Search Results (89)

This work aims to investigate the buckling and post-buckling behaviour of wood-based sandwich structures with and without a manufacturing defect, under compressive loading. The specimens were made by gluing birch veneers to a balsa wood core. The defect consisted of a central zone where glue was lacking between the skin and the core. A compression load was applied to the plate using the VERTEX test rig, with the plate placed on the upper surface of a rectangular box and bolted at its borders. The upper surface of the plate was monitored using optical and infrared cameras. The stereo digital image correlation method was used to capture the in-plane and out-of-plane deformations of the specimen, and to calculate the strains and stresses. The infrared camera enabled the failure scenario to be identified. The buckling behaviour of pristine specimens showed small local debonding in the post-buckling range, which was not detrimental to overall performance. In the presence of a manufacturing defect, the decrease in buckling load was only about 15%, but final failure occurred at lower compressive loads. Full article

Semi-rigid base asphalt pavements, a common highway structure in China, often suffer from debonding defects which reduce road stability and shorten service life. In this study, a new method of road debonding detection based on the acoustic vibration method is proposed to address the needs of hidden debonding defects which are difficult to detect. The approach combines the Transformer model and the Transformer-based Parallel Cross-Gated Convolutional Neural Network (T-PCG-CNN) to classify and recognize semi-rigid base asphalt pavement acoustic data. Firstly, over a span of several years, an excitation device was designed and employed to collect acoustic data from different road types, creating a dedicated multi-sample dataset specifically for semi-rigid base asphalt pavements. Secondly, the improved Mel frequency cepstral coefficient (MFCC) feature and its first-order differential features (ΔMFCC) and second-order differential features (Δ₂MFCC) are adopted as the input data of the network for different sample acoustic signal characteristics. Then, the proposed T-PCG-CNN model fuses the multi-frequency feature extraction advantage of a parallel cross-gate convolutional network and the long-time dependency capture ability of the Transformer model to improve the classification performance of different road acoustic features. Comprehensive experiments were conducted to analyze parameter sensitivity, feature combination strategies, and comparisons with existing classification algorithms. The results demonstrate that the proposed model achieves high accuracy and weighted F1 score. The confusion matrix indicates high per-class recall (including debonding), and the one-vs-rest ROC curves (AUC ≥ 0.95 for all classes) confirm strong class separability with low false-alarm trade-offs across operating thresholds. Moreover, the use of blockwise self-attention with global tokens and shared weight matrices significantly reduces model complexity and size. In the multi-type road data classification test, the classification accuracy reaches 0.9208 and the weighted F1 value reaches 0.9315, which is significantly better than the existing methods, demonstrating its generalizability in the identification of multiple road defect types. Full article

Non-destructive testing (NDT) methods are critical for evaluating the structural integrity of and detecting defects in composite materials across industries such as aerospace and renewable energy. This review examines the recent trends and successful implementations of NDT approaches for composite materials, focusing on articles published between 2015 and 2025. A systematic literature review identified 120 relevant articles, highlighting techniques such as ultrasonic testing (UT), acoustic emission testing (AET), thermography (TR), radiographic testing (RT), eddy current testing (ECT), infrared thermography (IRT), X-ray computed tomography (XCT), and digital radiography testing (DRT). These methods effectively detect defects such as debonding, delamination, and voids in fiber-reinforced polymer (FRP) composites. The selection of NDT approaches depends on the material properties, defect types, and testing conditions. Although each technique has advantages and limitations, combining multiple NDT methods enhances the quality assessment of composite materials. This review provides insights into the capabilities and limitations of various NDT techniques and suggests future research directions for combining NDT methods to improve quality control in composite material manufacturing. Future trends include adopting multimodal NDT systems, integrating digital twin and Industry 4.0 technologies, utilizing embedded and wireless structural health monitoring, and applying artificial intelligence for automated defect interpretation. These advancements are promising for transforming NDT into an intelligent, predictive, and integrated quality assurance system. Full article

