Closure to “Stress‐Strain Modeling of Sands Using Artificial Neural Networks” by D. Penumadu

High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.

Download Full-text

Investigation of back-propagation artificial neural networks in modelling the stress-strain behaviour of sandstone rock

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) ◽

10.1109/icnn.1994.374770 ◽

2002 ◽

Cited By ~ 4

Author(s):

D. Millar ◽

E. Clarici

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Back Propagation ◽

Stress Strain ◽

Strain Behaviour ◽

Sandstone Rock ◽

Artificial Neural

Download Full-text

Prediction of Mechanical Properties of Local Concrete in Compression Using Artificial Neural Networks

Association of Arab Universities Journal of Engineering Sciences ◽

10.33261/jaaru.2019.27.1.012 ◽

2020 ◽

Vol 27 (1) ◽

pp. 105-121

Author(s):

Baylasan Mohamad ◽

Soleman Alamoudi ◽

Abd alrahman Issa

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Compressive Strength ◽

Artificial Neural Networks ◽

Actual Behavior ◽

Stress Strain ◽

Strain Relationship ◽

Artificial Neural ◽

Local Materials ◽

Ready Mix Concrete

Mechanical properties of concrete are highly dependent on the local materials used in its preparation. experiments on ready mix concrete in our region illustrate the actual behavior of concrete produced by local materials. Six standard cylinders (D=150mm, H=300mm) were casted of most ready mix concrete in central area in Syria (13 of them) covering a wide range of compressive strength . Tests were carried out using a testing machine which gives the applied force values and the corresponding displacement simultaneously until failure. The mean curves representing the (stress-strain) relationship of concrete in compression are drawn, from which the mechanical properties of each mixture were derived, such modulus of elasticity compressive strength , and the corresponding strain . Artificial neural networks were trained on experimental test results (using MATLAB). The laws of concrete behaviour were well assimilated by Artificial neural networks, which is possible to be used as an alternative method of available models of stress-strain relationship, by predicting the curve directly for various concrete mixtures prepared using local materials with different mixing ratios, or a complementary method through the adoption of an appropriate mathematical model and then predict its parameters ( ، ، ). ANNs proved their ability to predict mechanical properties of concrete better than linear regression equations, which promises a more accurate and comprehensive prediction.

Download Full-text