Abstract Machine learning (ML), as a component of artificial intelligence, encourages
structural design exploration which leads to new technological advancements. By …

Abstract Machine learning opens up a new avenue for advancing the development of
phononic crystals and elastic metamaterials. Numerous learning models have been …

In this study, artificial intelligence algorithms are proposed for estimating the compressive
strength of hollow concrete block masonry prisms, including neural networks (ANN) …

The demand for cellular metamaterials exhibiting multiple desired properties has become
increasingly prominent due to the complexity of engineering applications. In this study, a …

This paper proposes a modified artificial intelligence (AI) approach based on the gradient-
enhanced physics-informed neural network (gPINN) with a novel structure for the …

Inversely designing and optimizing topological structures of phononic crystals that dominate
extraordinary wave characteristics has become a research hotspot. In this study, a joint …

In this paper, an analytical method is proposed for the analysis of the effects of the strain
gradients on the frequency band structures and band-gaps of the elastic waves propagating …

In this paper, the band structure analysis of the thermoelastic wave propagation in a
phononic crystal (PC), which it is reinforced by graphene platelets (GPLs) and carbon …

The energy harvesting capability of a graded metamaterial is maximised via reinforcement
learning (RL) under realistic excitations at the microscale. The metamaterial consists of a …

Elastic phononic crystals (PnCs) and metamaterials show promising prospects in seismic
wave protection. Hitherto, most existing solutions for this purpose use PnCs' bandgaps …