Smart composite materials with integrated sensing layers are gaining attention for their potential to improve structural health monitoring and damage detection in high-performance applications. This study experimentally evaluates validated advanced simulation techniques to investigate impact-induced damage in such composites, with a particular focus on barely visible impact damage (BVID). A refined finite element model is developed using user-defined cohesive materials to capture both matrix cracking and delamination, which are critical to understanding damage mechanisms associated with BVID. The model is applied to hybrid laminates incorporating surface-integrated sensing layers composed of ultra-high modulus carbon and S-glass fibres. These layers are designed to show visible signs of damage that can be correlated with internal failure mechanisms. The simulation results are compared against experimental data, including C-scan imaging and surface inspections, to assess accuracy in predicting damage initiation, growth, and patterns. Particular attention is given to the effects of through-thickness compression and the interaction between different failure modes. This work offers practical insights for reducing reliance on costly testing during the early stages of material and structural design for smart composites.

Field measurement of structural displacement and inclination is hindered by drift in double integration, stringent filtering needs, and the limited compute/bandwidth of low-cost microcontrollers. This work presents node based on an ADXL345 tri-axial accelerometer with on-board processing that estimates dynamic tilt and symmetry in real time without double integration. Three real-time filters, Butterworth IIR (BWF), finite impulse response (FIR), and moving average (MAF, uniform-tap FIR), were implemented on the device and benchmarked against an offline Savitzky–Golay reference. A rigid-body rotation model links off-centre acceleration to inclination and defines a dimensionless rotation index for symmetry assessment. Calibration against analytical motion identified a linear-phase FIR as optimal, yielding the lowest RMSE over 0.5–8 Hz while preserving waveform shape. Computational profiling on an 11.0592 MHz microcontroller measured average per-sample execution of 2.5 µs (MAF), 6 µs (FIR), and 12 µs (BWF), enabling 800 Hz local sampling with ≥ 100 Hz wireless streaming and an 8 × reduction via on-device decimation (no compression). Under cyclic tension, lateral acceleration quantified asymmetry: non-cracked and two-sided-cracked specimens showed comparable lateral levels, whereas one-sided-cracked specimens exhibited markedly higher values consistent with crack-induced rotation. Vertical acceleration agreed with acceleration reconstructed from displacement, with peak deviations of ∼ 9–14%; accuracy decreased at the lowest acceleration levels, consistent with stronger low-frequency spectral content. Tilt from acceleration tracked displacement-based tilt with minor underestimation at the smallest amplitudes. Overall, the node delivers embedded, phase-faithful filtering and tilt/symmetry estimation with quantified computational cost, making acceleration-based monitoring practical on resource-constrained hardware while avoiding big-data burdens.

Favourable pseudo-ductile behaviour under compressive loading with a knee-point was achieved for unidirectional (UD) interlayer hybrids made of thin-ply high modulus carbon/epoxy (CF/EP) layers sandwiched between standard thickness glass/epoxy (GF/EP). The UD thin-ply hybrids were tested under two loading scenarios: 1. Direct compressive loading, 2. Four-point bending loading. In both cases, the damage mechanisms responsible for the pseudo-ductile behaviour are fragmentation of the carbon layer and localised delamination, which later propagates unstably. The final failure of the UD thin-ply hybrid composites examined in four-point bending loading occurs at a higher strain than that under direct compressive loading. This is due to the strain gradient in bending, which results in a lower energy release rate than in direct compression. An increasing carbon layer thickness reduces the final delamination failure strain of the UD thin-ply hybrid composites in compression, but the knee-point strain is not affected.

The buckling mode in piezoelectric materials offers advantages such as an increased measurable strain range, ease of installation, and extended service life. This paper investigates the potential of piezoelectric sensors operating in buckling mode for structural strain measurement by evaluating key factors including boundary conditions, sensor response linearity under dynamic loading, and impedance engineering to optimize the voltage–strain relationship. A structural extension was developed to facilitate sensor integration and to enable the application of different buckling boundary conditions. Results show that the clamped–clamped configuration generated at least 1.65 times higher output voltage, and three times greater peak strain compared to other boundary conditions. An experimentally validated analytical model was employed to assess and improve the performance of buckled piezoelectric sensors in dynamic environments. The findings highlight that introducing initial buckling reduces signal perturbations, enhances voltage linearity across loading frequencies, and extends the effective strain measurement range. Furthermore, impedance engineering was used to successfully mitigate the nonlinear effects of transient response, thereby improving signal stability and accuracy in dynamic strain monitoring 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.

