Structural Damage Research Articles

Tannic Acid Selective Modulation Defects to Enhance the Photocatalytic CO2 Reduction Activity of Layered Double Hydroxides.

Recently, layered double hydroxides (LDH) have shown great potential in photoreduction of CO2 owing to its flexible structural adjustability. In this study, the mild acidic property of tannic acid (TA) is exploited to etch the bimetal LDH to create abundant vacancies to gain the coordination unsaturated active centers. Based on the different chelating abilities of TA to various metal ions, the active metals are remained by selective chelation while the inert metals are removed during the etching process of bimetal LDH. Furthermore, selective chelating with metal ions not only increases the percentage of highly active metals but also compensates for the structural damage caused by the etch, which achieves a scalpel-like selective construction of vacancies. The NiAl-LDH etched and functionalized by TA for 3h exhibits superior photo-reduction of CO2 performance without co-catalysts and photo-sensitizers, which is 14 times that of the pristine NiAl-LDH. The fact that many bimetal LDHs can be functionalized by TA and exhibit significantly improved photocatalytic efficiency is confirmed, suggesting this strategy is generalized to functionalize double- or multi-metal LDH. The method provided in this work opens the door for polyphenol-functionalized LDHs to enhance their ability for light-driven chemical transformations.

Vibration monitoring of masonry bridges to assess damage under changing temperature

Structural Health Monitoring (SHM) is of utmost importance for the preservation and safe operation of historical arch bridges. This paper presents the development of a SHM strategy aimed at the model-based damage assessment of masonry bridges using frequency data. Structural damage induces natural frequency changes that are strictly related to the damage location. Consequently, a numerical model capable of reproducing the intact dynamic characteristics should allow to simulate damage scenarios, including the observed one, with the anomaly localisation being performed through the similarity between the experimentally detected frequency changes and the numerically simulated ones. The proposed methodology is based on the availability of an appropriate knowledge of the investigated structure, allowing to define a Finite Element (FE) model that accurately reproduces the system dynamic characteristics. Hence, the SHM strategy involves: (a) the use of the calibrated model to simulate different damage scenarios, so that a Damage Location Reference Matrix (DLRM) is defined through the associated frequency shifts; (b) the damage detection through statistical pattern recognition of vibration data; (c) the damage localisation through the comparison between the identified frequency changes and those defined in the DLRM matrix. Pseudo-experimental monitoring data, referring to a historical masonry viaduct, were generated and used to exemplify the reliability and accuracy of the developed algorithms in detecting and localizing damage.

The corticospinal tract in multiple sclerosis: correlation between cortical excitability and magnetic resonance imaging measures.

Multiple sclerosis (MS) is a central nervous system disease involving gray and white matters. Transcranial magnetic stimulation (TMS) and magnetic resonance imaging (MRI) could help identify potential markers of disease evolution, disability, and treatment response. This work evaluates the relationship between intracortical inhibition and facilitation, motor cortex lesions, and corticospinal tract (CST) integrity. Consecutive adult patients with progressive MS were included. Sociodemographic and clinical data were collected. MRI was acquired to assess primary motor cortex lesions (double inversion and phase-sensitive inversion recovery) and CST integrity (diffusion tensor imaging). TMS outcomes were obtained: motor evoked potentials (MEP) latency, resting motor threshold, short-interval intracortical facilitation (ICF) and inhibition. Correlation analysis was performed. Twenty-five patients completed the study (13 females, age: 55.60 ± 11.49 years, Expanded Disability Status Score: 6.00 ± 1.25). Inverse correlations were found between ICF mean and each of CST radial diffusivity (RD) (ρ =-0.56; p < 0.01), CST apparent diffusion coefficient (ADC) (ρ=-0.44; p = 0.03), and disease duration (ρ=-0.46; p = 0.02). MEP latencies were directly correlated with disability scores (ρ = 0.55; p < 0.01). High ADC/RD and low ICF have been previously reported in patients with MS. While the former could reflect structural damage of the CST, the latter could hint towards an aberrant synaptic transmission as well as a depletion of facilitatory compensatory mechanisms that helps overcoming functional decline. The findings suggest concomitant structural and functional abnormalities at later disease stages that would be accompanied with a heightened disability. The results should be interpreted with caution mainly because of the small sample size that precludes further comparisons (e.g., treated vs. untreated patients, primary vs. secondary progressive MS). The role of these outcomes as potential MS biomarkers merit to be further explored.