During the machining of unidirectional carbon fiber-reinforced polymers (UD-CFRPs), their anisotropic characteristics and the complex cutting conditions often lead to defects such as delamination, burrs, and surface/subsurface damage. This study systematically investigates the effects of different fiber orientation angles (0°, 45°, 90°, and 135°) on cutting force, chip formation, stress distribution, and damage characteristics using a coupled macro–micro finite element model. The model successfully captures key microscopic failure mechanisms, such as fiber breakage, resin cracking, and fiber–matrix interface debonding, by integrating the anisotropic mechanical properties and heterogeneous microstructure of UD-CFRPs, thereby more realistically replicating the actual machining process. The cutting speed is kept constant at 480 mm/s. Experimental validation using T700S/J-133 laminates (with a 70% fiber volume fraction) shows that, on a macro scale, the cutting force varies non-monotonically with the fiber orientation angle, following the order of 0° < 45° < 135° < 90°. The experimental values are 24.8 N/mm < 35.8 N/mm < 36.4 N/mm < 44.1 N/mm, and the simulation values are 22.9 N/mm < 33.2 N/mm < 32.7 N/mm < 42.6 N/mm. The maximum values occur at 90° (44.1 N/mm, 42.6 N/mm), while the minimum values occur at 0° (24.8 N/mm, 22.9 N/mm). The chip morphology significantly changes with fiber orientation: 0° produces strip-shaped chips, 45° forms block-shaped chips, 90° results in particle-shaped chips, and 135° produces fragmented chips. On a micro scale, the microscopic morphology of the chips and the surface damage characteristics also exhibit gradient variations consistent with the experimental results. The developed model demonstrates high accuracy in predicting damage mechanisms and material removal behavior, providing a theoretical basis for optimizing CFRP machining parameters. Full article

Debonding, especially in plastic materials, refers to the separation occurring at the interface within a bonded structure composed of two or more polymeric layers. Due to the great heterogeneity of materials and layering configurations, highly specialized expertise is often required to detect the presence and extent of such defects. This study presents a novel approach that leverages transfer learning techniques to improve the detection of debonding defects across different surface types using PICUS, an acoustic diagnostic device developed at Roma Tre University for the assessment of defects in heritage wall paintings. Our method leverages a pre-trained deep learning model, adapting it to new material conditions. We designed a planar test object embedded with controlled subsurface cavities to simulate the presence of defects of adhesion and air among the layers. This was rigorously evaluated using non-destructive testing using PICUS, augmented by artificial intelligence (AI). A convolutional neural network (CNN), initially trained on this mock-up, was then fine-tuned via transfer learning on a second test object with distinct geometry and material characteristics. This strategic adaptation to varying physical and acoustic properties led to a significant improvement in classification precision of defect class, from 88% to 95%, demonstrating the effectiveness of transfer learning for robust cross-domain defect detection in challenging diagnostic applications. Full article

Adhesive bonding has emerged as a transformative joining method across multiple industries, offering lightweight, durable, and versatile alternatives to traditional fastening techniques. This review provides a comprehensive exploration of adhesive bonding, from fundamental adhesion mechanisms, mechanical and molecular, to application-specific criteria and the characteristics of common adhesive types. Emphasis is placed on challenges affecting bond quality and longevity, including defects such as kissing bonds, porosity, voids, poor cure, and substrate failures. Critical aspects of surface preparation, bond line thickness, and adhesive ageing under environmental stressors are analysed. Furthermore, this paper highlights the pressing need for sustainable solutions, including the disassembly and recyclability of bonded joints, particularly within the automotive and aerospace sectors. A key insight from this review is the lack of a unified framework to assess defect interaction, stochastic variability, and failure prediction, which is mainly due complexity of multi-defect interactions, the compositional expense of digital simulations, or the difficulty in obtaining sufficient statistical data needed for the stochastic models. This study underscores the necessity for multi-method detection approaches, advanced modelling techniques (i.e., debond-on-demand and bio-based formulations), and future research into defect correlation and sustainable adhesive technologies to improve reliability and support a circular materials economy. Full article

Interface debonding between high-strength grouting materials and the inner surfaces of steel tubes in grouting jacket connections (GJCs), which have been widely employed in offshore wind turbine support structures, negatively affects their mechanical behavior. In this study, an interface debonding defect detection and localization approach for scaled underwater GJC specimens using surface wave measurements with piezoelectric lead zirconate titanate (PZT) actuation and sensing technology was validated experimentally. Firstly, GJC specimens with artificially mimicked interface debonding defects of varying dimensions were designed and fabricated in the lab, and the specimens were immersed in water to replicate the actual underwater working environment of GJCs in offshore wind turbine structures. Secondly, to verify the feasibility of the proposed interface debonding detection approach using surface wave measurements, the influence of the height and circumferential dimension of the debonding defects on the output voltage signal of PZT sensors was systematically studied experimentally using a one pitch and one catch (OPOC) configuration. Thirdly, a one pitch and multiple catch (OPMC) configuration was further employed to localize and visualize the debonding defect regions. An abnormal value analysis was carried out on the amplitude of the output voltage signals from PZT sensors with identical wave traveling paths, and the corresponding abnormal surface wave propagation paths were identified. Finally, based on the influence of interface debonding on the surface wave measurements mentioned above, the mimicked interface debonding defect was detected successfully and the region of debonding was determined with the intersection of the identified abnormal wave travelling paths. The results showed that the mimicked debonding defect can be visualized. The feasibility of this method for interface debonding defect detection in underwater GJCs was confirmed experimentally. The proposed approach provides a novel non-destructive debonding defect detection approach for the GJCs in offshore wind turbine structures. Full article

