• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• Wind and Structures
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• v.20 no.6
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• pp.763-791
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• 2015

"Wind and Ports" is a European project that has been carried out since 2009 to handle wind forecast in port areas through an integrated system made up of an extensive in-situ wind monitoring network, the numerical simulation of wind fields, the statistical analysis of wind climate, and algorithms for medium-term (1-3 days) and short term (0.5-2 hours) wind forecasting. The in-situ wind monitoring network, currently made up of 22 ultrasonic anemometers, provides a unique opportunity for detecting high resolution thunderstorm records and studying their dominant characteristics relevant to wind engineering with special concern for wind actions on structures. In such a framework, the wind velocity of thunderstorms is firstly decomposed into the sum of a slowly-varying mean part plus a residual fluctuation dealt with as a non-stationary random process. The fluctuation, in turn, is expressed as the product of its slowly-varying standard deviation by a reduced turbulence component dealt with as a rapidly-varying stationary Gaussian random process with zero mean and unit standard deviation. The extraction of the mean part of the wind velocity is carried out through a moving average filter, and the effect of the moving average period on the statistical properties of the decomposed signals is evaluated. Among other aspects, special attention is given to the thunderstorm duration, the turbulence intensity, the power spectral density and the integral length scale. Some noteworthy wind velocity ratios that play a crucial role in the thunderstorm loading and response of structures are also analyzed.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.5
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• pp.539-544
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• 2000

A simple analytic model is proposed here to analyze the concentration deficit field caused by a large area of vegetated area. With non-dimensional deposition velocity chosen as small parameter, the regular perturbation method is exploited to derive the mass balance equation and the dynamic equations for the concentration deficit field, Analytic solutions to those equations are obtained in a closed form for several cases of interest, assuming that the concentration field is stationary and the plume can be nicely approximated as Gaussian for a point source. The results suggest that quite a negligible fraction (less than 1%) of the gaseous air pollutants emitted into the air is removed by the vegetated area of which width is 4 km in wind-wise direction, the typical dimension of the Restricted Development Zones around the metropolitan regions in South Korea.

• Journal of the korean society of oceanography
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• v.31 no.4
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• pp.183-195
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• 1996

A barotropic non-linear numerical model is used to study the response of the Yellow Sea to winter cyclone passage. Cyclones normally come from the outside of the western boundary, China, and pass the region eastward. The cyclone parameters used for the present study are the following: the intensity, i.e., the maximum wind speed of the cyclone; the effective radius corresponding to this maximum; and the translation speed. The equations of motion are integrated over the depth which is supposed to be a constant. The Gaussian function is used to define the stream function of the wind. The following results have been found. A northward current is generated by the frontal part of the cyclone near the western boundary. After the cyclone leaves the sea area, a southward current is generated by the rear part of the cyclone. After that, a northward current is generated once again due to the westward propagating Rossby waves. The response of the sea to the cyclone passage is strongly influenced by a steady current when the steady current and the current due to the cyclone wind are of the same order. The steady current diminishes the sea response and reduces the speed of the southward current, and enhances the northward current speed. The intensity and the translation speed of a cyclone also influence the flow pattern significantly.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Water for future
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• v.10 no.1
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• pp.101-117
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• 1977

The statatistical characteristics and spectra of sea waves at Mookho were analysed by several statistical methods. As the results, the following conclusions are obtained: 1. Values of surface elevation of sea wave are better fitted to Gram Charlier distribution than Gaussian distribution. This proves that sea waves have not only characters of irregularity but also non-linearity. 2. Distribution of maxima of surface elevation practically follows the distribution of Cartwright and Longuet-Higgins, also spectral width parameter is found to be increased with the increase of root mean square of surface elevation. 3. Sea wave may have spectrum of broad frequency band, however distributions of wave heights and periods follow the Rayleigh distribution which is derived from the assumption of narrow frequency band. 4. Ratios among mean wave heights from observed data show good agreements with theoretical values from Rayleigh distribution. 5. Spectral density and spectral width parameter increase with increase of wind velocity. And wave period at optimum band gas higher value than significant wave period by about 10 percent.