Search Results (74)

This paper experimentally investigates the impact response of composite laminates made with conventional and bio-based epoxy resin. Drop tower impact tests were conducted at varying energy levels, including repeated low-energy impacts, to evaluate perforation resistance. The laminates’ residual strength and damage tolerance were assessed using the Damage Index (DI) and by analysing the resonance frequency variations through the Impulse Excitation Technique (IET). The study demonstrates a strong correlation between the DI and the resonance frequencies of the specimens, suggesting that IET can effectively track damage progression in composite laminates. Bio-based resin laminates exhibited higher energy absorption at perforation and lower damage progression during repeated impacts due to the higher ductility of the resin. This method of using resonance frequencies to assess impact damage progression directly in composite laminates throughout the IET technique has not been previously reported in the literature. Full article

Fusarium wilt disease severely constrains the global banana industry. The highly destructive disease is caused by Fusarium oxysporum f. sp. cubense, especially its virulent tropical race 4 (Foc TR4). Selenium (Se), a non-essential mineral nutrient in higher plants, is known to enhance plant resistance against several fungal pathogens. The experiments we conducted showed that selenium (≥10 mg/L) dramatically inhibited the growth of Foc TR4 mycelia and promoted plant growth. The further study we performed recorded a substantial reduction in the disease index (DI) of banana plants suffering from Foc TR4 when treated with selenium. The selenium treatments (20~160 mg/L) demonstrated significant control levels, with recorded symptom reductions ranging from 42.4% to 65.7% in both greenhouse and field trials. The DI was significantly negatively correlated with the total selenium content (TSe) in roots. Furthermore, selenium treatments enhanced the antioxidant enzyme activities of peroxidase (POD), polyphenol oxidase (PPO), and glutathione peroxidase (GSH-Px) in banana. After two applications of selenium (100 and 200 mg/plant) in the field, the TSe in banana pulps increased 23.7 to 25.9-fold and achieved the Se enrichment standard for food. The results demonstrate that selenium applications can safely augment root TSe levels, both reducing Fusarium wilt disease incidence and producing Se-enriched banana fruits. For the first time, this study has revealed that selenium can significantly reduce the damage caused by soil-borne pathogens in banana by increasing the activities of antioxidant enzymes and inhibiting fungal growth. Full article

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. Full article

Most humanitarian logistics research focuses on immediate response efforts, leaving a gap regarding the long-term socio-economic impacts of post-tragedy financial aid. Our research investigates the Brumadinho landslide tragedy in Minas Gerais, Brazil, analyzing the effectiveness of financial aid in fostering sustainable recovery and resilience in affected communities. We employ a hybrid multi-criteria decision-making (MCDM) and machine learning model to quantitatively assess the socio-economic impact on affected municipalities. Using social responsibility indices from official state government datasets and data from the PTR transparency initiative—a financial aid program determined by the Judicial Agreement for Full Reparation and operationalized by FGV Projetos, which allocates USD 840 million for the reparation of damages, negative impacts, and socio-environmental and socio-economic losses—our analysis covers all municipalities in Minas Gerais over 14 years (10 years before and 4 years after the tragedy). We determine a final socio-economic performance score using the max entropy hierarchical index (MEHI). Additionally, we assess the efficiency of the PTR financial aid in affected municipalities through examining MEHI changes before and after the transfers using a difference-in-differences (DiD) approach. Our findings reveal both direct and indirect impacts of the tragedy, the efficacy of financial aid distribution, and the interplay of various socio-economic factors influencing each municipality’s financial health. We propose policy recommendations for targeted and sustainable support for regions still coping with the long-term repercussions of the Brumadinho landslide. Full article

Orthotropic steel decks (OSDs) are commonly used in the construction of bridges due to their load-bearing capabilities. However, they are prone to fatigue damage over time due to the cyclic loads from vehicles. Therefore, the early structural health monitoring of fatigue damage in OSDs is crucial for ensuring bridge safety. Moreover, Lamb waves, as elastic waves propagating in OSD plate-like structures, are characterized by their long propagation distances and minimal attenuation. This paper introduces a method of emitting high-energy ultrasonic waves onto the OSD surface to capture the nonlinear Lamb waves formed, thereby calculating the nonlinear parameters. These parameters are then correlated with the fatigue damage endured, forming a damage index (DI) for monitoring the fatigue life of OSDs. Experimental results indicate that as fatigue damage increases, the nonlinear parameters exhibit a significant initial increase followed by a decrease. The behavior is distinct from the characteristic parameters of linear ultrasound (velocity and energy), which also exhibit changes but to a relatively smaller extent. The proposed DI and fatigue life based on nonlinear parameters can be fitted with a Gaussian curve, with the R-squared value of the fitting curve being close to 1. Additionally, this paper discusses the influence of rib welds within the OSDs on the DI, whereby as fatigue damage increases, it enlarges the value of the nonlinear parameters without altering their trend. The proposed method provides a more effective approach for monitoring early fatigue damage in OSDs. Full article

