Search Results (48)

The southeastern coastal region of China has long been affected by typhoon disasters, which pose significant threats to the safety of offshore structures. Therefore, predicting extreme wind speeds corresponding to various return periods on the basis of limited typhoon samples is particularly important for wind-resistant design. This study systematically predicts extreme typhoon wind speeds for various return periods and quantitatively assesses the sensitivity of key parameters by employing a Monte Carlo stochastic simulation framework integrated with a typhoon straight-line trajectory model and the Yan Meng wind field model. Focusing on Guangzhou (23.13° N, 113.28 °E), a representative coastal city in southeastern China, this research establishes a modular analytical framework that provides generalizable solutions for typhoon disaster assessment in coastal regions. The probabilistic wind load data generated by this framework significantly increases the cost-effectiveness and safety of wind-resistant structural design. Full article

This study examines the trends and statistical characteristics of daily maximum wind speed across various synoptic stations in Ardabil Province, Iran, with diverse topography. Using daily wind speed data from multiple synoptic stations, the research focuses on three primary objectives: assessing changes in daily maximum wind speed, fitting various statistical distributions to the data, and estimating wind speed values for different return periods. In this research, the temporal changes were evaluated while analyzing the frequency of the data, and then the maximum wind speed values were calculated and analyzed for different return periods by fitting frequency distributions. The analysis reveals notable variability in maximum wind speeds across stations. The trend analysis, conducted using the nonparametric Mann–Kendall method, reveals significant positive trends in maximum wind speed at Meshgin-Shahr and Sareyn (p < 0.05). Meanwhile, data from Khalkhal station displays a significant decreasing trend, while other stations, like Ardabil and Parsabad, show no meaningful trends. According to the statistical distributions analysis, the Fisher–Tippett T2 mirrored distribution demonstrates the best fit for Ardabil, with an absolute difference of 2.52%, while the Laplace distribution yields the lowest discrepancies for Bilesavar (3.50%) and Ardabil Airport (3.83%). This ranking indicates that, despite similar first-ranked distributions in some stations, secondary models show variability, suggesting localized influences on wind speed that modify distributional fit. As a conclusion, the Laplace (std) distribution stands out as the best-fit model for several stations, showing relative consistency across several stations. These findings demonstrate the necessity of site-specific statistical modeling to accurately represent wind speed patterns across the diverse landscapes of Ardabil Province. Based on the results, comparing the wind characteristics in the study area with those of other regions in Iran, as well as analyzing the reported trends, can be useful in determining the impact of the region’s climatic conditions and topography on wind patterns. This research offers key insights into wind speed variability and trends in Ardabil, crucial for climate adaptation and risk management of extreme wind events. Full article

Zhejiang Province in China is one of the most typhoon-prone regions globally, making typhoon and storm surge hazard analysis critically important for disaster mitigation. This study integrates the Tropical Cyclone Risk Model (TCRM) with a machine learning-based storm surge forecasting model to analyze typhoon hazards and storm surge risks at four representative coastal sites in Zhejiang Province: Haimen, Ruian, Wenzhou, and Zhapu. Firstly, the input database of the TCRM has been updated and subsequently used to generate a 1000-year synthetic typhoon event catalog for the Northwest Pacific region. Secondly, four machine learning models—Long Short-Term Memory (LSTM), Back Propagation (BP), Support Vector Regression (SVR), and Random Forest (RF)—were developed to forecast storm surge component at the four sites, with sensitivity analysis conducted on the input parameters. Among the four models, RF consistently outperformed the others across all four sites. Thirdly, by integrating the storm surge forecasting model with the Yan Meng (YM) typhoon wind field model, extreme wind speed sequences and extreme surge component sequences were derived for the four coastal sites. Finally, four extreme value distribution models—empirical distribution, Weibull, Gumbel, and Generalized Pareto Distribution (GPD)—were applied to fit the extreme wind and surge sequences. Goodness-of-fit tests indicated that the GPD best captured extreme wind speeds at all four sites and extreme surge levels at Haimen, Ruian, and Wenzhou. Using the optimal distributions, return periods (10-, 50-, 100-, and 200-year) for extreme wind speeds and surge components were calculated, providing actionable references for disaster risk management authorities. Full article

