The lateral component of turbulence and the vortices shed in the wake of a structure result in introducing dynamic wind load in the acrosswind direction and the resulting level of motion is typically larger than the corresponding alongwind motion for a dynamically sensitive structure. The underlying source mechanisms of the acrosswind load may be classified into motion-induced, buffeting, and Strouhal components. This study proposes a frequency domain framework to decompose the overall load into these components based on output-only measurements from wind tunnel experiments or full-scale measurements. First, the total acrosswind load is identified based on measured acceleration response by solving the inverse problem using the Kalman filter technique. The decomposition of the combined load is then performed by modeling each load component in terms of a Bayesian filtering scheme. More specifically, the decomposition and the estimation of the model parameters are accomplished using the unscented Kalman filter in the frequency domain. An aeroelastic wind tunnel experiment involving a tall circular cylinder was carried out for the validation of the proposed framework. The contribution of each load component to the acrosswind response is assessed by re-analyzing the system with the decomposed components. Through comparison of the measured and the re-analyzed response, it is demonstrated that the proposed framework effectively decomposes the total acrosswind load into components and sheds light on the overall underlying mechanism of the acrosswind load and attendant structural response. The delineation of these load components and their subsequent modeling and control may become increasingly important as tall slender buildings of the prismatic cross-section that are highly sensitive to the acrosswind load effects are increasingly being built in major metropolises.

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

Aerodynamic force coefficients are generally prescribed by an ensemble average of ten and/or twenty 10-minute samples. However, this makes it difficult to identify the exact probability distribution and exceedance probability of the prescribed values. In this study, 12,600 10-minute samples on three tall buildings were measured, and the probability distributions were first identified and the aerodynamic force coefficients corresponding to the specific non-exceedance probabilities (cumulative probabilities) of wind load were then evaluated. It was found that the probability distributions of the mean and fluctuating aerodynamic force coefficients followed a normal distribution. The ratios of aerodynamic force coefficients corresponding to the specific non-exceedance probabilities (C_f,Non) to the ensemble average of 12,600 samples (C_f,Ens), which was defined as an adjusting factor (C_f,Non/C_f,Ens), were less than 2%. The effect of coefficient of variation of wind speed on the adjusting factor is larger than that of the annual non-exceedance probability of wind load. The non-exceedance probabilities of the aerodynamic force coefficient is between P_C,nonex = 50% and 60% regardless of force components and aspect ratios. The adjusting factors from the Gumbel distribution were larger than those from the normal distribution.

As a new kind of construction facility for high rise buildings, the integral steel platform scaffold system (ISPS) consisting of the steel skeleton and suspended scaffold faces high wind during the construction procedure. The lattice structure type and existence of core tubes both make it difficult to estimate the wind load and calculate the wind-induced responses. In this study, an aeroelastic model with a geometry scale ratio of 1:25 based on the ISPS for Shanghai Tower, with the representative square profile, is manufactured and then tested in a wind tunnel. The first mode of the prototype ISPS is a torsional one with a frequency of only 0.68 Hz, and the model survives under extreme wind speed up to 50 m/s. The static wind load and wind vibration factors are derived based on the test result and supplementary finite element analysis, offering a reference for the following ISPS design. The spacer at the bottom of the suspended scaffold is suggested to be long enough to touch the core tube in the initial status to prevent the collision. Besides, aerodynamic wind loads and cross-wind loads are suggested to be included in the structural design of the ISPS.

Low-rise structures are generally immersed within the roughness layer of the atmospheric boundary layer flows and represent the largest class of the structures for which wind loads for design are being obtained from the wind standards codes of distinct nations. For low-rise buildings, wind loads are one of the decisive loads when designing a roof. For the case of cylindrical roof structures, the information related to wind pressure coefficient is limited to a single span only. In contrast, for multi-span roofs, the information is not available. In this research, the numerical simulation has been done using ANSYS CFX to determine wind pressure distribution on the roof of low-rise cylindrical structures arranged in rectangular plan with variable spacing in accordance with building width (B=0.2 m) i.e., zero, 0.5B, B, 1.5B and 2B subjected to different wind incidence angles varying from 0° to 90° having the interval of 15°. The wind pressure (P) and pressure coefficients (C_pe) are varying with respect to wind incidence angle and variable spacing. The results of present numerical investigation or wind induced pressure are presented in the form of pressure contours generated by Ansys CFD Post for isolated as well as variable spacing model of cylindrical roofs. It was noted that the effect of wind shielding was reducing on the roofs by increasing spacing between the buildings. The variation pf Coefficient of wind pressure (C_pe) for all the roofs have been presented individually in the form of graphs with respect to angle of attacks of wind (AoA) and variable spacing. The critical outcomes of the present study will be so much beneficial to structural design engineers during the analysis and designing of low-rise buildings with cylindrical roofs in an isolated as well as group formation.