Structural design and investigation of Ti3C2 MXene as a conductive interlayer for improving the lithium-storage performance of PSi@C anode material

Silicon stands out as an ideal anode material for the next generation of lithium-ion batteries (LIBs) due to its abundant sources, low lithiation/delithiation potential, and high specific capacity. However, its practical application is impeded by significant volume expansion, leading to electrode structure damage. In this study, the Porous silicon(PSi)@C/Ti3C2 MXene composite was developed by dispersing porous micro-silicon@carbon (PSi@C) particles into layered stackable Ti3C2 MXene sheets using ultrasonic and freeze drying. The Ti3C2 MXene interlayer played a crucial role in enhancing the conductive crosslinking network between PSi@C particles, and providing efficient channels for electron transport/ion diffusion. Additionally, the Ti3C2 MXene interlayer served as a buffer to accommodate the substantial volume changes in silicon during electrochemical cycling. Consequently, the PSi@C/Ti3C2 MXene composite electrode demonstrated rapid electron/ion conduction and maintained structural stability. Remarkably, the electrode exhibited outstanding long cycle stability with 952 mAh g-1 at 0.5 A g-1 after 200 cycles and excellent rate performance with 542 mAh g-1 at 2 A g-1.

Computational Analysis of Stiffness Reduction Effects on the Dynamic Behaviour of Floating Offshore Wind Turbine Blades

This paper describes the study of a floating offshore wind turbine (FOWT) blade in terms of its dynamic response due to structural damage and its repercussions on structural health monitoring (SHM) systems. Using a finite element model, natural frequencies and mode shapes were derived for both an undamaged and a damaged blade configuration. A 35% reduction in stiffness at node 1 was applied in order to simulate significant damage. Concretely, the results are that the intact blade has a fundamental frequency of 0.16 Hz, and this does not change when damaged, while higher modes exhibit frequency changes: mode 2 drops from 2.05 Hz to 2.00 Hz and mode 3 from 6.15 Hz to 6.01 Hz. The shifts show a critical loss in the capability of handling vibrational energy due to the damage; higher modes (4, 5, and 6) show larger frequency deviations going down to as low as 18.06 Hz in mode 6. The mode shape change is considerable for the edge-wise and flap-wise deflection of the 2D contour plots, indicating possible coupling effects between modes. These results indicate that lower modes are sensitive to stiffness reductions, and the continuous monitoring of the lower harmonic modes early is required to detect damages. These studies have helped to improve blade design, maintenance, and operational safety for FOWT systems.

Nanotherapy for Cancer and Biological Activities of Green Synthesized AgNPs Using Aqueous Saussurea costus Leaves and Roots Extracts.

Background/Objectives: Nanoparticles derived from medicinal plants are gaining attention for their diverse biological activities and potential therapeutic applications. Methods: This study explored the antioxidant, anti-inflammatory, anti-tumoral, and antimicrobial properties of green synthesized silver nanoparticles (AgNPs) using the aqueous leaf and root extracts of Saussurea costus (S. costus). The physicochemical characterizations of both biosynthesized AgNPs using the aqueous leaf extract (L-AgNPs) and root extract (R-AgNPs) were examined using UV spectroscopy, fluorescence spectroscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, dynamic light scattering, and Fourier-transform infrared spectroscopy. The antioxidant activity measured using ABTS, DPPH, and FRAP assays showed that AgNPs, particularly from roots, had higher activity than aqueous extracts, attributed to phenolic compounds acting as capping and antioxidant agents. Results: Enzyme inhibition studies indicated that AgNPs exhibited remarkable anti-inflammatory effects, inhibiting COX-1, 5-LOX, and secreted PLA2 enzymes by over 99% at 120 µg/mL, comparable to standard drugs. The anti-tumoral effects were evaluated on the human cancer cell lines HCT-116, LoVo, and MDA-MB-231, with AgNPs inhibiting cell proliferation dose-dependently and IC50 values between 42 and 60 µg/mL, demonstrating greater potency than extracts. The AgNPs also showed enhanced antimicrobial activities against various microbial strains, with IC50 values as low as 14 µg/mL, which could be linked to nanoparticle interactions with microbial cell membranes, causing structural damage and cell death. Conclusions: These findings suggest that S. costus-derived AgNPs are promising natural, biodegradable agents for various biological applications and potential new therapeutic agents, necessitating further research to explore their mechanisms and applications.