Large-scale windstorms represent an important atmospheric hazard in the Northeastern US (NE) and are associated with substantial socioeconomic losses. Regional simulations performed with the Weather Research and Forecasting (WRF) model using lateral boundary conditions from three Earth System Models (ESMs: Geophysical Fluid Dynamics Laboratory (GFDL), Hadley Centre Global Environment Model (HadGEM) and Max Planck Institute (MPI)) are used to quantify possible future changes in windstorm characteristics and/or changes in the parent cyclone types responsible for windstorms. WRF nested within MPI ESM best represents important aspects of historical windstorms and the cyclone types responsible for generating windstorms compared with a reference simulation performed with the ERA-Interim reanalysis for the historical climate. The spatial scale and frequency of the largest windstorms in each simulation defined using the greatest extent of exceedance of local 99.9th percentile wind speeds (U > U₉₉₉) plus 50-year return period wind speeds (U_50,RP) do not exhibit secular trends. Projections of extreme wind speeds and windstorm intensity/frequency/geolocation and dominant parent cyclone type associated with windstorms vary markedly across the simulations. Only the MPI nested simulations indicate statistically significant differences in windstorm spatial scale, frequency and intensity over the NE in the future and historical periods. This model chain, which also exhibits the highest fidelity in the historical climate, yields evidence of future increases in 99.9th percentile 10 m height wind speeds, the frequency of simultaneous U > U₉₉₉ over a substantial fraction (5–25%) of the NE and the frequency of maximum wind speeds above 22.5 ms⁻¹. These geophysical changes, coupled with a projected doubling of population, leads to a projected tripling of a socioeconomic loss index, and hence risk to human systems, from future windstorms. Full article

The ongoing impacts of climate change, driven by both anthropogenic and global warming, significantly influence wind characteristics, resulting in increased wind speeds. Consequently, buildings that currently satisfy safety and serviceability standards may face challenges in the future. Despite extensive studies on wind-induced responses of tall buildings, there is a notable lack of comparative analyses assessing their performance under both historical and projected future wind conditions influenced by climate change. This study investigates the wind-induced performance of a 151 m tall building located in Suzhou, China, employing time history generation based on power spectral density functions. The analysis evaluates the acceleration responses of the building under both historical and projected future wind scenarios across different return periods and compares the responses to identify the potential changes in the building’s performance due to changing wind conditions. The structural acceleration responses are projected to rise significantly under future wind conditions. Furthermore, this study uses a time-domain Monte Carlo simulation framework to conduct a fragility analysis of the case study building, assessing the comfort of human occupants and the likelihood of exceeding performance thresholds under various wind scenarios. The fragility curve for the case study building is plotted for human occupant comfort as a function of mean wind speed. A substantial increase in the building’s fragility concerning occupant comfort is observed. The future wind climate will significantly impact the performance of tall buildings, necessitating proactive measures to address increased wind-induced effects and ensure long-term safety and habitability. Full article

