Auxetic behavior is a promising new area for use in defense applications. In comparison to a conventional material, an auxetic material has superior properties because of having a negative Poisson’s ratio; it gets broadened when stretched or becomes smaller when compressed. Furthermore, auxetic materials have enhanced properties such as shear resistance, indentation resistance, fracture toughness, energy absorption, and so on. These improved properties make auxetic materials very appealing and have the potential to revolutionize their applications in aerospace, sports, automotive, construction, biomedical engineering, smart sensors, packaging, cushioning, air filtration, shock absorption and sound insulation, and defense personal protective equipment. This article examines the most recent scientific advances in auxetic materials and structures, such as auxetic textiles (fibers, yarns, and fabrics), auxetic textile-reinforced composites, and auxetic foams, as well as their exceptional auxetic behavior and various approaches to achieving them. Although many potential applications have been proposed, actual applications of auxetic materials in defense are still limited. This is an in-depth review article, and its main goal is to serve as a foundation for future studies concerning the topic.

Poly(vinyl alcohol) (PVA) in multifilament and braided yarns (BY) forms presents great potential for the design of numerous applications. However, such solutions fail to accomplish their requirements if the chemical and thermomechanical behaviour is not sufficiently known. Hence, a comprehensive characterisation of PVA multifilament and three BY architectures (6, 8, and 10 yarns) was performed involving the application of several techniques to evaluate the morphological, chemical, thermal, and mechanical features of those structures. Scanning electron microscopy (SEM) was used to reveal structural and morphological information. Differential thermal analysis (DTA) pointed out the glass transition temperature of PVA at 76 °C and the corresponding crystalline melting point at 210 °C. PVA BY exhibited higher tensile strength under monotonic quasi-static loading in comparison to their multifilament forms. Creep tests demonstrated that 6BY structures present the most deformable behaviour, while 8BY structures are the least deformable. Relaxation tests showed that 8BY architecture presents a more expressive variation of tensile stress, while 10BY offered the least. Dynamic mechanical analysis (DMA) revealed storage and loss moduli curves with similar transition peaks for the tested structures, except for the 10BY. Storage modulus is always four to six times higher than the loss modulus.

Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.

In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technology.