Structural response of steel-concrete composite panels to near field simultaneous blast and fragmentation loading

Steel-concrete composite sandwich panels are used in blast doors or blast walls to protect personnel and equipment in explosive environments due to their superior performance against blast effects. In the design of such panels, most design methods treat blast and fragment loading independently for far-field explosions. However, the synergistic effects of combined blast and fragments loading should be accounted for in close-in explosion scenarios. In this study, a field test program was conducted to assess the damage of the steel-concrete composite panels subjected to close-in explosion of cased charges at various scaled distance between 0.39 and 0.78 m/kg1/3. Characterization test of cased charge detonation was performed to explore the combined loading effect. The analysis includes the pressure-time history from the cased charge detonation, distribution of the fragment masses and velocities over the test panels. The structural damage and response of the composite panels against the combined loading effects was measured. The results indicated that in addition to the damage from the blast wave, the panel was remarkably damaged by the fragment impact. Despite the significant structural damage, the panels maintained its structural integrity after the tests. Additionally, quasi-static load tests were conducted on the panels to quantify their load resistant differences in pristine condition and various damaged conditions due to the explosive effects.

Radiology of bronchiectasis

Bronchiectasis is an irreversible bronchial dilatation. It is chronically progressive through avicious circle of secretion retention, infection, inflammation and structural damage. The underlying causes are diverse and the severity of the disease is variable, which makes both the diagnostics and treatment challenging. Computed tomography (CT) is the gold standard in the diagnosis of bronchiectasis and can be helpful in clarifying the etiology. The type of bronchiectasis, the distribution of the bronchiectasis within the lungs and associated findings are particularly relevant. Imaging is also important in monitoring the progression of bronchiectasis. In the usual report of the findings this is carried out visually and descriptively, while semiquantitative scores and computer-aided quantitative analysis of the respiratory tract enable amore precise assessment and are used in particular for clinical studies.

Integrated Estimation of Stress and Damage in Concrete Structure Using 2D Convolutional Neural Network Model Learned Impedance Responses of Capsule-like Smart Aggregate Sensor.

Stress and damage estimation is essential to ensure the safety and performance of concrete structures. The capsule-like smart aggregate (CSA) technique has demonstrated its potential for detecting early-stage internal damage. In this study, a 2 dimensional convolutional neural network (2D CNN) model that learned the EMI responses of a CSA sensor to integrally estimate stress and damage in concrete structures is proposed. Firstly, the overall scheme of this study is described. The CSA-based EMI damage technique method is theoretically presented by describing the behaviors of a CSA sensor embedded in a concrete structure under compressive loadings. The 2D CNN model is designed to learn and extract damage-sensitive features from a CSA's EMI responses to estimate stress and identify damage levels in a concrete structure. Secondly, a compression experiment on a CSA-embedded concrete cylinder is carried out, and the stress-damage EMI responses of a cylinder are recorded under different applied stress levels. Finally, the feasibility of the developed model is further investigated under the effect of noises and untrained data cases. The obtained results indicate that the developed 2D CNN model can simultaneously estimate stress and damage status in the concrete structure.

