Search Results (151)

The displacement of bridge towers is relatively large under strong wind action. Changes in tower displacement can reflect the usage status of the bridge towers. Therefore, it is necessary to conduct performance warning research on tower displacement under strong wind action. In this paper, the triple standard deviation method, multiple linear regression method, and interpolation method are used to preprocess monitoring data with skipped points and missing anomalies. An improved multi-rate data fusion method, validated using simulated datasets, was applied to correct monitoring data at bridge tower tops. The fused data were used to feed predictive models and generate structural performance alerts. Spectral analysis confirmed that the fused displacement measurements achieve high precision by effectively merging the low-frequency GPS signal with the high-frequency accelerometer signal. Structural integrity monitoring of wind-loaded bridge towers used modeling residuals as alert triggers. The efficacy of this proactive monitoring strategy has been quantitatively validated through statistical evaluation of alarm accuracy rates. Full article

The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events. Full article

A real-time structural health monitoring (SHM) system of an airplane composite wing with adjustable damage is reported, where testing under realistic flight conditions is carried out in the controllable and repeatable environment of an industrial wind tunnel. An FBG-based sensing array monitors a debonded region, whose compromised structural strength is regained by a set of lockable fasteners. Damage tunability is achieved by loosening some of or all these fasteners. Real-time analysis of the data collected involves Principal Component Analysis, followed by Hotelling’s T-squared and Q measures. With previously set criteria, real-time data collection and processing software can declare the structural health status as normal or abnormal. During testing, the system using the Q measure successfully identified the initiation of the damage and its extent, while the T-squared one returned limited outcomes. Full article

Cable-stayed bridge cables experience significant tension over time, making the bridge cables prone to corrosion and fatigue. The direct measurement of cable length is not a standard capability in most current structural health monitoring systems, nor is long-term monitoring of cable changes. Bridge displacements are caused by both dynamic loads (wind and traffic) and quasi-static factors, primarily temperature. This study filtered out dynamic responses by the three-sigma rule, multiple linear regression, interpolation method, and not-a-number calibration. Monitoring data were used to analyze the bridge’s thermal field distribution and the time-dependent variation of tower displacements. Correlation analysis revealed a strong linear correlation between air temperature and quasi-static tower-girder displacements. This research proposes to use the tower-girder distance (effective cable length) to represent the length of the cable, take the thermal expansion coefficient of the effective length of the cable as the quantitative index for long-term monitoring, and take its error as the performance early warning indicator. This method effectively monitors cable health and provides damage warnings. 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 article presents the findings of an experimental study conducted on a vertical axis wind turbine (VAWT) tower instrumented with cascaded fiber Bragg grating (FBG) sensors to detect bending deformations. Structural health monitoring (SHM) is an essential need in the industry to reduce costs and maintenance time, and to prevent machine failures. First, FBG strain sensors were glued vertically along the tower to investigate the sensors behavior as a function of their height. The maximum signal-to-noise ratio is obtained when FBGs are placed at the tower base. Then, four packages were installed inside the tower, at the base, according to four cardinal directions. Each package contains an FBG strain sensor, and an extra temperature FBG for discrimination. The use of easy-to-deploy packages is a must for industrial installations. Afterwards, by using power spectral density (PSD) on the strain signals, three sources of tower oscillations are discovered: wind force, structure unbalance, and 1st tower mode resonance, each with its intrinsic frequency. Wind force and structure unbalance cause mechanical stresses at a frequency proportional to the wind turbine rotational speed, while the 1st tower mode frequency depends only on the machine geometry, regardless of the rotational speed. This study also analyzes the deformation amplitude for different rotational rates within the VAWT operational range (10–35 rpm). The resonance amplitude depends on the proximity of the rotational rate to the resonant frequency (22 rpm) and the duration at that rate. For structure unbalance, the oscillation amplitude increases with the rotational rate, due to the centrifugal effect. It is supposed that wind force deformation amplitude naturally depends on wind speed, which is unpredictable at a given precise time. The results of our experimental observations are very valuable for both the wind turbine manufacturer and owner. Full article

