Search Results (221)

Infrared thermography non-destructive testing technology has been widely used in the defect detection of composite structures due to its advantages, including non-contact operation, rapidity, low cost, and high precision. In this study, a laser-line scanning system combined with an infrared thermography was developed, along with a corresponding dynamic sequence image reconstruction method, enabling rapid localization of surface damages. Then, high-precision quantitative characterization of defect morphology in reconstructed images was achieved by integrating an edge gradient detection algorithm. The reconstruction method was validated through finite element simulations and experimental studies. The results demonstrated that the laser-line scanning thermography effectively enables both rapid localization of surface damages and precise quantitative characterization of their morphology. Experimental measurements of ceramic materials indicate that the relative error in detecting crack width is about 6% when the crack is perpendicular to the scanning direction, and the relative error gradually increases when the angle between the crack and the scanning direction decreases. Additionally, an alumina ceramic plate with micrometer-width cracks is inspected by the continuous laser-line scanning thermography. The morphology detection results are completely consistent with the actual morphology. However, limited by the spatial resolution of the thermal imager in the experiment, the quantitative identification of the crack width cannot be carried out. Finally, the proposed method is also effective for detecting surface damage of wrinkles in ceramic matrix composites. It can localize damage and quantify its geometric features with an average relative error of less than 3%, providing a new approach for health monitoring of large-scale ceramic matrix composite structures. Full article

This brief editorial introduces the Special Issue “Computational Methods in Structural Engineering”. This Special Issue brings together recent advances in computational approaches—including finite element modeling, machine learning applications, stochastic analysis, and high-precision numerical methods— highlighting their increasing influence on the analysis, design, and assessment of modern structural systems. The published contributions cover topics such as the nonlinear finite element method (FEM) for structural response under extreme loading, advanced plate and composite modeling, explainable AI for material characterization, machine learning for predictive performance modeling, data-driven signal processing for structural health monitoring, and stochastic analysis of dynamic inputs. Through this collection of studies, this Special Issue underscores both the opportunities and the challenges of applying advanced computational methods to enhance the resilience, efficiency, and understanding of structural engineering systems. Full article

This study aims to evaluate the effectiveness of five analytical and semi-analytical methods for estimating Lamb wave dispersion in viscoelastic plates—the Rayleigh–Lamb solution, the Global Matrix Method (GMM), the Semi-Analytical Finite Element (SAFE) method, the Scaled Boundary Finite Element Method (SBFEM), and the Legendre Polynomial Method (LPM). The Rayleigh–Lamb equations are solved using an optimized Newton–Raphson algorithm, enhancing computational efficiency while maintaining comparable accuracy. The SAFE method exhibited a remarkable balance between computational efficiency and physical accuracy, outperforming SBFEM at high frequencies. For epoxy and high-performance polyethylene (HPPE) plates, the SAFE method and the LPM significantly outperform the GMM in relation to computational efficiency, with errors below $1\%$ for fundamental symmetric and antisymmetric modes across the tested frequency range of 0 to 100 kHz. In addition, the ability of the SAFE method to accurately predict both phase velocity and attenuation in viscous media supports their use in guided-wave-based structural health monitoring applications. Among the investigated approaches, the SAFE method emerges as the most robust and accurate for viscoelastic plates, while the SBFEM and LPM show limitations at higher frequencies. This study provides a quantitative and methodological foundation for selecting and implementing numerical methods for guided wave analysis, emphasizing the dual necessity of physical fidelity and numerical stability. Full article

Effective glucose monitoring is paramount for patients with diabetes to effectively manage their condition and prevent health complications. Electrochemical sensors for glucose monitoring have key advantages over other systems, including cost-effectiveness, miniaturisation and portability, enabling the design of compact and wearable devices. Typically, enzymes are used in these sensors, with the limitations of poor stability and high cost. In alternative, this study reports the development of a gold and platinum composite nanostructured electrode and its testing as an abiotic (enzyme-free) electrocatalyst for glucose oxidation. The electrode consists of a film of highly porous gold electrodeposited onto gold-plated electrodes on a printed circuit board (PCB), which is coated with polyaniline decorated with platinum nanoparticles. The resulting nanocomposite structure shows a sensitivity towards glucose as high as 95.12 ± 2.54 µA mM⁻¹ cm⁻², nearly twice that of the highly porous gold electrodes, and excellent stability in synthetic interstitial fluid over extended testing, thus demonstrating robustness. Accordingly, this study lays the groundwork for the next generation of durable, selective, and affordable abiotic glucose biosensors. Full article