Scutellarin alleviated ulcerative colitis through gut microbiota-mediated cAMP/PKA/NF-κB pathway

PurposeUlcerative colitis (UC) is a chronic, non-specific inflammatory condition of the colon, characterized by recurrent episodes and a notable lack of effective pharmacological treatments. Scutellarin, a natural component, exhibits appreciable pharmacological effects and therapeutic potential for various diseases. However, its effects on UC are not fully understood, and the precise mechanisms remain to be deciphered. This study aimed to assess the therapeutic efficacy of scutellarin and elucidate its underlying mechanisms in treating UC. MethodsThis study utilized dextran sulfate sodium (DSS)-induced mice to evaluate the therapeutic potential of scutellarin against UC and to elucidate the mechanisms involving the gut microbiota. An antibiotics cocktail (ABX) and fecal microbiota transplantation (FMT) were also used to determine the mechanistic role of the gut microbiota. An integrative approach combining fecal metabolomics and network pharmacology analysis was used to explore the gut microbiota-directed molecular mechanism. ResultsThe results showed that scutellarin provided various therapeutic benefits in UC management, including alleviating weight loss, slowing disease progression, and reducing inflammatory damage in colon structures. The improved gut microbiota after scutellarin administration contributed to these effects. Fecal metabolome revealed that scutellarin selectively mitigated DSS-induced dysregulation of gut microbiota-derived metabolites, including glycolic acid, γ-aminobutyric acid, glutamate, tryptophan, xanthine, and β-hydroxypyruvate. Network pharmacology analysis, along with in vivo experimental verification, implicated the cAMP/PKA/NF-κB pathway in the action of these metabolites in treating UC, which may be the mechanism responsible for scutellarin's curative effects on UC. ConclusionThis study demonstrates the potential of scutellarin in alleviating UC by activating the cAMP/PKA/NF-κB pathway through gut microbiota-derived metabolites, highlighting scutellarin as a promising therapeutic agent for UC.

Study on Fractal Damage of Concrete Cracks Based on U-Net

The damage degree of a reinforced concrete structure is closely related to the generation and expansion of cracks. However, the traditional damage assessment methods of reinforced concrete structures have defects, including low efficiency of crack detection, low accuracy of crack extraction, and dependence on the experience of inspectors to evaluate the damage of structures. Because of the above problems, this paper proposes a damage assessment method for concrete members combining the U-Net convolutional neural network and crack fractal features. Firstly, the collected test crack images are input into U-Net for segmenting and extracting the output cracks. The damage to the concrete structure is then classified into four empirical levels according to the damage index (DI). Subsequently, a linear regression equation is constructed between the fractal dimension (D) of the cracks and the damage index (DI) of the reinforced concrete members. The damage assessment is then performed by predicting the damage index using linear regression. The method was subsequently employed to predict the damage level of a reinforced concrete shear wall–beam combination specimen, which was then compared with the actual damage level. The results demonstrate that the damage assessment method for concrete members proposed in this study is capable of effectively identifying the damage degree of the concrete members, indicating that the method is both robust and generalizable.

Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections

AbstractTraditional methods of detecting concrete damage, such as manual inspection, are typically slow, labor‐intensive, and subjective. Integrating deep learning algorithms has automated this process, representing a significant advance in building damage inspection. Semantic segmentation, a technique within deep learning, is increasingly recognized for its capability to identify the location and shape of concrete damage accurately. Unlike basic deep learning approaches like classification and object detection, semantic segmentation not only recognizes damage but also delineates its boundaries, and has great potential in facilitating dimension measurement. However, multi‐level, high‐precision detection for various structural damage assessments remains an area requiring further research. This study created a database of reinforced concrete surfaces with pixel‐level, multi‐category semantic segmentation annotations for various levels of component damage, including cracks and spalling of concrete, exposure, buckling, fracture of reinforcing bars, etc. The performance of advanced deep learning segmentation algorithms, including U‐Net, DeepLab, K‐Net, and FastSCNN, was consequently trained and evaluated in detecting various types of concrete damage. All models show good accuracy of more than 98%, but lower F1 scores around 70%. U‐Net and K‐Net demonstrate relatively stable performance, indicating a certain degree of consistency and a high‐performance peak. In contrast, DeepLabv3's F1 score fluctuates significantly, suggesting the model may suffer from overfitting or other stability issues during training, resulting in a relatively low average performance. FastSCNN exhibits the highest potential, achieving the highest F1 score. In addition, this study also tested the performance of each model on new damage images and explored the impact of the dataset proportion ratio (training set vs. validation set), contributing to providing insights into each algorithm's suitability for various inspection scenarios. Finally, perspectives on current challenges and future directions in the field have also been given.