The present work addresses the critical challenge of assessing building facade watertightness against wind-driven rain, a major threat to structural integrity and durability. The current evaluation methods rely heavily on standardized test outcomes, neglecting a disconnect between test conditions and real-world exposure, leading to subjective judgments. To bridge this gap, this paper developed a quantitative method linking key inspection parameters (pump pressure, water spray distance) to wind-driven rain characteristics (wind speed, rainfall intensity) in the Shanghai area using statistical return periods. The calculation process encompasses regression models that correlate extreme rainfall and wind velocity values over sub-daily intervals, as well as a method for extrapolating maximum wind velocities using wind data coinciding with rainfall events. This approach enables specification-compliant performance assessment and tailored inspection protocols, such as JGJ/T 299, EN 12155, and ASTM E547. Applied to two Shanghai buildings, the method demonstrated a robust framework for translating environmental data into actionable inspection criteria. The results show a direct correlation between test parameters and extreme weather statistics. For instance, the watertightness performance of an old building is quantitively assessed as a return period of 1.02 years, while a new office building aiming for 50-year waterproofing could be inspected at a pump pressure of 900 kPa and a spraying distance of 0.15 m using the proposed method. This paper offers a data-driven alternative to empirical assessments, enhancing reliability in facade design and regulatory compliance, and provides a scientific basis for decision-making in building maintenance and renovation. Full article

The Weather Research and Forecasting (WRF) model is employed to conduct numerical simulations and simulated acquisition of a 30-year (1993–2022) wind field dataset for the Bohai Sea. The simulated WRF wind field is subsequently used to drive the Simulating Waves Nearshore (SWAN) model, producing a corresponding wave field dataset for the same period in the Bohai Sea. Using these datasets, we analyzed the extreme value distributions of wind speed and significant wave height in the study area. The results reveal that both the annual mean wind speed and significant wave height exhibit a ring-like spatial pattern. The highest values are concentrated in the southern Liaodong Bay to the central Bohai Sea region, with a gradual radial decrease toward the periphery. Specifically, values decline from the center outward, from southeast to northwest, and from offshore to nearshore regions. The Gumbel extreme value distribution is applied to estimate 100-year return period extremes, yielding maximum wind speeds of 37 m/s and significant wave heights of 6 m in offshore areas. In nearshore regions, the 100-year return period wind speeds range between 20–25 m/s, while significant wave heights vary from 2 to 3 m. This study provides important scientific basis and decision-making reference for the design of offshore extreme conditions. Full article

Environmental load data are an essential input for the analysis of offshore wind structures in typhoon-prone marine environments. However, numerical simulations of typhoon waves lack a systematic examination of the specific influence of typhoon trajectories on the spatial evolution of wave fields. In particular, the intricate mechanisms governing wave propagation within wind farm areas remain poorly understood. This present study, drawing upon a real-world case in an offshore wind farm area in the South China Sea, employs the Finite Volume Coastal Ocean Model–Surface Wave Module (FVCOM–SWAVE) wave–current coupling model to assess the joint wind–wave distribution characteristics during 35 typhoon events. The findings reveal that typhoon wave fields exhibit a notable rightward bias. As waves approach the coast, the significant wave height decreases progressively due to wave breaking, friction, refraction, and nonlinear interactions. During the passage of typhoons Prapiroon, Hato, and Mangkhut, the significant wave height distribution in the wind farm area closely correlated with the wind speed distribution. By constructing a joint distribution function of sea wind and wave elements, the joint distribution characteristics of wind speed and significant wave height for different return periods can be obtained, providing important oceanic environmental inputs for the design analysis of offshore wind structures. Full article

During its service life, a deep-sea floating structure is likely to encounter extreme marine disasters. The combined action of wind and wave loads poses a threat to its structural safety. In this study, elliptical copula, Archimedean copula, and vine copula models are employed to depict the intricate dependence structure between wind and waves in a specific sea area of the Shandong Peninsula. Moreover, hourly significant wave height, spectral peak period, and 10 m average wind speed hindcast data from 2004 to 2023 are utilized to explore the joint distribution of multidimensional parameters and environmental design values. The results indicate the following: (1) There exists a significant correlation between wind speed and wave parameters. Among them, the C-vine copula model represents the optimal trivariate joint distribution, followed by the Gaussian copula, while the Frank copula exhibits the poorest fit. (2) Compared with the high-dimensional symmetric copula models, the vine copula model has distinct advantages in describing the dependence structure among several variables. The wave height and period demonstrate upper tail dependence characteristics and follow the Gumbel copula distribution. The optimal joint distribution of wave height and wind speed is the t copula distribution. (3) The identification of extreme environmental parameters based on the joint probability distribution derived from environmental contour lines is more in line with the actual sea conditions. Compared with the design values of independent variables with target return periods, it can significantly reduce engineering costs. In conclusion, the vine copula model can accurately identify the complex dependency characteristics among marine variables, offering scientific support for the reliability-based design of floating structures. Full article