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.

Accurate evaluation of air-void content in hardened concrete is important for assessing durability and long-term performance. Traditional methods often require time-consuming surface preparation and depend on imaging techniques that are sensitive to surface texture. This study investigates the potential of a high-resolution tactile sensing approach for non-destructive void detection with minimal surface preparation. To understand the effect of surface roughness, void detection was first performed on paired concrete specimens with identical internal surfaces, while one was left unpolished and the other polished. Despite the presence of surface irregularities, the tactile sensor was still able to identify most of the voids larger than a defined threshold, demonstrating its effectiveness even under challenging surface conditions. In the second phase, the sensor’s void quantification performance was benchmarked on a polished specimen using a standardized digital microscopy-based method. The tactile system estimated porosity at 4.58% with an average void area of 0.013 mm², closely matching the reference digital microscopy-based results of 5.05% and 0.011 mm². These results highlight the sensor’s capability to produce consistent and quantitative measurements on both rough and smooth surfaces. The approach offers a practical alternative to traditional void analysis methods, with the potential to simplify inspection workflows and support future automation in concrete surface evaluation.

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.

Accurate and reliable strain measurement is essential for effective condition monitoring of engineering structures. This study presents an analytical and experimental investigation into the performance of piezoelectric sensors for structural strain measurements, evaluating the effect of attachment strategy and the properties of the substrate and the sensor. Lead zirconate titanate (PZT) and polyvinylidene fluoride (PVDF) sensors were evaluated in two attachment configurations: Fully Attached (FA) and Two-End Attached (TEA). A voltage-strain relationship was developed based on principles of piezoelectricity, electrical circuit modelling, and solid mechanics. Results indicate that sensor performance is significantly influenced by the attachment method. Specifically, the TEA configuration reduced the impact of substrate properties and improved uniaxial strain measurement accuracy by up to 32 % compared to the FA configuration. The FA configuration exhibited sensitivity to the substrate's Poisson ratio, leading to a nonlinear voltage-strain response. In contrast, the TEA configuration provided pure uniaxial strain measurements by reducing the effects of shear lag and substrate elasticity. These findings provide a comprehensive approach to using piezoelectric sensors for structural strain measurement, allowing for the placement of sensors on various substrates without the need for calibration by effectively utilizing sensor and substrate properties along with the attachment strategy. The study provides a novel analytical–experimental comparison of sensor attachment methods, showing how TEA significantly improves uniaxial strain accuracy and reduces substrate dependency in piezoelectric strain measurements.

Purpose – This study aims to automate the visual inspection of piling sheets in water channel construction using artificial intelligence (AI). By employing image classification and object detection techniques, the research focuses on extracting and analysing geometric features to enhance the accuracy and efficiency of the inspection process. It also addresses key challenges associated with the unique characteristics of construction materials and the limited variability of available inspection datasets. Design/methodology/approach – Convolutional neural networks (CNNs) with varying complexities are employed for image classification, across four and six classes, and for object detection of piling sheets in water channel environments. A dataset provided by Witteveen + Bos is preprocessed to generate training sets, and the CNN architectures are optimized for enhanced performance. The accuracy and efficiency of the proposed models are evaluated and compared against traditional manual inspection methods. Findings – The AI-driven approach significantly reduces processing time, evaluating 40, 000 images in just 11.9 h, compared to approximately one month using manual assessment. The 4-class classification model achieves an accuracy of 96%, while the 6-class model attains 72%. The object detection model produces a mean average precision (mAP) of 79%. These results meet the performance standards set by the Dutch company Witteveen + Bos, which demonstrate the effectiveness of AI in automating the inspection of piling sheets. Originality/value – This study introduces a novel AI-based approach for assessing piling sheets, demonstrating substantial improvements over traditional inspection methods. It introduces a systematic evaluation of various CNN architectures and hyperparameters to optimize the models specifically for piling sheet inspection rather than relying on off-the-shelf solutions. The use of CNNs for both image classification and object detection adheres to relevant Dutch engineering standards. Notably, the reduction in processing time, from one month to around 12 h, represents a major advancement in the efficiency of civil engineering inspections.

