Abstract
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.
Address
Jae-Seung Hwang:School of Architecture, Chonnam National University, Gwangju 61186, Republic of Korea
Dae-Kun Kwon:1)NatHaz Modeling Laboratory, University of Notre Dame, Notre Dame, IN 46556, USA
2)Center for Research Computing (CRC), University of Notre Dame, Notre Dame, IN 46556, USA
Jungtae Noh:Department of Architectural Engineering, Dankook University, Yongin-si, Gyeonggi-do 16890, Republic of Korea
Ahsan Kareem:NatHaz Modeling Laboratory, University of Notre Dame, Notre Dame, IN 46556, USA
Abstract
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.
Key Words
deep convolutional generative adversarial network; deep learning; generative adversarial imputation model; highrise buildings; wind-tunnel test
Address
K.R. Sri Preethaa and N. Yuvaraj:1)Department of Robot and Smart System Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea
2)Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore–641407, India
Gitanjali Wadhwa:Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore–641407, India
Sujeen Song:Earth Turbine, 36 Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Korea
Se-Woon Choi:Department of Architectural Engineering, Daegu Catholic University,
Hayang-Ro 13-13, Hayang-Eup, Gyeongsan-si, Gyeonqbuk, 38430, Korea
Bubryur Kim:Department of Robot and Smart System Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea
Abstract
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 (Cf,Non) to the ensemble average of 12,600 samples
(Cf,Ens), which was defined as an adjusting factor (Cf,Non/Cf,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 PC,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.
Key Words
adjusting factor; aerodynamic force coefficient; coefficient of variation; non-exceedance probability; probability
density distribution
Address
Yong Chul Kim:Department of Engineering, Tokyo Polytechnic University, Atsugi 2430297, Japan
Shuyang Cao:State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
Abstract
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.
Abstract
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 (Cpe) 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 (Cpe) 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.