Complex wind and temperature characteristics in the Yellow River basin (YRB) challenge the safety and durability of long-span concrete-filled steel tube (CFST) bridges greatly. To address this issue, it is important to accurately assess the joint actions of wind and temperature. In this paper, the joint actions of wind and temperature in eight typical YRB cities are analyzed. The joint distributions of wind speed and air temperature are developed with the Archimedean Copula, and the Kendall return period is used for occurrence probability estimations. Eight wind–temperature combinations are considered. Responses for these combinations are calculated and compared with specification actions. Results show significant wind–temperature variations in the YRB. When wind actions adopt the univariate representative values (URVs), the temperature actions are reduced by 20–40%; when temperature actions use URVs, wind actions experience a reduction by more than half of their URVs. The joint responses can sometimes exceed, but are mostly less than, the specification responses, with a maximum strength margin over 11 MPa. These efforts suggest that the proposed joint actions can expand the provisions in the General Specification and provide guidance for the design of long-span CFST bridges. Full article

The new global pandemic of COVID-19, declared on 11 March 2020 by the World Health Organization, has already had an unprecedented impact on health and socioeconomic activities worldwide. The second wave of the COVID-19 pandemic swept through the United States of America and Europe in late September 2020. Compared with other southern countries, such as Greece, where there was a significant increase in cases at the end of October 2020, Northern European countries (Germany, France, Austria, Finland, and Sweden) experienced this second wave of the pandemic earlier in September 2020. To understand the epidemiological behavior of the virus from an environmental perspective, we examined the effects of air temperature, humidity, and wind on the spread of COVID-19 in two of the largest population Regional Units (R.U.) of Greece, namely the R.U. of the Central Sector of Athens in Central Greece and the R.U. of Thessaloniki in Northern Greece. We applied Pearson correlation analysis and generalized linear models (GLM) with confirmed COVID-19 Intensive Care Unit (ICU) admissions from the National Public Health Organization as dependent variables and the corresponding air temperature, humidity, and wind speed from the Greek National Meteorological Service as independent covariates. The study focused on the period from 4 May 2020 to 3 November 2020 to investigate the impact of weather on the spread of COVID-19, in a period where human activities had partially returned to normal after the gradual lifting of the restrictive measures of the first lockdown (23 March 2020). The end date of the study period was set as the date of imposition of a new local lockdown in the R.U. of Thessaloniki (3 November 2020). Our findings showed that COVID-19 ICU admissions in both Regional Units decreased significantly with the temperature (T) and wind speed (WS) increase. In the R.U. of the Central Sector of Athens, this picture is reflected in both the single and cumulative lag effects of meteorological parameters. In the R.U. of Thessaloniki, this correlation was differentiated only in terms of the cumulative lag effect of the average daily temperature, where an increase (+17.6%) in daily confirmed COVID-19 ICU admissions was observed. On the other hand, relative humidity (RH) was significantly associated with an increase in cases in both R.U. This study, in addition to its contribution to the global research effort to understand the effects of weather on the spread of COVID-19, aims to highlight the need to integrate meteorological parameters as predictive factors in surveillance and early warning systems for infectious diseases. The combination of weather and climate factors (e.g., humidity, temperature, wind) and other contagious disease surveillance indicators (e.g., wastewater, geographic and population data, human activities) would make the early identification of potential epidemic risks more effective and would contribute to the immediate initiation of public health interventions and the more rational allocation of resources. Full article

