High-fidelity full-field micro-mechanical modeling of the non-linear path-dependent materials
demands a substantial computational effort. Recent trends in the field incorporates data …

Woven composites exhibit complex meso-scale behavior depending on meso-and micro-
structural parameters. Accurately modeling their mechanical response is challenging and …

This study presents an innovative application of the Taguchi design of experiment method to
optimize the structure of an Artificial Neural Network (ANN) model for the prediction of elastic …

Finding quantitative descriptors representing the microstructural features of a given material
is an ongoing research area in the paradigm of Materials-by-Design. Historically, the …

Grasping the track stiffness status is significant to railway maintenance. However, the
research on the data collection and detection method of ballastless track layered stiffness is …

In this study, we propose an efficient homogenization model based on the fast Fourier
transform (FFT) method and artificial neural network (ANN) models to predict the multiscale …

For unidirectional laminates, four properties are required for mechanical characterization
regarding the laminae elastic response: the longitudinal elastic modulus, the transverse …

Fatigue and fracture prediction in composite materials using cohesive zone models depends
on accurately characterizing the core and facesheet interface in advanced composite …

The purpose of this study is to establish simple discrete distribution models capable of
expressing the two and three-dimensional random orientation states of fillers in composite …

A machine learning (ML) model can provide a precise prediction very quickly, if it is well
trained with a massive amount of reliable training data. A finite element method (FEM) is …