23 publications from this institution
For the purpose of field inspection and maintenance of transmission towers, computer vision-based damage identification and structural analysis method is propos
This paper proposes a fractal-based technique for simulating multivariate nonstationary wind fields by the stochastic Weierstrass Mandelbrot function. Upon conducting a systematic fractal analysis, it was found that the structure function method is more suitable and reliable than the box counting method, variation method, and R/S analysis method for estimating the fractal dimension of the stochastic wind speed series. Wind field measurement at the meteorological gradient tower with a height of 356 m in Shenzhen was conducted during Typhoon Mandelbrot (1983). Significant non-stationary properties and fractal dimensions of typhoon wind speed data at various heights were analyzed and used to demonstrate the effectiveness of the proposed multivariate typhoon wind speed simulation method. The multivariate wind speed components simulated by the proposed fractal-based method are in good agreement with the measured records in terms of the fractal dimension, standard deviation, probability density function, wind spectrum and cross-correlation coefficient.
The time-varying mean (TVM) component has a major influence on the characterization of nonstationary wind events and associated parameters of the fluctuating components. However, it is challenging to determine the optimal TVM of the nonstationary wind speed accurately due to variability in the low-frequency signal contents. To address this challenge, this study develops an effective wavelet-based method together with necessary conditions to calculate the optimal TVM from nonstationary wind speed data. For comparison, the time-dependent memory method is also used and enhanced to obtain the optimal TVM. Wind field turbulence characteristics of Typhoon Mangkhut are then analyzed and presented based on the conventional stationary model and nonstationary model with the proposed optimal TVM. In addition, this study also proposes an empirical description of the wind spectrum, which provides a better fit to the typhoon-related nonstationary winds than those offered by classical wind spectral description.
<strong class="journal-contentHeaderColor">Abstract.</strong> This paper proposes a fractal-based technique for simulating multivariate nonstationary wind speed fields by the stochastic Weierstrass Mandelbrot function. Upon conducting a systematic fractal analysis, it was found that the structure function method is more suitable and reliable than the box counting method, variation method, and R/S analysis method for estimating the fractal dimension of the stochastic wind speed series. Wind field measurement at the meteorological gradient tower with a height of 356 m in Shenzhen was conducted during Typhoon Mangkhut (2018). Significant non-stationary properties and fractal dimensions of typhoon wind speed data at various heights were analyzed and used to demonstrate the effectiveness of the proposed multivariate typhoon wind speed simulation method. The multivariate wind speed components simulated by the proposed fractal-based method are in good agreement with the measured records in terms of the fractal dimension, standard deviation, probability density function, wind spectrum and cross-correlation coefficient.
Flexible photovoltaic (PV) support structures are widely used due to their large span, high land-use efficiency, low construction cost, and short construction periods. However, they exhibit low stiffness, light weight, and low damping, making them wind-sensitive and prone to wind-induced vibrations. Evaluating their dynamic performance remains challenging due to two critical limitations: the lack of field-measured modal property and the absence of reliably validated finite element (FE) models. In this study, a field modal testing of flexible PV support structure was conducted, and high-order modal properties were identified from multi-sensor data. Subsequently, response surface model was constructed, and the optimal combination of metal frame mass, cable initial tension, and column modeling was obtained through particle swarm optimization (PSO), leading to an updated FE model. The results show that the damping ratios of the first and second torsional modes is only 0.7% and 0.4%, respectively, highlighting the need to consider low damping properties. Besides, the deviation between the design and actual values of structural parameters cannot be ignored.
Despite the substantial advancements made over the past 50 years in solving flow problems using numerical discretization of the Navier–Stokes (NS) equations, seamlessly integrating noisy data into existing algorithms remains a challenge. In addition, mesh generation is intricate, and addressing high-dimensional problems governed by parameterized NS equations is difficult. The resolution of inverse flow problems is notably resource-intensive, often necessitating complex formulations and the development of new computational codes. To address these challenges, a physics-informed neural network (PINN) has been proposed to seamlessly integrate data and mathematical models. This innovative approach has emerged as a multi-task learning framework, where a neural network is tasked with fitting observational data while reducing the residuals of partial differential equations (PDEs). This study offers a comprehensive review of the literature on the application of PINNs in solving two-dimensional and three-dimensional NS equations in structural wind engineering. While PINN has demonstrated efficacy in many applications, significant potential remains for further advancements in solving NS equations in structural wind engineering. This work discusses important areas requiring improvement, such as addressing theoretical limitations, refining implementation processes, and improving data integration strategies. These improvements are essential for the continued success and evolution of PINN in computational fluid dynamics.