Interface debonding between the steel tube and grouting materials in grouting jacket connections (GJCs) of offshore wind turbine supporting structures leads to negative effects on the load-carrying capacity and safety concerns. In this paper, an interface debonding defect detection and localization approach for scale underwater GJC specimens using surface wave measurement is proposed and validated numerically. A multi-physics finite element model (FEM) of underwater GJCs with mimicked interface debonding defects, surrounded by water, and coupled with surface-mounted piezoelectric lead zirconate titanate (PZT) patches is established. Under the excitation of a five-cycle modulated signal, the surface stress wave propagation, including transmission, diffraction, and reflection, within the outer steel tube, grouting material, and inner steel tube is simulated. The influence of mimicked interface debonding defects of varying dimensions on stress wave propagation is systematically analyzed through stress wave field distributions at distinct time intervals. Additionally, the response of surface-mounted PZT sensors in the underwater GJC model under a one-pitch-one-catch (OPOC) configuration is analyzed. Numerical results demonstrate that the wavelet packet energy (WPE) of the surface wave measurement from the PZT sensors corresponding to the traveling path with a mimicked interface debonding defect is larger than that without a defect. To further localize the debonding region, a one pitch and multiple catch (OPMC) configuration is employed, and an abnormal value analysis is conducted on the WPEs of PZT sensor measurements with identical and comparable wave traveling patches. The identified debonding regions correspond to the simulated defects in the models. Full article

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article

Flexible pipe end fittings (EFs) transfer axial loads by embedding tensile armor within epoxy matrices. The integrity of bonding between the armor and resin profoundly influences the EF load-bearing capacity. This study investigated the debonding failure mechanism at the epoxy-resin–tensile-armor interface in flexible pipe end fittings through integrated experimental and numerical approaches. Combining tensile tests with digital image correlation (DIC) and cohesive zone modeling (CZM), the research quantified the impacts of interfacial defects and adhesive properties on structural integrity. Specimens with varying bond lengths (40–60 mm) and defect diameters (0–4 mm) revealed that defects significantly reduced load-bearing capacity, with larger defects exacerbating strain localization and accelerating failure. A dimensionless parameter, the defect-size-to-bond-length ratio ($\lambda =D/2L$), was proposed to unify defect impact analysis, demonstrating its nonlinear relationship with failure load reduction. High-toughness adhesives, such as Sikaforce^® 7752, mitigated defect sensitivity by redistributing stress concentrations, outperforming brittle alternatives like Araldite^® AV138. DIC captured real-time strain evolution and crack propagation, validating strain concentrations up to 3.2 at defect edges, while CZM simulations achieved high accuracy (errors: 3.0–7.2%) in predicting failure loads. Critical thresholds for λ (λ < 0.025 for negligible impact; λ > 0.05 requiring defect control or high-toughness adhesives) were established, providing actionable guidelines for manufacturing optimization and adhesive selection. By bridging experimental dynamics with predictive modeling, this work advances the design of robust deepwater energy infrastructure through defect management and material innovation, offering practical strategies to enhance structural reliability in critical applications. Full article

The aviation industry currently faces several challenges in inspecting and diagnosing aircraft structures. Current aircraft inspection methods still need to be fully automated, making early detection and precise sizing of defects difficult. Researchers have expressed concerns about current aircraft inspections, citing safety, maintenance costs, and reliability issues. The next generation of aircraft inspection leverages semi-autonomous and fully autonomous systems integrating robotic technologies with advanced Non-Destructive Testing (NDT) methods. Active Thermography (AT) is an example of an NDT method widely used for non-invasive aircraft inspection to detect surface and near-surface defects, such as delamination, debonding, corrosion, impact damage, and cracks. It is suitable for both metallic and non-metallic materials and does not require a coupling agent or direct contact with the test piece, minimising contamination. Visual inspection using an RGB camera is another well-known non-contact NDT method capable of detecting surface defects. A newer option for NDT in aircraft maintenance is 3D scanning, which uses laser or LiDAR (Light Detection and Ranging) technologies. This method offers several advantages, including non-contact operation, high accuracy, and rapid data collection. It is effective across various materials and shapes, enabling the creation of detailed 3D models. An alternative approach to laser and LiDAR technologies is photogrammetry. Photogrammetry is cost-effective in comparison with laser and LiDAR technologies. It can acquire high-resolution texture and colour information, which is especially important in the field of maintenance inspection. In this proposed approach, an automated vision-based damage evaluation system will be developed capable of detecting and characterising defects in metallic and composite aircraft specimens by analysing 3D data acquired using an RGB camera and a IRT camera through photogrammetry. Such a combined approach is expected to improve defect detection accuracy, reduce aircraft downtime and operational costs, improve reliability and safety and minimise human error. Full article