High energy consumption in residential buildings poses significant challenges, prompting governments to regulate this sector through comprehensive energy assessments and classification strategies. This study introduces a multi-layer perceptron artificial neural network (ANN) model to grade and predict energy consumption levels in residential buildings in Tabriz, Iran, based on their geometric and functional characteristics. This study uses the K-Nearest Neighbors (KNN) algorithm to classify energy consumption grades based on energy ratio (R-value). Six sample buildings were modeled using Rhinoceros 3D version 7 and Grasshopper version 1.0.0007 software to extract key energy-influencing factors. A parametric geometric model was developed for rapid data generation and validated against reference buildings to ensure reliability. Building classifications spanned areas of 40 to 300 square meters and heights of up to six stories, with energy evaluations conducted using EnergyPlus. The collected data informed the ANN model, enabling accurate predictions for existing and future constructions. The results demonstrate that the model achieves a remarkable prediction error of just 0.001, facilitating efficient energy assessments without requiring extensive modeling expertise. This research emphasizes the role of geometric features and natural lighting in energy consumption prediction, highlighting the model’s practicality for early design evaluations and architectural validations.

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.

Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer distinct advantages, including compact size, lightweight design, low cost, flexible formats, and high sensitivity to dynamic loads. The paper reviews the working principles and recent advancements in passive piezoelectric-based sensors, such as acoustic emission wave and strain measurements, and active piezoelectric-based sensors, including ultrasonic wave and dynamic characteristic measurements. These measurements, captured under in-service dynamic strain, can be correlated to the remaining structural fatigue life. Case studies are presented, highlighting applications of fatigue life monitoring in metals, polymeric composites, and reinforced concrete structures. The paper concludes by identifying challenges and opportunities for advancing piezoelectric-based sensors for fatigue life monitoring in engineering structures.

This paper reports the development, optimization, and real-world application of an innovative wobbling triboelectric nanogenerator to harvest energy from wind or vibrations. The harvester features a spring-supported structure, purposely designed to become unbalanced and reversibly shift from a steady to a non-steady state in response to minimal wind or vibration stimuli. Unlike conventional wind turbines, this approach transforms wind energy into contact-separation events via a wobbling structure. The harvester’s mechanisms are engineered to enhance power generation efficiency, optimizing parameters like electrode dimensions and contact-separation quality. Experimental findings showcase a maximum output power density of 1.6 W/m2 under optimal conditions, employing a fixed suspending mechanism for heightened impact energy during contact. Moreover, the harvester efficiently charges a 3.7 V lithium-ion battery with over 4.5 μA, showcased in a self-powered light mast as a practical demonstration. The harvester provides cost-effectiveness by utilizing inexpensive, easily accessible materials without complex fabrication. This research paves the way for future exploration in integrating triboelectric nanogenerators with wobbling mechanisms, offering a promising pathway for sustainable power generation across various applications, including lighting and IoT sensor nodes.

The present paper reports an overview of mechanochromic self-reporting thin-ply hybrid composite sensors, which are designed to visually indicate overload in structures. These sensors, made from combinations of high-strain and low-strain materials, change appearance earlier than the final fracture, providing a clear visual cue of damage like delamination or strain overload. They can be used for various applications, for example, overload monitoring, and to detect barely visible impact damage in composites.

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.

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

The study considers the delamination resistance of carbon/epoxy laminates modified with Thermoplastic Nanoparticles of Polysulfone (TNPs). A new electrospinning nanofiber technique was utilized to convert polysulfone polymer into nanoparticles and uniformly disperse them within the resin. Fracture toughness was evaluated under loading modes I and II. In mode I, the toughness (GIC) increased significantly from 170 to 328 J/m² with TNPs incorporation. However, mode II showed minimal change, with GIIC values of 955 J/m² for virgin and 950 J/m² for TNPs-modified specimens. Scanning Electron Microscopy (SEM) was employed to depict the influence of TNPs on damage characteristics and crack propagation patterns. In mode I, crack deviation enhanced toughness as TNPs bypassed the PSU, while in mode II, cracks propagated through TNPs, resulting in particle smearing on the epoxy surface. This highlights TNPs' potential to modify the fracture toughness in mode I loading, but their effect is constrained in mode II loading scenarios.

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.

Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced using random changes in the stiffness of different parts of the structure. Contrary to the assumption of inherent reliability, undetected defects in crucial structural elements can lead to catastrophic failures. Then, the vibration equations of healthy and damaged models are analyzed to find the displacement, velocity, and acceleration of the different crane parts. The learning vector quantization neural network is used to train and detect the defects. The output is the location of the damage and the damage severity. Noisy data are then used to evaluate the network performance robustness. This research also addresses the limitations of traditional inspection methods, providing early detection and classification of defects in gantry cranes. The study’s relevance lies in the need for a comprehensive and efficient damage detection method, especially for components not easily accessible during routine inspections.