Air pollution is one of the most serious environmental threats, with particulate matter PM₁₀ and PM_2.5 representing its most harmful components, significantly affecting public health. These particles are primarily generated by transport, industry, residential heating, and agriculture, and are associated with increased incidence of respiratory and cardiovascular diseases, asthma attacks, and heart attacks, as well as chronic illnesses and premature mortality. The most vulnerable groups include children, the elderly, and individuals with pre-existing health conditions. This study focuses on the analysis of health risks associated with PM₁₀ and PM_2.5 air pollution in the city of Zvolen, which serves as a representative case due to its urban structure, traffic load, and industrial activity. The aim is to assess the current state of air quality, identify the main sources of pollution, and evaluate the health impacts of particulate matter on the local population. The results will be compared with selected Slovak cities—Banská Bystrica and Ružomberok—to understand regional differences in exposure and its health consequences. The results revealed consistently elevated concentrations of particulate matter (PM) across all analyzed cities, frequently exceeding the guideline values recommended by the World Health Organization (WHO), although remaining below the thresholds set by current national legislation. The lowest average concentrations were recorded in the city of Zvolen (PM₁₀: 20 μg/m³; PM_2.5: 15 μg/m³). These lower values may be attributed to the location of the reference monitoring station operated by the Slovak Hydrometeorological Institute (SHMÚ), situated on J. Alexy Street in the southern part of the city—south of Zvolen’s primary industrial emitter, Kronospan. Due to predominantly southerly wind patterns, PM particles are transported northward, potentially leading to higher pollution loads in the northern areas of the city, which are currently not being monitored. We analyzed trends in PM₁₀ and PM_2.5 concentrations and their relationship with hospitalization data for respiratory diseases. The results indicate a clear correlation between the concentration of suspended particulate matter and the number of hospital admissions due to respiratory illnesses. Our findings thus confirm the significant adverse effects of particulate air pollution on population health and highlight the urgent need for systematic monitoring and effective measures to reduce emissions, particularly in urban areas. Full article

Background: Air pollution is a persistent and critical challenge for Almaty, Kazakhstan’s largest city. The city’s unique topographical and meteorological conditions—being located in a mountain basin with dense urban development—restrict natural ventilation and contribute to frequent exceedances of air quality standards. These factors make accurate assessment and management of atmospheric pollution particularly urgent for the region. Aim: This study aims to develop and apply a novel, high-resolution three-dimensional numerical model to analyze the spatial distribution of key atmospheric indicators—air velocity, temperature, and pollutant concentrations in Almaty. The goal is to provide a comprehensive understanding of how meteorological and urban factors influence air quality, with a focus on both horizontal and vertical stratification. Methods: A three-dimensional computational model was constructed, integrating real meteorological data and detailed urban topography. The model solves the compressible Navier–Stokes, energy, and pollutant transport equations using the finite volume method over a 1000 × 1000 × 500 m domain. Meteorological fields are synthesized along all spatial axes to account for vortex structures, urban heat islands, and stratification effects. This approach enables the simulation of atmospheric parameters with unprecedented spatial resolution for Almaty. Results: The simulation reveals significant spatial heterogeneity in atmospheric parameters. Wind velocity ranges from 0.31 to 5.76 m/s (mean: 2.14 m/s), temperature varies between 12.03 °C and 19.47 °C (mean: 16.12 °C), and pollutant concentrations fluctuate from 5.02 to 102.35 μg/m³ (mean: 44.87 μg/m³). Notably, pollutant levels in the city center exceed those at the periphery by more than two-fold (68.23 μg/m³, 29.14 μg/m³), and vertical stratification leads to a marked decrease in concentrations with altitude. These findings provide, for the first time, a comprehensive and quantitative picture of air quality dynamics in Almaty. Conclusion: The developed model advances the scientific understanding of urban air pollution in complex terrains and offers practical tools for city planners and policymakers. By identifying pollution hotspots and elucidating the influence of meteorological factors, the model supports the optimization of urban infrastructure, zoning, and environmental monitoring systems. This research lays the groundwork for evidence-based strategies to mitigate air pollution and improve public health in Almaty and similar urban environments. Full article

This paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. Subsequent radar measurements are transformed into an image-type representation for use as CNN input. In contrast to conventional approaches that require compensation for temperature and loading effects, the proposed framework inherently learns all required information during the training phase. Its damage detection performance (i.e., detecting intact vs. damaged condition) is demonstrated using measurements from multiple embedded radar sensors during fatigue testing of a wind turbine blade with a length of 31 m. The achieved F1-score for correct damage classification is between 91% and 100% for both the unloaded and the loaded blade. Full article

Long-span structures are highly vulnerable to wind-induced vibrations, which can pose a significant threat to their structural stability and safety. This paper introduces a novel monitoring method that combines Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology with Auto-Regressive Moving Average (ARMA) models, providing an innovative approach to monitoring wind-induced vibrations in large-span bridges. While previous studies have focused on individual techniques, this integrated approach is largely unexplored and offers a new perspective for structural health monitoring. By collating a series of SAR images and examining phase alterations on the bridge surface, a three-tiered detection methodology is employed to identify stable points accurately. The surface deformation data are then analyzed alongside wind speed and weather data to construct a comprehensive model elucidating the relationship between the bridge and vibrations. The ARMA model is used for real-time monitoring and assessment. Experimental results demonstrate that this method offers precise, real-time monitoring of wind-resistant stability. By leveraging the spatial accuracy and long-term monitoring capability of PS-InSAR, along with the time-series forecasting strength of ARMA models, the method enables data-driven analysis of bridge vibrations. It also provides comprehensive coverage under various conditions, enhancing the safety of long-span bridges through advanced predictive analytics. Full article

Forest ecosystems are crucial in mitigating air pollution and improving air quality. Therefore, investigating the relationships between air quality, forest structure, and environmental factors in different forest types is of significant importance. This study conducted three months of continuous monitoring (June–September 2023) of air quality factors (particulate matter (PM_2.5 and PM₁₀), ozone (O₃), and negative air ions (NAI)) and environmental factors (air temperature (TA), relative humidity (RH), light intensity (LI), and wind speed (WS)) in four subtropical forest types, along with vegetation characteristic surveys. The effects of forest structure and environmental factors on air quality were determined by correlation and multiple regression analysis. The results showed that the forest air quality is at its best in July during the summer season. Concentrations of particulate matter (PM) and ozone (O₃) in mixed coniferous and broadleaf forests (MCB), as well as deciduous broadleaf forests (DB), are lower than those in moso bamboo forests (MB) and evergreen broadleaf forests (EB). The troughs of PM concentrations occur in the early morning (4:00–6:00), while the troughs of O₃ concentrations occur in the early morning (4:00–6:00) and in the evening (18:00). NAI concentrations were highest in DB (1287 ions/cm³), followed by MCB (1187 ions/cm³), MB (896 ions/cm³), and EB (584 ions/cm³), with NAI concentrations peaking between 14:00 and 16:00. PM concentrations in forest air were primarily influenced by stand density (SD) and the Shannon–Wiener index of herbaceous layer (SWH) (p < 0.05); ozone concentrations were significantly affected by tree height (TH) and canopy density (CD) (p < 0.05); and NAI concentrations were primarily related to TH and diameter at breast height (DBH). Air particulate matter concentrations were negatively affected by TA and RH (p < 0.01), and ozone concentrations were negatively influenced by RH and WS and were positively influenced by TA. TA has a direct and significant positive effect on the NAI concentration (p < 0.01), and RH indirectly influences the changes in NAI concentration through its interaction with TA. This study provides new insights for vegetation optimization in forest parks and planning forest health-promoting activities for sub-healthy populations. Full article

