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ANN model of stress-strain relationship for aluminium sponge in uniaxial compression

100%

Dudzik M. , Stręk A. M.

Journal of Theoretical and Applied Mechanics

2020

tom Vol. 58 nr 2

385--390

In this article, we present a proposition of a model of the compressive behaviour of open- -cell aluminium with relation to the material apparent density. The research was based on experimental data from uniaxial compression tests conducted for two sample lots. These results were analysed with the use of neural networks in a specially designed algorithm. The main criterion for choosing a satisfactory approximation was mean absolute relative error MARE<5%. As a result of the analysis, the sought relation was extracted and is presented as a proposition of a new ANN model of the compressive stress-strain relationship for aluminium sponge.

A neural network for monitoring and characterization of buildings

80%

Romańska-Zapała A. , Dudzik M. , Kuc S.

Inżynieria Bezpieczeństwa Obiektów Antropogenicznych

2023

tom Nr 4

134--143

This paper continues work from part 1 where a high precision estimator for energy efficiency and indoor environment based on artificial neural networks (ANN) was examined. Part 1 demonstrated that creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. Now, we extend our requirements for the model to be rapid and precise. At the end of this work we obtain a road map for the design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. This paper also shows that ANN system designed may have a high precision in characterizing the response of the building exposed to variable outdoor climatic conditions. The absolute value of the relative errors, MaxAR, is less than 2%. It proves that monitoring and ANN-based characterization approach can be used for different buildings, including those with the best environmental performance.

Verification of application of ANN modelling in study of compressive behaviour of aluminium sponges

70%

Stręk A. M. , Dudzik M. , Kwiecień A. , Wańczyk K. , Lipowska B.

Engineering Transactions

2019

tom Vol. 67, iss. 2

271--288

This article presents a preliminary neural network analysis of the compressive behaviour of aluminium open-cell sponges and answers the question of whether this phenomenon can be modelled using artificial intelligence. The research consisted of two phases: first – compression experiments, which in turn provided data for the second phase – the artificial neural network (ANN) analysis. A two-argument function was proposed and tested using the gathered experimental data with a two-layer feedforward network. The determination coefficient R 2 for linear correlation between targets and modelling outputs was chosen as the criterion for the assessment of the quality of modelling. The obtained values were R 2 > 0.96, which shows that neural networks hold the capacity to address the characterisation of the mechanical response of aluminium open-cell sponges in compression. Additionally, the mean absolute relative error (MARE) and the mean square error (MSE) were also determined.