Relationship Between Ultrasound and Physical Examination in the Assessment of Enthesitis in Patients With Spondyloarthritis: Results From the DEUS Multicenter Study.

The study objectives were (i) to explore the agreement between the Outcome Measures in Rheumatology (OMERACT) ultrasound lesions of enthesitis and physical examination in assessing enthesitis in patients with spondyloarthritis (SpA) and (ii) to investigate the prevalence and clinical relevance of subclinical enthesitis in this population. Twenty rheumatology centers participated in this cross-sectional study. Patients with SpA, including axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA), underwent both ultrasound scan and physical examination of large lower limb entheses. The OMERACT ultrasound lesions of enthesitis were considered, along with a recently proposed definition for "active enthesitis" by our group. Subclinical enthesitis was defined as the presence of "active enthesitis" in ≥1 enthesis in patients with SpA without clinical enthesitis (ie, number of positive entheses on physical examination and Leeds Enthesitis Index score = 0). A total of 4,130 entheses in 413 patients with SpA (224 with axSpA and 189 with PsA) were evaluated through ultrasound and physical examination. Agreement between ultrasound and physical examination ranged from moderate (ie, enthesophytes) to almost perfect (ie, power Doppler and "active enthesitis"). Patellar tendon entheses demonstrated the highest agreement, whereas Achilles tendon insertion showed the lowest. Among 158 (38.3%) of 413 patients with SpA with clinical enthesitis, 108 (68.4%) exhibited no "active enthesitis" on ultrasound. Conversely, of those 255 without clinical enthesitis, 39 (15.3%) showed subclinical enthesitis. Subclinical enthesitis was strongly associated with local structural damage. However, no differences were observed regarding the demographic and clinical profiles of patients with SpA with and without subclinical enthesitis. Our study underscores the need for a comprehensive tool integrating ultrasound and physical examination for assessing enthesitis in patients with SpA.

Exploring the potential of rigid polyurethane foam waste in structural lightweight concrete

The structural lightweight concrete is gaining significant interest in the research community due to its reduced dead load. The reduction in the self-weight not only lowers the gravity load but can also lower the seismic demands for the structural members and hence can potentially reduce the overall cost. This study examines the feasibility of developing a sustainable structural lightweight concrete by utilizing rigid polyurethane foam waste as coarse aggregates (5–10 mm). The silica fume was incorporated as a cement replacement to enhance the compressive strength of the mix. A total of three composite mix formulations consisting of polyurethane foam, cement-coated polyurethane foam, and pumice (control specimens) as coarse aggregates were tested. The testing was performed for workability, compressive strength, flexural strength, elastic modulus, absorption, permeable voids, chloride-ion penetration, freeze and thaw resistance, and drying shrinkage. The results achieved for elastic modulus, chloride ion penetrability, and drying shrinkage, were meeting the minimum requirements of the standards making this composite suitable for the structural application. The results revealed that uncoated polyurethane based lightweight aggregate concrete resulted in an air-dry density of 1829 kg/m3 with the corresponding compressive strength of 19.5 MPa and flexural strength of 2.78 MPa at 28 days. Besides, the polyurethane-based lightweight concrete also showed lower internal structure damage against freeze and thaw action, along with improved thermal conductivity.