A dendrochronological study was conducted on a submontane raised bog, Bór na Czerwonem, in the Orava–Nowy Targ Basin in Southern Poland. In the past, the bog was drained to enable peat extraction. In recent years, a number of measures considered as active protection were undertaken, including the construction of ridges and locks, filling of the drainage trenches, and clearance of most of the tree stand on the bog dome. Pinus sylvestris, P. × rhaetica, and P. mugo were the focuses of the study, which aimed to determine the age of the genus stand and its age structure and to identify the factors influencing tree ring width. The age of the trees indicates a post-war succession induced by large-scale drainage in 1942, although single trees were present on the bog dome as early as the late 19th century, and probably earlier. High values of pith eccentricity at ground level testify to substratum instability and the impact of strong winds on tree ring formation. The growth–climate relationships change with the progressive climate change: the significance of insolation increases, while the significance of the absolute air temperature decreases. The thermal and pluvial conditions of the summer in the previous growth season, however, make the strongest impact on the tree ring width in the following growth season. The health of the trees left growing on the bog, due to the constantly rising water level, will likely deteriorate, and a decreasing number of seedlings will be observed. A full assessment of the conducted restoration efforts, however, will be possible after years of monitoring of the bog environment. Full article

The accuracy of identifying dynamic characteristics of super-tall buildings under typhoon conditions, as well as their correlation with the vibration amplitude, remains unclear, limiting the effective assessment of the structural performance and optimization of wind-resistant designs. To address this issue, the measured wind-generated vibration responses of Shanghai World Finance Center during the passage of Typhoon “Rumbia” were derived using data obtained from the health monitoring system of a super-tall building in Shanghai. The first and second inherent frequencies, as well as the damping ratio of the structure, were ascertained through the employment of the curve method and the standard deviation method. Based on this, a comparison and analysis were carried out regarding the variation patterns of the first and second inherent frequencies and the damping ratio with reference to the vibration amplitude. Vibration modes were identified using frequency domain analysis. The results of the natural frequency identification were compared to those from the Peak Picking method to see how well the curve method and the standard deviation method worked at finding modal parameters. Ultimately, an assessment of the super-tall building’s performance during the impact of the typhoon was conducted. The results demonstrate that the curve method and the standard deviation method can accurately identify the inherent frequency and damping ratio of the structure, with the curve method revealing a more pronounced regularity of the modal parameters. For the structure, in the horizontal and longitudinal directions, the first and second inherent frequencies exhibit a negative correlation with amplitude, while the damping ratio shows a positive correlation with amplitude. Moreover, as the floor level rises, the vibration modes in both directions of the structure steadily increase. During the impact of Typhoon “Rumbia”, the building’s performance complied with the requirements set by comfort standards. These analytical results not only provide valuable references for the wind-resistant design and vibration control of super-tall buildings but also offer critical support for condition assessment and damage identification within structural health monitoring systems. Full article

The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of hybrid energy harvesters, which utilize multiple energy sources, compared to single-source harvesters. The analysis leverages a real-world dataset collected from a meteorological station in Cervinara, Southern Italy. The measured data are processed to estimate the energy that can be recovered from solar, wind, and rain sources using energy harvesters designed to supply low-power electronic devices. The available energy serves as the basis for optimizing the sizing of a hybrid energy harvester that effectively integrates the aforementioned energy sources. The system sizing, carried out under the constraint of ensuring a continuous and uninterrupted power supply to the load, quantifies the benefits of using a hybrid harvester over a single-source harvester. The results show that one of the main advantages of the hybrid solution is the reduction in the size of the storage device, enabling the replacement of rechargeable batteries with supercapacitors, which offer both environmental and reliability benefits. Full article

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health. Full article