The accuracy of modal superposition methods for determining displacement or strain field of structures largely depends on the selection of modes relevant to its deformation. Analytical methods for modal selection have been developed to minimise errors in reconstructing deformation through a linear combination of modal shapes. This study constitutes an initial step towards the development of structural health-monitoring algorithms for large engineering machines, where continuous monitoring of strain and stress, assuming a linear elastic field, is critical. The focus is on selecting modes that significantly contribute to the reconstruction of static deformation of structures. A detailed analytical approach, derived from established structural dynamics principles, leads to the formulation of modal selection criteria. These criteria are based on two fundamental quantities from dynamic and elastic theory: the modal participation factor and internal strain potential energy. Three criteria are introduced: the directional participation factor criterion (DPFC), the global participation factor criterion (GPFC), and the internal strain potential energy criterion (ISPEC). While DPFC and GPFC rely on displacements, ISPEC uses strains. The methods are validated through a case study involving a rectangular plate subjected to various loads, demonstrating their applicability to complex deformation scenarios, which require the combination of multiple modes to fully describe the static deformation. The proposed criteria are formulated for linear elastic systems and are therefore applicable, in principle, to plate-like components, machine casings, thin structural panels, and certain civil and aerospace panels, under the assumptions of small strains, linear constitutive behaviour, and validity of modal superposition. The approach also represents a first step towards the integration of modal selection with machine learning for structural health-monitoring applications and presents a computational cost significantly lower than that of full finite element analyses. Full article

Acoustic Emission tomography (AET) has the potential to visualize damage in existing structures, contributing to structural health monitoring. Further, AET requires only the arrival times of elastic waves at sensors to identify velocity distributions, as source localization based on ray-tracing is integrated into its algorithm. Thus, AET offers the advantage of easy acquisition of measurement data. However, accurate source localization requires a large number of elastic wave source candidate points, and increasing these candidates significantly raises the computational resource demand. Lagrange Interpolation has the potential to reduce the number of candidate points, optimizing computational resources, and this potential has been validated numerically. In this study, AET incorporating Lagrange Interpolation is applied to identify the velocity distribution in a defective concrete plate, validating its effectiveness using measured wave data. The validation results show that the defect location in the concrete plate is successfully identified using only 36 source candidates, compared to the 121 candidates required in conventional AET. Furthermore, when using 36 source candidates, the percentage error in applying Lagrange Interpolation is 8.4%, which is significantly more accurate than the 25% error observed in conventional AET. Therefore, it is confirmed that AET with Lagrange Interpolation has the potential to identify velocity distributions in existing structures using optimized resources, thereby contributing to the structural health monitoring of concrete infrastructure. Full article

Structural Health Monitoring (SHM) in aerospace engineering is more and more based on the use of Artificial Intelligence. In this manuscript machine learning algorithms were trained to identify and to characterize the structural effects of impacts on a typical aerospace aluminum panel. A significant experimental campaign was conducted to create suitable impact datasets (the vibrational behavior of the reinforced plate, acquired by piezo sensors). Shallow neural networks, properly trained, were applied to determine critical events affecting the operational conditions. The focus of the manuscript was double: on the severity of the event (a regression problem regarding impact energy) and on the detection of preexisting damage to monitored areas (a classification problem regarding the identification of damaged zones). The scope of this work was to demonstrate the validity of the machine learning approach as an SHM tool for impact effect characterization in a realistic aerospace structure (i.e., energy prediction with a percentage error never more than 10% and identification of previous damaged zones with an accuracy of more than 95%) and to demonstrate its computational efficiency despite the test complexity, provided that the selection of features is guided by a meaningful physical and mechanical interpretation of the underlying phenomena. Full article

It is important to accurately characterize the plane-stress state of pipe walls for evaluating the bearing capacity of the pipe and ensuring the structural safety. This paper describes a novel ultrasonic technique for evaluating anisotropic pipe-wall plane stresses using three-directional longitudinal critical refracted (LCR) wave time-of-flight (TOF) measurements. The connection between plane stress and ultrasonic TOF is confirmed by examining how the anisotropy of rolled steel plates affects the speed of ultrasonic wave propagation, which is a finding not previously documented in spiral-welded pipes. Then based on this relationship, an ultrasonic stress coefficient calibration experiment for spiral-welded pipes is designed. The results show that the principal stress obtained by the ultrasonic method is closer to the engineering stress than that obtained from the coercivity method. And, as a nondestructive testing technique, the ultrasonic method is more suitable for in-service pipelines. It also elucidates the effects of probe pressure and steel plate surface roughness on the ultrasonic TOF, obtains a threshold for probe pressure, and reveals a linear relationship between roughness and TOF. This study provides a feasible technique for nondestructive measurement of plane stress in anisotropic spiral-welded pipelines, which has potential application prospects in the health monitoring of in-service pipelines. Full article