Debonding in concrete-filled steel tubes (CFSTs) is a common defect that often occurs during the construction phase of CFST structures, significantly reducing their load-bearing capacity. Current methods for detecting debonding in CFSTs using infrared thermography primarily rely on heat excitation. However, applying this method during the exothermic hydration phase presents considerable challenges. This paper proposes the innovative use of spray cooling as an excitation method during the exothermic hydration phase, providing quantitative insights into the heat conduction dynamics on steel plates for infrared debonding detection in CFSTs. The effects of atomization level, excitation distance, excitation duration, and water temperature in the tank on infrared debonding detection performance were examined. The timing of the maximum temperature difference under cooling excitation was analyzed, and the heat conduction characteristics on the surface of the steel plate during the cooling process were explored. A highly efficient and stable cooling excitation method, suitable for practical engineering detection, is proposed, providing a foundation for quantitative infrared debonding detection in CFSTs. This method does not require additional energy sources, features a simple excitation process, and results in a five-times increase in temperature difference in the debonded region after excitation. Full article

Carbon fiber/polyether ether ketone (CF/PEEK) is widely used in aerospace, transportation, and other high-end industries for its light weight, high strength, and recyclability. However, its inherently brittle–ductile two-phase structure presents challenges in processing CF/PEEK. This paper introduces a laser-assisted milling method, wherein four types of CF/PEEK unidirectional plates (0°, 45°, 90°, and 135°) are milled under varying laser powers and spindle speeds. The results are compared with conventional milling (CM) techniques, based on cutting forces, temperatures, surface roughness, and damage defects. The cutting force, temperature, and surface quality were optimal at a fiber direction of 0° and were least favorable at 90° under identical machining conditions. When the fiber direction was 90°, the milling temperatures at 400 W and 500 W laser power decreased by 19.8% and 7.9%, respectively, while the average values of Fx and Fy decreased by 20.5% and 9.55%, compared to conventional milling. Furthermore, the laser-assisted milling method significantly reduces surface defects and improves surface roughness. In CF/PEEK composites, brittle fracture is the primary material removal mechanism, with damage characteristics such as fiber fracture, fiber pullout, and fiber/matrix debonding. The optimal parameter combination is a 0° fiber orientation, 400 W laser power, and a spindle speed of 4000 rpm. This study provides theoretical and technical support for the high-quality processing of CF/PEEK composites. Full article

Carbon fiber-reinforced polymer (CFRP) composites, renowned for their high strength-to-weight ratio, are increasingly utilized in the strengthening of structural components. The application of CFRP for strengthening concrete components notably improves the cracking moment and substantially elevates the ultimate load-bearing capacity. This study focuses on a reinforced concrete (RC) two-way slab with an initial defect, specifically an initial deflection. To avert deformations or damages that could break structural integrity during service, the slab was strengthened by adhering CFRP to its underside. An in situ multi-stage loading test was conducted to evaluate the load-bearing capacity of the CFRP-strengthened slab, and the findings revealed that the mid-span deflection of the two-way slab incrementally reached 1.64 mm after the loading stages, with no observable signs of concrete cracking, debonding, or tearing of the CFRP-strengthened slab. The failure modes indicated a transition from concrete compression damage to CFRP anchorage stress concentrations, highlighting the effective stress distribution and load-sharing synergy provided by CFRP-strengthening. Additionally, a numerical model based on the finite element (FE) method was developed using ABAQUS to simulate the component’s performance during the loading process. A comparison between the measured mid-span deflection of the strengthened slab and the numerically simulated values confirmed the high accuracy and rationality of the simulation method. Utilizing the validated numerical model, an analysis of the slab’s ultimate load capacity was conducted, demonstrating that the CFRP strengthening technique effectively increased the load-bearing capacity of the initially imperfect RC two-way slab by nearly 50%. Full article

This study presents a comprehensive method for detecting debonding defects in concrete-filled steel tube (CFST) structures using wave propagation analysis with externally attached piezoelectric ceramic sensors. Experimental tests and numerical simulations were conducted to evaluate the sensitivity and accuracy of two measurement techniques—the flat and oblique measurement methods—in detecting debonding defects of varying lengths and heights. The results demonstrate that the flat measurement method excels in detecting debonding height, while the oblique method is more effective for detecting debonding length. A normalized judgment index (DI) was introduced to quantify the correlation between debonding characteristics and the detected signal amplitude, revealing the significant influence of sensor spacing on detection accuracy. Furthermore, a mathematical model based on wavelet packet energy analysis was developed to establish a linear relationship between wavelet packet energy and debonding size. This model offers a scientific foundation for the quantitative detection of debonding defects and provides a new approach to the health monitoring of CFST structures. The integrated use of both measurement techniques enhances detection precision, enabling both qualitative and quantitative defect analysis, which can significantly guide the maintenance and repair of CFST structures. Full article