Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

The increasing use of fibre reinforced polymer composite materials in a wide range of
applications increases the use of similar and dissimilar joints. Traditional joining methods …

In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine
learning architecture for data-driven modeling of the mechanical constitutive behavior of …

Recent developments in mechanical metamaterials exemplify a new paradigm shift called
mechanomaterials, in which mechanical forces and designed geometries are proactively …

Soil-rock mixtures (SRMs) are the main unfavorable geologic bodies in Southwest China.
This paper presents a novel mesoscale computational modeling study of SRMs with …

This work investigates the mechanical response of bio-inspired titanium mechanical
metamaterials with negative Poisson's ratio evolved from rotating squares unit. The systems …

The thermal expansion and stiffness characteristics are significant in determining the
responses of lightweight mechanical metamaterials in a thermal environment. This paper …

Determining the macroscopic mechanical properties of composites with complex
microstructures is a key issue in many of their applications. In this Letter, a machine learning …

Multifunctional 3D lattice structures with unconventional mechanical properties offer unique
material properties and broad applications in complex engineering environments. This …

This paper introduces a new methodology for generating three-dimensional (3D) negative
Poisson's ratio behavior with lattice representations of the rotating rigid mechanism as a …