This study investigates the structural behavior of a sailing catamaran subjected to wind, wave, and self-weight loads, with the ultimate goal of improving the structural design through redrawing techniques. A digital model was developed using Creo software 6 and analyzed through Finite Element Analysis (FEA), complemented by experimental deformation tests conducted under dry conditions and controlled loading. These tests provided a reliable dataset for calibrating and validating the numerical model. The analysis focused on the structural responses of key components—such as bulkheads, hulls, and beam-to-hull connections—under both isolated as well as combined load scenarios. Most structural elements demonstrated low deformation, confirming the robustness of the design; however, stress concentrations were observed at the connecting plates, highlighting areas for improvement. The vessel’s overall stiffness, though advantageous for structural integrity, was identified as a constraint in weight redrawing efforts. Consequently, targeted structural modifications were proposed and implemented, resulting in reduced material usage, construction time, and overall costs. The study concludes by proposing the integration of advanced composite materials to further enhance performance and efficiency, thereby laying the groundwork for future integration with digital and structural health monitoring systems. Full article

This study investigates a novel pulsed electromagnetic field (PEMF) device for dynamic testing and structural health monitoring. The research utilises a PEMF generator CD-1501 with a maximum energy capacity of 0.5 kJ and a flat multifilament coil (IC-1) with a 100 mm diameter. Experiments were conducted on a model steel stand with two joint configurations, using steel plates of 4 mm and 8 mm thickness. The device’s efficacy was evaluated through oscillation pattern analysis and spectral characteristics. Results demonstrate the device’s ability to differentiate between joint states, with the 4 mm plate configuration showing a 15% reduction in high-frequency components compared to the 8 mm plate. Fundamental resonant frequencies of 3D-printed specimens were observed near 5100 Hz, with Q-factors ranging between 200 and 300. The study also found that a 10% increase in volumetric porosity led to a 7% downward shift in resonant frequencies. The developed PEMF device, operating at 50–230 V and delivering 1–5 pulses per minute, shows promise for rapid, non-destructive monitoring of structural joints. When combined with the coaxial correlation method, the system demonstrates enhanced sensitivity in detecting structural changes, utilising an electrodynamic actuator (10 Hz to 2000 Hz range). This integrated approach offers a 30% improvement in early-stage degradation detection compared to traditional methods. Full article

Untreated olive oil mill wastewater (OOMW) from conventionally farmed olives was used in bread production to create a new functional product. Two types of bread were developed with 50% OOMW (EXP-1) and 100% OOMW (EXP-2) replacing water. Two leavening processes were tested: sourdough inoculum (S) vs. biga-like inoculum (B), with controls (CTR) without OOMW addition. The doughs were monitored throughout the acidification process by measuring pH, total titratable acidity, and the development of key fermentative microorganisms. To assess the hygienic quality during fermentation, plate count techniques were employed. After baking, the breads were evaluated for various quality parameters, including weight loss, specific volume, crumb and crust colors, image analysis, and the presence of spore-forming bacteria. Volatile compounds released from the breads were identified using solid-phase microextraction coupled with gas chromatography–mass spectrometry (SPME-GC/MS). Polyphenolic compounds were analyzed via liquid chromatography–mass spectrometry (LC-MS). To assess the functional properties of the final products, the breads were homogenized with synthetic human saliva and subjected to in vitro digestion. OOMW did not significantly affect the growth of yeasts and lactic acid bacteria (LAB) or the acidification process. However, in terms of the specific volume and alveolation, breads from the S process and OOMW had poor quality, while those from the B process had better quality. Experimental breads (EXP_B-1 and EXP_B-2) contained higher levels of alcohols (especially ethanol and isobutyl alcohol), carbonyl compounds (like benzaldehyde), esters (such as ethyl caproate and ethyl caprylate), and terpenes. OOMW introduced phenolic compounds like hydroxytyrosol, coumaric acid, caffeic acid, and trans-hydroxycinnamic acid, which were absent in CTR_B breads. Functionalization of EXP_B-1 and EXP_B-2 breads was demonstrated by a 2.4- and 3.9-fold increase in Trolox equivalents, respectively. However, OOMW did not reduce post-prandial hyper-glycemia, as starch digestibility was similar between CTR_B and EXP_B breads. The sensory analysis, which focused solely on the visual, structural, and olfactory characteristics of the breads, excluding taste testing to prevent potential health risks from residual pesticides, showed a high appreciation for EXP_B-1 and EXP_B-2 breads, scoring higher than CTR_B in the overall assessment. Full article