Biotic stress in cotton plants caused by the phytopathogenic fungus Colletotrichum gossypii var. cephalosporioides triggers symptoms of ramulosis, a disease characterized by necrotic spots on young leaves, followed by death of the affected branch’s apical meristem, plant growth paralysis, and stimulation of lateral bud production. Severe cases of ramulosis can cause up to 85% yield losses in cotton plantations. Currently, this disease is controlled exclusively by using fungicides. However, few studies have focused on biological alternatives for mitigating the effects of contamination by C. gossypii var. cephalosporioides on cotton plants. Thus, the hypothesis raised is that endophytic fungi isolated from an Arecaceae species (Butia purpurascens), endemic to the Cerrado biome, have the potential to reduce physiological damage caused by ramulosis, decreasing its severity in these plants. This hypothesis was tested using plants grown from seeds contaminated with the pathogen and inoculated with strains of Gibberella moniliformis (BP10EF), Hamigera insecticola (BP33EF), Codinaeopsis sp. (BP328EF), G. moniliformis (BP335EF), and Aspergillus sp. (BP340EF). C. gossypii var. cephalosporioides is a leaf pathogen; thus, the evaluations were focused on leaf parameters: gas exchange, chlorophyll a fluorescence, and oxidative metabolism. The hypothesis that inoculation with endophytic strains can mitigate physiological and photochemical damage caused by ramulosis in cotton was confirmed, as the fungi improved plant growth and stomatal index and density, increased net photosynthetic rate (A) and carboxylation efficiency (A/Ci), and decreased photochemical stress (ABS/RC and DI₀/RC) and oxidative stress by reducing enzyme activity (CAT, SOD, and APX) and the synthesis of malondialdehyde (MDA). Control plants developed leaves with a low adaxial stomatal index and density to reduce colonization of leaf tissues by C. gossypii var. cephalosporioides due to the absence of fungal antagonism. The Codinaeopsis sp. strain BP328EF can efficiently inhibit C. gossypii var. cephalosporioides in vitro (81.11% relative inhibition), improve gas exchange parameters, reduce photochemical stress of chlorophyll-a, and decrease lipid peroxidation in attacked leaves. Thus, BP328EF should be further evaluated for its potential effect as a biological alternative for enhancing the resistance of G. hirsutum plants and minimizing yield losses caused by C. gossypii var. cephalosporioides. Full article

Pavement distress detection is a crucial task when assessing pavement performance conditions. Here, a novel deep-learning method based on a transformer network, referred to as ISTD-DisNet, is proposed for multi-type pavement distress semantic segmentation. In this methodology, a mix transformer (MiT) based on a hierarchical transformer structure is chosen as the backbone to obtain multi-scale feature information on pavement distress, and a mixed attention module (MAM) is introduced at the decoding stage to capture the pavement distress features across different channels and spatial locations. A learnable transposed convolution upsampling module (TCUM) enhances the model’s ability to restore multi-scale distress details. Subsequently, a novel parameter—the distress pixel density ratio (PDR)—is introduced based on the segmentation results. Analyzing the intrinsic correlation between the PDR and the pavement condition index (PCI), a new pavement damage index prediction model is proposed. Finally, the experimental results reveal that the F1 and mIOU of the proposed method are 95.51% and 91.67%, respectively, and the segmentation performance is better than that of the other seven mainstream segmentation models. Further PCI prediction model validation experimental results also indicate that utilizing the PDR enables the quantitative evaluation of the pavement damage conditions for each assessment unit, holding promising engineering application potential. Full article