Preparation, physicochemical characterization, ex vivo, and in vivo evaluations of asiatic acid-loaded solid lipid nanoparticles formulated with natural waxes for nose-to-brain delivery

Asiatic acid (AA) has neuroprotective potential for prevention and treatment of Alzheimer's disease. Natural waxes with various ratios of Tween 80 and Span 80 or soybean lecithin were formulated to obtain AA-loaded solid lipid nanoparticles (AA-SLN) to improve direct nose to brain transport. Optimal AA-SLN had particle size below 200 nm with uniform size distribution and zeta potential of nearly -30 mV indicating a low risk of particle aggregation. Formulation with rice bran wax, Tween 80, and soybean lecithin (AA-RwS100) showed the highest entrapment efficiency and yield of >98 % while in vitro AA release of AA-SLN was linearly up to 48 h For ex vivo permeation, confocal laser scanning microscopy (CLSM) and histopathological studies on porcine olfactory mucosa (OM) and respiratory mucosa (RM), AA-SLN showed significantly higher permeation across OM than RM (p < 0.05) up to 6 h and AA-RwS100 also showed the highest amount of drug permeated as confirmed by CLSM results. Although AA-SLN showed non-significantly lower permeation than AA solution (AA-SOL) (p > 0.05), no epithelial and mucosal structure damages were observed in OM treated with AA-RwS100 and RM treated with all AA-SLNs indicating safety for nasal administration while AA-SOL showed significant damage to both OM and RM. In addition, in vivo brain distribution study by fluorescence imaging using Rhodamine (R6g) showed higher brain distribution after intranasal administration of R6g-loaded solid lipid nanoparticles (R6g-SLN) than R6g solution (R6g-SOL) and intravenous administration of R6g-SLN, and R6g-RwS100 also showed the highest brain accumulation at 8 h post administration.

Splice variant of retinal G-protein-coupled receptor deletion (RGR-d)-mediated dysregulation of autophagy increases the susceptibility to age-related macular degeneration-like defects.

The splice variant of retinal G-protein-coupled receptor deletion (RGR-d) is a persistent component of drusen and may be involved in the pathogenesis of dry age-related macular degeneration (AMD). Increasing evidence has demonstrated the critical role of autophagy in AMD. In this study, we investigated whether RGR-d disrupts autophagy in early dry AMD in vivo and in vitro. Fundus imaging and fluoroscopy were performed on RGR-d mice created by multiplex gene editing. The retina microstructure was evaluated by performing hematoxylin and eosin (H&amp;E) staining as well as transmission electron microscopy (TEM). Retinal function was assessed by full-field electroretinography (ERG). After lentivirus transfection and stimulation, the permeability, phagocytosis, and tight junctions of ARPE-19 cells were evaluated. Western blotting of ATG5, beclin-1, LC3II/I, and P62 was performed to detect the changes in autophagy pathways. Atrophy and patchy penetrating hyperfluorescent foci, consistent with early AMD-like defects, were observed in the fundus of 12-month-old RGR-d mice. H&amp;E staining of retinal tissues indicated thinning of each layer of the retinal structure. H&amp;E staining of retinal tissues indicated thinning of each layer of the retinal structure. TEM analysis showed some diffuse granular deposits. And the morphology of choroidal microvascular endothelial cells was degraded and distorted. The morphology of the photoreceptor outer segments showed structural damage and Bruch's membrane was thickened. ERG indicated that the photoreceptor of RGR-d mice were dysfunctional. Changes in autophagy-related protein expression were observed in the retinal pigment epithelium and retinal neuroepithelium, and autophagy regulation was decreased. Palmitic acid (PA) stimulation caused permeability, phagocytosis, and tight junction dysfunction in cells overexpressing RGR-d. Beclin-1 and LC3II/I expression levels were significantly decreased and that of P62 was elevated in RGR-d cells after PA stimulation. RGR-d disrupts the autophagy pathway, causing the development of an early AMD-like pathophysiology.

