Modeling complex, non-stationary dynamics remains challenging for deterministic neural networks. We present the Chaos-Integrated Synaptic-Memory Network (CISMN), which embeds controlled chaos across four modules—Chaotic Memory Cells, Chaotic Plasticity Layers, Chaotic Synapse Layers, and a Chaotic Attention Mechanism—supplemented by a logistic-map learning-rate schedule. Rigorous stability analyses (Lyapunov exponents, boundedness proofs) and gradient-preservation guarantees underpin our design. In experiments, CISMN-1 on a synthetic acoustical regression dataset (541 samples, 22 features) achieved R ² = 0.791 and RMSE = 0.059, outpacing physics-informed and attention-augmented baselines. CISMN-4 on the PMLB sonar benchmark (208 samples, 60 bands) attained R ² = 0.424 and RMSE = 0.380, surpassing LSTM, memristive, and reservoir models. Across seven standard regression tasks with 5-fold cross-validation, CISMN led on diabetes (R ² = 0.483 ± 0.073) and excelled in high-dimensional, low-sample regimes. Ablations reveal a scalability–efficiency trade-off: lightweight variants train in <10 s with >95% peak accuracy, while deeper configurations yield marginal gains. CISMN sustains gradient norms (~2300) versus LSTM collapse (<3), and fixed-seed protocols ensure <1.2% MAE variation. Interpretability remains challenging (feature-attribution entropy ≈ 2.58 bits), motivating future hybrid explanation methods. CISMN recasts chaos as a computational asset for robust, generalizable modeling across scientific, financial, and engineering domains.

The wide application of curved composite profiles across various industries raises questions about transferring findings from standardised tests to curved structures. Particularly for low-velocity impacts, understanding the deformation and damage behaviour of curved structures is crucial to achieve their lightweight potential. Carbon fibre reinforced polymer (CFRP) composites often show no surface visible damage after low-energy impacts, but barely visible impact damage (BVID) can compromise load-bearing capacity under continuous loads. This study aims to evaluate the effectiveness of self-reporting thin-ply hybrid composites as coating layers for structural health monitoring in curved panels, focusing specifically on enhancing the visual detection of BVID. Quasi-isotropic curved composite panels made from IM7 carbon/8552 epoxy were first manufactured, and their mechanical response under quasi-static indentation was analysed and compared to flat panels. Next, a hybrid composite—comprising a layer of unidirectional S-glass/epoxy and thin-ply YS-90A carbon/epoxy—was applied to the outer surfaces of the curved panels. To simulate a real-world application, the curved panels were designed with dimensions similar to those of composite hydrogen storage tanks. The results indicate that the hybrid composite sensors functioned satisfactorily and provided direct correlations between visible surface damage on the surface and BVID. The visual inspection results are connected to data obtained from the load-displacement graphs, enabling a thorough analysis of sensor performance in visualising different stages of BVID.

This paper investigates the relationship between nanomaterials concentration, scaffold topologies, and mechanical and vibrational performance of additive manufactured Polylactic Acid (PLA) scaffolds reinforced with Graphene Oxide (GO) and Carbon Nanotube (CNT). Three different scaffold topologies namely Cube, Diamond, and Lattice Diamond were designed. Every scaffold comprised PLA reinforced with GO and CNT at different concentration levels of 0 wt%, 0.2 wt%, 0.4 wt%, and 0.6 wt%. The mechanical performance of scaffolds was evaluated via compressive testing. A representative volume element (RVE) model, evaluated using periodic boundary conditions, was developed and successfully validated against experimental results, with a deviation between 13.5 % to 23 %. The novelty of the work lies in incorporating GO and SWCNT agglomeration within the RVE models, enabling a more accurate comparison with 3D-printed composite test samples. Additionally, a comprehensive evaluation of scaffold shapes, geometry, vibrational response, and reinforcement concentration, provides valuable insights into their performance. Experimental and RVE results indicated that the PLAs reinforced with 0.6 wt% GO and 0.6 wt% CNT experience agglomerations. Finite element simulations using the Mooney-Rivlin hyperelastic model showed that the compressive strength of the structures followed this order: Diamond > Cube > Lattice Diamond. The results showed that increasing GO and CNT content from 0 wt% to 0.4 wt% led the Diamond scaffold to exhibit the greatest improvement in compressive strength and elastic modulus, with increases of 79.3 %, 26 %, 165.9 %, and 129 %, respectively. It was found that the additive manufactured modified PLA Diamond scaffold displays the best mechanical performance among the other scaffold topologies. The finite element analysis using the Lanczos Eigensolver revealed that mechanical enhancements and structural topology directly impact natural frequency variations, making them major parameters to consider for specific applications.