In an era marked by increasing demands for stability and durability in construction, the importance of damage detection in concrete structures cannot be overstated. As these structures underpin the safety and longevity of vital assets, this paper embarks on a comprehensive exploration of methodologies to enhance precision and reliability in 2D concrete plate damage detection. By focusing on the interpolation of damage index values and leveraging the insights gained from energy loss analysis and the characterization of the time of arrival of signals, we address the pressing need for improved non-destructive damage detection techniques. Our study encompasses a range of simulation attempts, each involving various interpolation parameters, and systematically evaluates their performance. The culmination of this research identifies the most effective combination of techniques and parameters, leading to the best results in damage detection. This multidimensional investigation promises to provide valuable contributions to the field of structural health monitoring, benefiting both researchers and practitioners engaged in the evaluation of concrete structures. Full article

This paper presents experimental and theoretical assessments of the structural behavior of circular steel fiber-reinforced concrete (SFRC) columns reinforced with glass fiber-reinforced polymer (GFRP) bars subjected to a concentric axial compressive load. Laboratory experiments were planned to evaluate and compare the effect of different design parameters on the structural behavior of column specimens based on experiments and finite element (FE) analysis. The experimental variables were (i) concrete types, i.e., conventional concrete (CC) and fiber-reinforced concrete (FC), (ii) longitudinal reinforcement types, i.e., steel and GFRP bars, and (iii) transverse rebar configurations, i.e., tied and spiral with different pitches. Sixteen column specimens were fabricated and categorized into four groups with respect to rebar configurations and concrete types. The results showed that the failure modes and cracking patterns of those four column groups were comparable, particularly in the pre-peak branches of load-deflection curves. Even though the average ultimate load of the columns with longitudinal GFRP bars was 17.9% less than that with longitudinal steel bars, the ductility index (DI) was 10.2% greater than their counterpart on average. The addition of steel fibers (SF) to concrete increased the axial peak load by up to 3.1% and the DI by up to 6.6% compared to their counterpart CC columns without SFs. The DI of specimens was increased by higher volumetric ratios (up to 12%) and spiral types (up to 5.5%). The concrete damage plastic (CDP) model for FC columns was updated in the finite element software ABAQUS 6.14. Finally, a new simple equation was theoretically proposed to predict the axial capacity of specimens by considering the inclusion of longitudinal GFRP rebars, volumetric ratio, and steel spiral/hoop ties. Good agreement between the proposed model predictions and the experimental/numerical results was observed. Full article

Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enables the rapid and precise localization of multiple damages in GFRP composite plates. Modal testing on damaged GFRP plates confirms the enhanced accuracy for combining the feedback DI with multi-label classification for pinpointing damage locations. Compared with traditional methods, this feedback DI method improves sensitivity, while multi-label classification effectively handles multiple damage scenarios, enhancing the overall efficiency of the damage diagnosis. The effectiveness of the proposed methods is validated through experimentation, offering robust theoretical support for composite plate damage diagnostics. Full article

Curvature-based damage detection has been previously applied to identify damage in concrete structures, but little attention has been given to the capacity of this method to identify distributed damage in multiple damage zones. This study aims to apply for the first time an enhanced existing method based on modal curvature analysis combined with wavelet transform curvature (WTC) to identify zones and highlight the damage zones of a beam made of ultra-high-performance fiber-reinforced concrete (UHPFRC), a construction material that is emerging worldwide for its outstanding performance and durability. First, three beams with a 2 m span of UHPFRC material were cast, and damaged zones were created by sawing. A reference beam without cracks was also cast. The free vibration responses were measured by 12 accelerometers and calculated by operational modal analysis. Moreover, for the sake of comparison, a finite element model (FEM) was also applied to two identical beams to generate numerical acceleration without noise. Second, the modal curvature was calculated for different modes for both experimental and FEM-simulated acceleration after applying cubic spline interpolation. Finally, two damage identification methods were considered: (i) the damage index (DI), based on averaging the quadratic difference of the local curvature with respect to the reference beam, and (ii) the WTC method, applied to the quadratic difference of the local curvature with respect the reference beam. The results indicate that the developed coupled modal curvature WTC method can better identify the damaged zones of UHPFRC beams. Full article