Research on the mechanism of impact of vibrational loads on the bearing capacity of drilling surface conductor

The surface conductor is the first structural pipe in the development of deep-water oil and gas resources, bearing the top load and suspending various casing layers. Vibrational loads can cause soil structural damage and increase pore pressure, reducing the bearing capacity of the surface conductor and threatening the safety and stability of the subsea wellhead. Based on the vertical force analysis of the surface conductor and considering the impact of vibrational loads on soil strength, a model for the vertical bearing capacity of the surface conductor was established. Utilizing the dynamic Winkler model for pile foundation horizontal dynamic response, the surface conductor was simplified as a bending beam subjected to harmonic vibration, and a control equation for lateral deformation of the surface conductor was established. The effects of vibration load amplitude and frequency on the vertical bearing capacity of surface conductors were analyzed through simulation experiments, resulting in an equivalent bearing capacity coefficient for surface conductors ranging from 0.88 to 0.97. Combining the engineering data from a deep-water block in the South China Sea, the reliability of the theoretical calculation model was validated. The analysis indicates that the maximum bending moment of the surface conductor is approximately 6m below the mudline; low-frequency(0.05Hz–0.2Hz) vibrational loads can reduce the ultimate bearing capacity of the surface conductor by 3%–11%, with the effect gradually diminishing over time. This research provides a theoretical basis for the design of surface conductor in deep-water oil and gas wells.

Damage distribution of composite structures of a certain type of aircraft

Aiming at the damage and repair of aircraft composite structures, the applications and damage of composite structures in a certain type of aircraft, including the wing, fuselage and other components, were counted. Solid laminates (integral panels) and sandwich laminates are typical composite structural forms in the aircraft. Structural damage distribution models with time variable were established, and then the damage distributions of the structures were fitted and tested by structural methods. The results show that the quantitative proportions of damage are 75% for skin delamination, 20% for stringer delamination and 4% for stringer debonding. Lognormal distribution, Weibull distribution and Gamma distribution can all be used to fit the geometric parameter distributions of the three types of structural damage, in which the Lognormal distribution model has the best fitting effect. The distribution of structural damage geometric parameters of one aircraft does not vary with time, and the distributions of geometric parameters of the structural damage between the same type aircraft are also highly similar, which can be expressed in a unified function form.

Fatigue Damage Monitoring of Composite Structures Based on Lamb Wave Propagation and Multi-Feature Fusion

To address the challenges associated with fatigue damage monitoring in load-bearing composite structures, we developed a method that utilizes Lamb wave propagation and partial least squares regression (PLSR) for effective monitoring. Initially, we extracted diverse characteristics from both the time and frequency domains of the Lamb wave signal to capture the essence of the damage. Subsequently, we constructed a PLSR model, leveraging Lamb wave multi-feature fusion, specifically tailored for in-service fatigue damage monitoring. The efficacy of our proposed approach in quantitatively monitoring fatigue damage was thoroughly validated through rigorous standard fatigue tests. In practical applications, our model effectively mitigated the impact of multicollinearity among feature variables on model accuracy. Furthermore, the PLSR model demonstrated superior accuracy compared to the PCR model, given an equal number of principal components. To strike a harmonious balance between efficiency and precision, we optimized the size of the feature variable. The results show that the optimized PLSR model achieved an R-squared value exceeding 97% in predicting the in-service damage area. This underscores the robustness and reliability of our method in accurately monitoring fatigue damage in load-bearing composite structures.

Effectiveness of Generative AI for Post-Earthquake Damage Assessment

After an earthquake, rapid assessment of building damage is crucial for emergency response, reconstruction planning, and public safety. This study evaluates the performance of various Generative Artificial Intelligence (GAI) models in analyzing post-earthquake images to classify structural damage according to the EMS-98 scale, ranging from minor damage to total destruction. Correct classification rates for masonry buildings varied from 28.6% to 64.3%, with mean damage grade errors between 0.50 and 0.79, while for reinforced concrete buildings, rates ranged from 37.5% to 75.0%, with errors between 0.50 and 0.88. Fine-tuning these models could substantially improve accuracy. The practical implications are significant: integrating accurate GAI models into disaster response protocols can drastically reduce the time and resources required for damage assessment compared to traditional methods. This acceleration enables emergency services to make faster, data-driven decisions, optimize resource allocation, and potentially save lives. Furthermore, the widespread adoption of GAI models can enhance resilience planning by providing valuable data for future infrastructure improvements. The results of this work demonstrate the promise of GAI models for rapid, automated, and precise damage evaluation, underscoring their potential as invaluable tools for engineers, policymakers, and emergency responders in post-earthquake scenarios.