Flexible photovoltaic (PV) support structures are widely used due to their large span, high land-use efficiency, low construction cost, and short construction periods. However, they exhibit low stiffness, light weight, and low damping, making them wind-sensitive and prone to wind-induced vibrations. Evaluating their dynamic performance remains challenging due to two critical limitations: the lack of field-measured modal properties and the absence of reliably validated finite element (FE) models. In this study, field modal testing of a flexible PV support structure was conducted, and high-order modal properties were identified from multi-sensor data. Subsequently, a response surface model was constructed, and the optimal combination of metal frame mass, cable initial tension, and column modeling was obtained through particle swarm optimization (PSO), leading to an updated FE model. The results show that the damping ratios of the first and second torsional modes is only 0.7% and 0.4%, respectively, highlighting the need to consider low damping properties. Besides, the deviation between the design and actual values of structural parameters cannot be ignored.
Although widely used in various fields due to its powerful capability of signal processing, empirical mode decomposition has to decompose signals separately, which limits its application for multivariate data such as the structural monitoring data recorded by multiple sensors. In order to avoid this shortcoming, a multivariate extension of empirical mode decomposition is proposed to deal with the multidimensional signals synchronously by employing a real-valued projection on hyperspheres. This study presents a hybrid modal identification method combining the multivariate empirical mode decomposition with stochastic subspace identification and fast Bayesian FFT methods to more conveniently and accurately identify structural dynamic parameters from multi-sensor vibration measurements. Deployed as a preprocessing tool, the multivariate signals are decomposed into several aligned intrinsic mode functions, which contain only a dominant component in the frequency domain. Then, the modal parameters can be identified by advanced fast Bayesian FFT and stochastic subspace identification directly. The combined method is first validated by a numerical illustration of a frame structure and then is applied in a shaking table test and a full-scale measurement under nonstationary earthquake excitation. Compared with the finite element method, the peak–pick, the half-power bandwidth methods, and Hilbert–Huang transform method, the results show that this hybrid method is more robust and reliable in the modal parameters identification. The main contribution of this paper is to develop a more effective integrated approach for accurate modal identification with the output-only multi-dimensional nonstationary signal.
This paper introduces an approach designed to address an inadequacy of Taylor's frozen hypothesis in determining the scaling exponent of structure functions for nonstationary wind speeds. The key step of this approach is to substitute the time-varying mean (TVM) components U¯(t) for the constant mean U¯ in the calculation of structure functions of nonstationary wind speed fields. In this approach, TVM components of nonstationary wind speeds were first determined based on the advanced wavelet transform and empirical mode decomposition. Subsequently, a comprehensive comparison was conducted for the calculation of scaling exponents of nonstationary wind speed records measured during Typhoon and Downburst events, utilizing different approaches. Our analysis results reveal that various models, including the K41 model [A. N. Kolmogorov, “The local structure of turbulence in incompressible viscous fluid for very large Reynolds' numbers,” Proc. USSR Acad. Sci. 30, 301–305 (1941), available at https://scispace.com/papers/the-local-structure-of-turbulence-in-incompressible-viscous-25te3acxv9], K62 model [A. Kolmogorov, “A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number,” J. Fluid Mech. 13(1), 82–85 (1962)], β model [Frisch et al., “A simple dynamical model of intermittent fully developed turbulence,” J. Fluid Mech. 87(4), 719–736 (1978)], and SL model [She and Leveque, Universal scaling laws in fully developed turbulence,” Phys. Rev. Lett. 72(3), 336–339 (1994)], have their own respective strengths and limitations in describing the relationship between the scaling exponent ξp and the order p. Significant differences in scaling exponents were observed between the original and new versions of Taylor's hypothesis, particularly at higher orders of p or for wind speed with strong nonstationarity such as the downburst. It is suggested that the Taylor's frozen hypothesis needs to be adjusted when computing the scaling exponents ξp of nonstationary wind speed fields in future studies.