To investigate an appropriate wind load design for buildings considering dynamic air density changes, classical extreme value and copula theories were utilized. Using wind speed, air temperature, and air pressure data from 123 meteorological stations in Shandong Province from 2004 to 2017, a joint probability distribution model was established for extreme wind speed and air density. The basic wind pressure was calculated for various conditional return periods. The results indicated that the Gumbel and Gaussian mixture model distributions performed well in extreme wind speed and air density fitting, respectively. The joint extreme wind speed and air density distribution exhibited a distinct bimodal pattern. The higher the wind speed was, the greater the air density for the same return conditional period. For the 10-year return period, the air density surpassed the standard air density, exceeding 1.30 kg/m³. The basic wind pressures under the different conditional return periods were more than 10% greater than those calculated from standard codes. Applying the air density based on the conditional return period in engineering design could enhance structural safety regionally. Full article

To predict and explore the return period wind speed distribution in Beijing urban area, based on the short-term measured wind speed record, the measured independent storm peak samples are extracted and the Monte–Carlo numerical simulation method is used to randomly generate pseudo-independent storm peak samples with the same distribution as the independent storm peak samples. In addition, the appropriate threshold is used to select and extract the over-threshold independent storm peak samples from the measured samples. And, then, the return period wind speed of the whole wind direction is predicted. Further, the traditional Cook method is used to predict and analyze the return period wind speed of each wind direction according to the measured wind speed data. The results show that the return period wind speed prediction results of the whole wind direction corresponding to the simulated pseudo samples and the measured over-threshold samples are basically close to each other, and the maximum relative deviation is less than 7%. The mean relative deviation of the return period wind speed prediction results is about 10.6% between each wind direction and the whole wind direction, and the mean relative deviation is about 9.4% between the RPWS prediction result of each wind direction and the design value of the code. In addition, the return period wind speed prediction values of the west by southwest, northwest and north by northeast wind directions exceed the design value of the code, while the predicted values of the other wind directions are within the envelope range of the design value of the code. Finally, the research content of this paper can provide some reference for the wind resistance design analysis of building structures in Beijing area. Full article

Many modern frameworks for community resilience and emergency management in the face of extreme hydrometeorological and climate events rely on scenario building. These scenarios typically cover multiple hazards and assess the likelihood of their occurrence. They are quantified by their main characteristics, including likelihood of occurrence, intensity, duration, and spatial extent. However, most studies in the literature focus only on the first two characteristics, neglecting to incorporate the internal hazard dynamics and their persistence over time. In this study, we propose a multidimensional approach to construct extreme event scenarios for multiple hazards, such as heat waves, cold spells, extreme precipitation and snowfall, and wind speed. We consider the intensity, duration, and return period (IDRP) triptych for a specific location. We demonstrate the effectiveness of this approach by developing pertinent scenarios for eight locations in Greece with diverse geographical characteristics and dominant extreme hazards. We also address how climate change impacts the scenario characteristics. Full article

Structural robustness is defined by the international engineering community as the capabilities of structural systems that enable them to survive unforeseen or unusual circumstances. In order to highlight the unforeseen and unusual characteristics of wind hazards, this study introduces the concept of structural robustness into the wind-resistant design and evaluation of bridges and proposes the robustness evaluation of aerodynamic flutter and aerostatic torsional stability of long-span bridges. Furthermore, the return period of the design wind speed that a bridge can resist is used to represent wind-resistant robustness. Aiming at the problem of aerodynamic and aerostatic stability, the analysis methods of aerodynamic flutter stability robustness and aerostatic torsional stability robustness of long-span suspension bridges are respectively established. Based on the established methods of aerodynamic flutter stability and aerostatic torsional stability robustness evaluation, robustness analysis is carried out on eight completed long-span suspension bridges and two long-span suspension bridges to be built. The evaluation method proposed in this study makes it possible to measure the ability of bridge structures to resist multiple disasters using the same index. Full article