material parameters described by Ramberg–Osgood’s law, i.e., Young’s modulus, the yield point, and the material hardening exponent. Two combined identification methods were used: the P-A procedure, in which the material parameters are identified on the basis of the coordinates of the indentation curves, and the P-C procedure, which uses the coordinates describing the imprint profile. The inverse problem was solved by neural networks. The results of numerical indentation tests—pairs of coordinates describing the indentation curves and imprint profiles—were used as input data for the networks. In order to reduce the size of the input vector, a simple and effective method of approximating the branches of the curves was proposed. In the Results Section, we show the performance of the approximation as a data reduction mechanism on a synthetic dataset. The sparse model generated by the presented approach is also shown to efficiently reconstruct the data while minimizing error in the prediction of the mentioned material parameters. Our approach appeared to consistently provide better performance on the testing datasets with considerably easier computation than the principal component analysis compression results available in the literature.

The objective of the article is the quantitative assessment of the reliability index for a specific type of structure—trusses with node snap-through. The trends in contemporary geometric and structural design of architectural forms of rod domes are evolving towards increasing diameters and reducing rise. Therefore, it is justified to assess the safety of this type of structure. The Hasofer–Lind reliability index (β) was adopted as the reliability measure. In the reliability analysis, the FORM method was applied using the implicit form of the random variables function (combining external reliability software with the noncommercial finite-element method program) and using explicit forms of limit-state functions (neural networks were used and own original finite-element method module). In addition, the classical Monte Carlo method and the hybrid Monte Carlo method (combining with a neural network) were used. For dome loads in the range of 73–100%, the reliability index β can be estimated with reasonable accuracy (error) compared to standard methods. The obtained approximation functions allow for easy determination of the percentage of the maximum load that ensures safe operation. In addition, they allow us to indicate at what load level the reliability index reaches the standard level (at least β = 1.5 for the serviceability limit state).

The objective of the article involves presenting innovative approach to the assessment of structural reliability analysis. The primary research method was the First Order Reliability Method (FORM). The Hasofer-Lind reliability index in conjunction with transformation method in the FORM was adopted as the reliability measure. The implicit limit state functions were used in the analysis. The formulation of the random variables functions were created in the Matlab software by means of neural networks (NNs). The reliability analysis was conducted in Comrel module of Strurel computing environment. In the proposed approach, Hybrid FORM method (HF) used the concept in which NNs replaced the polynomial limit state functions obtained from FEM (Finite Elements Method) for chosen limit parameters of structure work. The module Comrel referenced Matlab files containing limit state functions. In the reliability analysis of structure, uncertain and uncorrelated parameters, such us base wind speed, characteristic snow load, elasticity modulus for steel and yield point steel are represented by random variables. The criterion of structural failure was expressed by four limit state functions-two related to the ultimate limit state and two related to the serviceability limit state. Using module Comrel values of the reliability index with the FORM method were determined. Additionally, the sensitivity of the reliability index to random variables and graph of partial safety factors were described. Replacing the FEM program by NNs significantly reduces the time needed to solve the task. Moreover, it enables the parallel formulation of many limit functions without extending the computation time.