On May 18, 2021, occupants in Saige Plaza Building felt significant building motions together with its roof masts caught in obvious vibrations (May 18 vibration event), which were recorded by a surveillance camera installed on the roof of the building. This study aims to investigate the vibration characteristics of masts by processing the video data using computer vision technique. A motion adaptive vision-based vibration measurement method (M-DAVIM) is firstly proposed, focusing on dealing with the adverse motion effects of the camera itself on measuring dynamic displacements. The M-DAVIM incorporates time-domain and frequency domain correction procedures to reduce noise caused by camera self-vibration, and utilizes a CNN-based object tracking method to search objects that blurred by camera shaking. Indoor periodic vibration tests and field tests of photovoltaic panels demonstrated that M-DAVIM outperforms previous vision-based methods in accurately measuring displacements under unfavorable conditions, such as target rotation, motion blurring, and background interference. The proposed M-DAVIM was then applied to measure the dynamic displacements of the roof masts of Saige Building and identify their modal parameters (frequencies and damping ratios) based on the limited video data. A vibration component of 7.60 Hz was identified as the camera self-vibration and was effectively corrected by the M-DAVIM method. Based on finite element analysis and given the wind condition, the twin-mast might mainly experience the vortex-induced vibration at 2.12 Hz with two masts vibrating synchronously in-plane along opposite directions during May 18 to 22, 2021. This study demonstrates the robustness and effectiveness of the M-DAVIM and shows its potential application for long-term field monitoring of large-scale structures under severe outdoor environments.
The time-varying mean (TVM) component plays a vital role in the characterization of non-stationary winds, whereas it is difficult to extract the TVM accurately or to validate it quantitively. To deal with this problem, this paper first develops two additional conditions for the TVM extraction from the perspective of structural wind-induced vibration response, then presents an approach, based on the combination of Vondrak filter and genetic algorithm (Vondrak-G), to derive the optimal TVM from non-stationary wind speed records as well as its turbulence characteristics (i.e. gust factor, turbulence intensity, and turbulence integral length scale). Furthermore, the wind characteristics obtained by the Vondrak-G approach are compared with those by a conventional approach derived for stationary winds, demonstrating that the results by the Vondrak-G approach are evidently more accurate. This paper aims to provide an effective method for accurately extracting the TVM and then evaluating wind characteristics of the non-stationary wind.
<strong class="journal-contentHeaderColor">Abstract.</strong> This paper proposes a fractal-based technique for simulating multivariate nonstationary wind speed fields by the stochastic Weierstrass Mandelbrot function. Upon conducting a systematic fractal analysis, it was found that the structure function method is more suitable and reliable than the box counting method, variation method, and R/S analysis method for estimating the fractal dimension of the stochastic wind speed series. Wind field measurement at the meteorological gradient tower with a height of 356 m in Shenzhen was conducted during Typhoon Mangkhut (2018). Significant non-stationary properties and fractal dimensions of typhoon wind speed data at various heights were analyzed and used to demonstrate the effectiveness of the proposed multivariate typhoon wind speed simulation method. The multivariate wind speed components simulated by the proposed fractal-based method are in good agreement with the measured records in terms of the fractal dimension, standard deviation, probability density function, wind spectrum and cross-correlation coefficient.
In civil engineering, an accurate characterization of wind loads is fundamental to wind-resistant design and wind effect assessment on structures. Reliable estimation of wind field extremes remains challenging. Existing methods typically require large sample sizes and incur substantial time and economic costs, limiting their practical applicability. To address these issues, a concept of time-varying energy (abbreviated as TV energy) is proposed to guide the reconstruction of wind speed time histories of downburst events. First, field-measured nonstationary wind speed data during the downburst are utilized to examine how time-frequency characteristics of fluctuating wind affect structural responses, and to quantify the correlation of statistical characteristics in the time-frequency domain. Then, a lightweight algorithm combining a mathematical model and a machine learning is proposed for wind field reconstruction and extrema estimation of downburst fluctuating wind. The wind speed and the TV energy are first reconstructed by the Kriging-based sequence interpolation based on data at measurement points; the resulting reconstructed wind speed is referred to as primary wind speed. Then extrema model is proposed according to the extrema correlation between the TV energy and wind speed time series. The estimated expectation and variance are used for extrema estimation, adjusting the extrema and their occurrence time of the primary wind speed at unmeasured points. A 10-storey steel frame structure under field-measured downburst wind is employed to demonstrate the effectiveness of the proposed algorithm, thereby providing a more reliable method for nonstationary wind field reconstruction.
The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.
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