This study evaluates the performance of damaged concrete beams retrofitted with a purpose-designed mechanochromic composite, which provides structural reinforcement and visual feedback for structural health monitoring (SHM). The retrofitting process utilizes externally bonded reinforcement (EBR) on pre-damaged concrete prisms. The mechanochromic composite, a thin-ply hybrid material made of unidirectional ultra-high modulus (UHM) carbon/epoxy and S-glass/epoxy layers, changes color to indicate structural overload when the UHM carbon layer fractures due to excessive strain. Eighteen concrete specimens were prepared and subjected to four-point bending tests, assessing various combinations of damaged, undamaged, retrofitted, and non-retrofitted configurations. Results showed that the mechanochromic composite functions effectively as both a passive visual sensor and reinforcement. For instance, a 5 % crack depth reduced load-bearing capacity by 30 %, however, retrofitting with the mechanochromic composite improved load-bearing capacity by up to 208 % compared to undamaged beams. The study further discusses the effects of different damage levels on load-bearing capacity through flexural strength, load-displacement curves, and failure modes.

Visual inspection is one of the most common nondestructive evaluation techniques that is performed through vision. This chapter will summarize visual inspection principles for detecting impact damage in composite materials. Effective parameters and available standards for visual inspection of low-velocity impact damage are introduced, and the relevant challenges are discussed. Current developments in improving visual inspection, including the use of artificial intelligence algorithms, as well as smart coating layers for improved visibility of impact damage are reported. Recent case studies are included, and future research directions are discussed.

A new method of creating in-plane combined tension-shear stress states using only tensile loading is proposed. Thin-ply angle-ply carbon/epoxy laminates are sandwiched between unidirectional glass layers to eliminate any stress concentration around the samples ends and gripping zone. Use of the thin plies successfully suppresses early occurrence of other modes of failure i.e. matrix cracking and free-edge delamination, so the first mode of failure is fibre failure in the angle-ply carbon sub-laminate. Compared with other methods of creating combined tension-shear stresses, the proposed technique has a simpler geometry, is significantly easier and cheaper to manufacture and test. Therefore, it provides more repeatable low-scatter experimental results. The obtained experimental results showed that the presence of in-plane shear stresses did not have a significant impact on the tensile fibre-direction failure strain of the tested carbon/epoxy laminate, suggesting that even at high shear stresses, the longitudinal tensile strength of the carbon/epoxy laminate is not significantly reduced.

Polymer composite laminates have established themselves as essential materials across a wide type of industrial fields because of their specific mechanical properties such as high strength and low weight. Among the main issues they face is susceptibility to delamination damage. This comprehensive review paper investigates various damage mechanisms and associated phenomena that obvious during delamination within polymer composite laminates. Delamination can primarily arise in Mode I, Mode II and mixed Mode I &II loading scenarios. Notably, the damage features can vary significantly between these conditions. This paper aims to characterize and identify delamination-dominated damage features by conducting a comprehensive examination of the parameters that influence these features, all based on an extensive literature review and utilizing fractography analysis. The findings of this review illustrate the valuable insights that can be obtained from delamination fracture surfaces through the utilization of fractography images and the examination of damage features. For instance, it is possible to recognize details such as determining of global crack growth direction, calculating the rate of fatigue crack growth, and anticipating of strain energy released rate. This deeper understanding aids in pinpointing the key factors contributing to delamination damage. It could offer valuable insights for designing composites resistant to delamination. Additionally, it may assist in determining the underlying causes of catastrophic failures in tragic events.