Lamb waves offer a series of desirable features for Structural Health Monitoring (SHM) applications, such as the ability to detect small defects, allowing to detect damage at early stages of its evolution. On the downside, their propagation through media with multiple geometrical features results in complicated patterns, which complicate the task of damage detection, thus hindering the realization of their full potential. This is exacerbated by the fact that numerical models for Lamb waves, which could aid in both the prediction and interpretation of such patterns, are computationally expensive. The present paper provides a flexible surrogate to rapidly evaluate the sensor response in scenarios where Lamb waves propagate in plates that include multiple features or defects. To this end, an offline–online ray tracing approach is combined with Frequency Response Functions (FRFs) and transmissibility functions. Each ray is thereby represented either by a parametrized FRFs, if the origin of the ray lies in the actuator, or by a parametrized transmissibility function, if the origin of the ray lies in a feature. By exploiting the mechanical properties of propagating waves, it is possible to minimize the number of training simulations needed for the surrogate, thus avoiding the repeated evaluation of large models. The efficiency of the surrogate is demonstrated numerically, through an example, including different types of features, in particular through holes and notches, which result in both reflection and conversion of incident waves. For most sensor locations, the surrogate achieves an error between 1% and 4%, while providing a computational speedup of three to four orders of magnitude. Full article

Expected lives of mechanical parts and structures depend upon the environmental conditions, their dynamic behaviours and the task-oriented spectra of different loadings. This paper exploits contactless full-field mobilities, estimated by Scanner Laser Doppler Vibrometry (SLDV), in the real manufacturing, assembling and loading conditions of the thin plate tested, whose structural dynamics can be described in broad frequency bands, with no distorting inertia of sensors and no numerical models. The paper derives the mobilities into full-field strain Frequency Response Functions (FRFs), which map, by selecting the proper complex-valued broad frequency band excitation spectrum, the surface strains. From the latter, by means of the constitutive model, dynamic stress distributions are computed, to be exploited in fatigue spectral methods to map the expected life of the component, according to the selected tasks’ spectra and the excitation locations. The results of this experiment-based approach are thoroughly commented in sight of non-destructive-testing, damage and failure prognosis, Structural Health Monitoring, manufacturing and maintenance actions. Full article

Among damage imaging methods based on Lamb waves, the Minimum Variance Distortionless Response (MVDR) method adaptively calculates channel weights to suppress interference signals, improving imaging resolution and the signal-to-noise ratio (SNR). However, the MVDR method involves matrix inversion, which introduces a high computational burden to the implementation process and makes real-time damage detection challenging. We propose constructing a Convolutional-Neural-Network (CNN)-based network architecture based on the Delay-and-Sum (DAS) beamforming method. This architecture replaces the MVDR’s adaptive weight calculation by establishing a nonlinear mapping from multi-channel data to weighting factors, enabling efficient high-resolution Lamb wave damage imaging with an enhanced SNR. To verify the effectiveness and imaging performance of the CNN-based method, damage in an aluminum plate is imaged using both simulation and experimental methods. The imaging results are compared and analyzed against those of the DAS and MVDR methods. The results show that the proposed CNN-based adaptive Lamb wave beamforming method, which combines the advantages of a high resolution and signal-to-noise ratio, as well as rapid imaging, can provide reference and support for real-time Lamb-wave-based Structural Health Monitoring (SHM). Full article

The effective measurement method plays a vital role in the structural health monitoring (SHM) field, which provides accurate and real-time information concerning structural conditions and performance. The innovative measurement approach based on strain sensors, referred to as the inverse finite element method (iFEM), has been considered the most promising and versatile technology for meeting the requirements of the SHM system. However, the existing iFEM for shape sensing of thick plate structures has the drawback that the transverse shear effect makes no contribution to the three-dimensional deformation of thick plate structures. Therefore, this study proposed an enhanced inverse finite element method (iFEM) based on single-surface fiber Bragg grating strain sensors for reconstructing thick plate structures coupled with an analytical formulation. The method characterized the explicit relationship between transverse shear and bending displacement field on the mid-plane, which presents the sixth-order differential equation based on a variational approach. The three-dimensional deformation field can be obtained along the thickness direction, expanding the SHM application of iFEM for composite structures based on strain measurement. By performing shape sensing analysis of the thick plate model, the exactness and applicability of the present method are numerically and experimentally validated for different loading cases. Full article