The use of fibre-reinforced plastics (FRPs) in sandwich structures increased for various industrial applications thanks to their strength-to-weight ratio which provides designers with advanced options for modern structures. FRP Sandwich Structures (FRPSS) are often used in aerospace, biomedical, defence, and marine products, where their high structural performance is required to sustain complex in-service loads and withstand varying environmental conditions. Progressive degradation of FRPSS under such circumstances has been a subject of interest for researchers owing to safety requirements for products with FRP. This paper reviews the state-of-the-art of the mechanical behaviour of FRPSS subjected to various loading regimes. It highlights the variation in structural performance, viscoelastic properties, damage resistance, and sequence of environmental degradation of FRPSS. Numerical methods and damage algorithms used to predict failures are also presented to provide sufficient knowledge for the design of FRPSS. This review contributes to further research on characterizing the properties of FRPSS under quasi-static and dynamic loading conditions.

Thermal bonding is the fastest and the cheapest technique for manufacturing nonwovens. Understanding mechanical behaviour of these materials, especially related to damage, can aid in design of products containing nonwoven parts. A finite element (FE) model incorporating mechanical properties related to damage such as maximum stress and strain at failure of fabric’s fibres would be a powerful design and optimisation tool. In this study, polypropylene-based thermally bonded nonwovens manufactured at optimal processing conditions were used as a model system. A damage behaviour of the nonwoven fabric is governed by its single-fibre properties, which are obtained by conducting tensile tests over a wide range of strain rates. The fibres for the tests were extracted from the nonwoven fabric in a way that a single bond point was attached at both ends of each fibre. Additionally, similar tests were performed on unprocessed fibres, which form the nonwoven. Those experiments not only provided insight into damage mechanisms of fibres in thermally bonded nonwovens but also demonstrated a significant drop in magnitudes of failure stress and respective strain in fibres due to the bonding process. A novel technique was introduced in this study to develop damage criteria based on the deformation and fracture behaviour of a single fibre in a thermally bonded nonwoven fabric. The damage behaviour of a fibrous network within the thermally bonded fabric was simulated with a FE model consisting of a number of fibres attached to two neighbouring bond points. Additionally, various arrangements of fibres’ orientation and material properties were implemented in the model to analyse the respective effects.

The use of fibre-reinforced plastics (FRPs) in various industrial applications continues to increase thanks to their good strength-to-weight ratio and impact resistance, as well as the high strength that provides engineers with advanced options for the design of modern structures subjected to a variety of out-of-plane impacts. An assessment of the damage morphology under such conditions using non-destructive techniques could provide useful data for material design and optimisation. This study investigated the damage mechanism and energy-absorption characteristics of E-glass laminates and sandwich structures with GFRP face sheets with PVC cores under quasi-static indentation with conical, square, and hemispherical indenters. An acoustic emission (AE) technique, coupled with a k-means++ pattern-recognition algorithm, was employed to identify the dominant microscopic and macroscopic damage mechanisms. Additionally, a post-mortem damage assessment was performed with X-ray micro computed tomography and scanning electron microscopy to validate the identified clusters. It was found that the specific energy absorption after impact with the square and hemispherical indenters of the GFRP sandwich and the plain laminate differed significantly, by 19.29% and 43.33%, respectively, while a minimal difference of 3.5% was recorded for the conical indenter. Additionally, the results obtained with the clustering technique applied to the acoustic emission signals detected the main damaged modes, such as matrix cracking, fibre/matrix debonding, delamination, the debonding of face sheets/core, and core failure. The results therefore could provide a methodology for the optimisation and prediction of damage for the health monitoring of composites.

A complex time-dependent deformation and damage behaviour in polymer-based nonwovens are analysed under conditions of multi-stage uniaxial loading. Elastic–plastic and viscous properties of a polypropylene-based fabric are obtained by series of tensile, creep and relaxation tests performed on single fibres extracted from the studied fabric. These properties are implemented in a finite-element (FE) model of nonwoven with direct introduction of fibres according to their actual orientation distribution in order to simulate the rate-dependent deformation up to the onset of damage in thermally bonded nonwovens. The predictions of FE simulations are compared with the experimental data of multi-stage deformation tensile tests and a good agreement is obtained including the mechanisms of deformation. Due to direct modelling of fibres based on their actual orientation distribution and implementation of viscous properties, the model could be extended to other types of polymer-based random fibrous networks.

Sandwich structures made with fibre-reinforced plastics are commonly used in maritime vessels thanks to their high strength-to-weight ratios, corrosion resistance, and buoyancy. Understanding their mechanical performance after moisture uptake and the implications of moisture uptake for their structural integrity and safety within out-of-plane loading regimes is vital for material optimisation. The use of modern methods such as acoustic emission (AE) and machine learning (ML) could provide effective techniques for the assessment of mechanical behaviour and structural health monitoring. In this study, the AE features obtained from quasi-static indentation tests on sandwich structures made from E-glass fibre face sheets with polyvinyl chloride foam cores were employed. Time- and frequency-domain features were then used to capture the relevant information and patterns within the AE data. A k-means++ algorithm was utilized for clustering analysis, providing insights into the principal damage modes of the studied structures. Three ensemble learning algorithms were employed to develop a damage-prediction model for samples exposed and unexposed to seawater and were loaded with indenters of different geometries. The developed models effectively identified all damage modes for the various indenter geometries under different loading conditions with accuracy scores between 86.4 and 95.9%. This illustrates the significant potential of ML for the prediction of damage evolution in composite structures for marine applications.