The corrosion of reinforced concrete (RC) is one of the most serious durability problems in civil engineering structures, and the corrosion detection of internal reinforcements is an important basis for structural durability assessment. In this paper, the appropriate frequency required to cause excitation signals in the specimen is first analyzed by means of frequency dispersion curves. Subsequently, the effectiveness of five damage indexes (DIs) is discussed using random corrosion in finite elements. Finally, guided ultrasonic wave (GUW) tests are conducted on reinforcement and RC specimens at different corrosion degrees, and the test results are verified using a theoretical corrosion model. The results show that the larger the covered thickness is at the same frequency, the higher the modal order of the GUW in the frequency dispersion curve is, and the smaller the group velocity is. The SAD is the most sensitive to the corrosion state of the reinforcement compared with the other DIs, and it shows a linear increasing trend with the increase in the corrosion degree of the reinforcement. The SAD values of the RC specimens showed a three-stage change with the increase in the corrosion time, and the time until the appearance of corrosion cracks was increased with the increase in the covered thickness. It can be seen that increasing the covered thickness is an effective method to delay the time until the appearance of corrosion cracks in RC specimens. Full article

In the 1990s, a sampling network for the biomonitoring of forests using epiphytic lichen diversity was established in the eastern Iberian Peninsula. This area registered air pollution impacts by winds from the Andorra thermal power plant, as well as from photo-oxidants and nitrogen depositions from local and long-distance transport. In 1997, an assessment of the state of lichen communities was carried out by calculating the Index of Atmospheric Purity. In addition, visible symptoms of morphological injury were recorded in nine macrolichens pre-selected by the speed of symptom evolution and their wide distribution in the territory. The thermal power plant has been closed and inactive since 2020. During 2022, almost 25 years later, seven stations of this previously established biomonitoring were revaluated. To compare the results obtained in 1997 and 2022, the same methodology was used, and data from air quality stations were included. We tested if, by integrating innovative methodologies (NIRS) into biomonitoring tools, it is possible to render an integrated response. The results displayed a general decrease in biodiversity in several of the sampling plots and a generalised increase in damage symptoms in the target lichen species studied in 1997, which seem to be the consequence of a multifactorial response. Full article

Fatigue and musculoskeletal pain are also frequent in patients with psoriasis (PsO) without arthritis (PsA). The current study aimed to assess the impact of an intervention program based on aerobic training to reduce fatigue and musculoskeletal pain in patients with PsO without PsA. A total of 118 male patients with PsO volunteered in the current interventional study and were randomly allocated to the experimental (n = 59) or control group (n = 59). The intervention consisted of a 16-week aerobic training program on a treadmill, three sessions per week, consisting of a warm-up, 35–50 min treadmill exercise (increasing 5 min/4 weeks) at a work intensity of 50–65% of peak heart-rate (increasing 5%/4 weeks), and cooling-down. The functional assessment of chronic illness therapy fatigue scale (FACIT-Fatigue), health assessment questionnaire disability index (HAQ-DI), and visual analog scale (VAS) were compared pre and post intervention. Nutritional intake, maximal aerobic power, lipid profile, serum markers of muscle damage, and body composition were also assessed. When compared to baseline, FACIT-Fatigue, HAQ-DI, and VAS scores were significantly improved without increasing markers of muscle damage. Fat mass percentage, lipid profile, and maximal oxygen consumption were also improved. In conclusion, a 16-week aerobic training program at moderate intensity was safe, well tolerated, and effective in psoriatic patients without PsA. Long-term follow-up studies are required to examine whether these promising results may improve clinical outcomes. Full article

Complete blood count-derived ratios have been described as inflammatory biomarkers in several diseases. These hematological scores include the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index ([SIRI]; neutrophils × monocytes/lymphocytes). Our aim was to study how these biomarkers are related to disease expression in a large and well-characterized series of patients with systemic lupus erythematosus (SLE). A total of 284 SLE patients and 181 age- and sex-matched healthy controls were recruited. The NLR, MLR, PLR, and SIRI were calculated, and activity (SLEDAI-2K), severity (Katz), and damage index (SLICC-DI) scores were assessed in patients with SLE. Multivariable linear regression analysis was performed to study whether these scores differ between patients and controls and how they are related to clinical and laboratory features of the disease. Crude cell counts of neutrophils, monocytes, lymphocytes, and platelets were lower in SLE patients compared to controls. Despite this, NLR, MLR, and PRL, but not SIRI, were higher in SLE patients than in controls after multivariable analysis. However, the relationship between the different scores and disease characteristics was limited. Only the Katz severity index revealed a significant positive relationship with SIRI, NLR, and MLR after adjustment for covariates. Similarly, alternative complement cascade activation and low C3 were significantly associated with higher NLR, MLR, and PLR. In conclusion, although cytopenias are a common feature of patients with SLE, hematologic composite scores are independently higher in this population compared to controls. However, the relationship of these scores with the characteristics of the disease is scarce, with the relationship with the complement system being the most consistent. Full article