Prediction of Wind-Induced Mean Pressure Coefficients Using GMDH Neural Network

Mallick, Monalisa; Mohanta, Abinash; Kumar, Awadhesh; Patra, Kanhu Charan

doi:10.1061/(asce)as.1943-5525.0001101

Cited by 20 publications

(8 citation statements)

References 44 publications

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“…Li et al (2018) studied the effect of horizontal changes such as corner-cutting on highrise buildings [34]. Mallick and Mohanta (2019) carried out wind tunnel model experiments to feed GMDH neural network for developing equations for the prognosis of average external pressure coefficients on the facets of various C plan-shaped buildings [35]. Off late, artificial neural networks are adopted to present the average pressure coefficients on all building facets of setback high-rise buildings (Bairagi and Dalui, 2020) and crossplan-shaped high-rise buildings (Paul and Dalui, 2020) [36,37].…”

Section: Introductionmentioning

confidence: 99%

Reduced Frequency and Power Spectra of Wind Pressure of a Tall Building Having "Z" Shape in Plan

Paul¹,

Dalui

2022

Preprint

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This study envisages a tall building model's dynamic behaviour with a 'Z' shape in plan and exposed to cyclonic wind tested using numerical simulation technique. The computational Fluid Dynamics (CFD) simulation tool of ANSYS-CFX is adopted in the present study. The geometric scale is considered as 1:300, whereas the velocity scale is taken to be 1:5. Wind streamlines under different wind occurrence angles on the building are displayed perceptibly, ensuring vortex shedding at the region of disturbed flow where the wind suction and pressure amalgamates. The peak wind pressures and suctions at building facets are presented under different wind angles. Again the features of normalized power spectra of wind pressure at the top part, main body, and bottom part of the building subjected to wind azimuths ranging from 0° to 150° are presented. It is found that at the bottom part of the building, the peak values of standardized power spectra occur in the low-frequency range, although, at the top part and main body of the building, the vortex shedding energy intensifies at relatively high reduced frequencies. The determination of reduced frequency is of paramount importance to evade resonance under wind tempted shakings of soaring buildings.

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Section: Introductionmentioning

confidence: 99%

Reduced Frequency and Power Spectra of Wind Pressure of a Tall Building Having "Z" Shape in Plan

Paul¹,

Dalui

2022

Preprint

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“…This study reviewed the relevant literature on wind velocity prediction models using DL-based and MLbased approaches, because they exhibit high calculation efficiency and accurate prediction ability (Dongmei et al, 2017;Mallick et al, 2020;Panapakidis et al, 2019;Sheela & Deepa, 2013;Wei, 2014Wei, , 2015. In addition, several popular approaches are investigated such as autoregressive integrated moving average (Cadenas & Rivera, 2010;Cadenas et al, 2016), support vector machine (Chou et al, 2020;Wei, 2017), random forest (Kim et al, 2019), radial basis function (Noorollahi et al, 2016), and neural networks (Chen et al, 2018;Huang et al, 2018a;Hu et al, 2016;Wei, 2019).…”

Section: Introductionmentioning

confidence: 99%

Collapse Warning System Using LSTM Neural Networks for Construction Disaster Prevention in Extreme Wind Weather

Wei

2021

Journal of Civil Engineering and Management

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Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.

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“…Kutluay et al 15 estimated aerodynamic constants of one-shot autonomous vehicle by flight tests using artificial neural networks (ANN). Mallick et al 16 predicted pressure coefficients of the c-shaped buildings using group method of data handling neural network. Comprehensive experimental study in subsonic wind tunnel is carried out to identify input–output relationship.…”

Section: Introductionmentioning

confidence: 99%

Development of a method for maximum structural response prediction of a store externally carried by a jet fighter

Kaplan

Acar

Özer

2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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Experimental structural response of equipment mounted in store carried externally by jet type aircraft is investigated, predicted and compared with responses suggested by military standards in this study. A representative store which is similar to Mark-83 warhead with guidance units in terms of mass and geometry is used in this study. The main scope of this study is to evaluate the structural response proposed by military standards with real test conditions and also suggest a new method with an artificial neural network to predict the maximum response. Seventy-five different flight conditions are used to train the network for low and high frequency components. Also, eight flight conditions apart from the training set of flight conditions are used to test the approach. Acceleration levels are collected in real flight conditions by the data storage system. In signal processing, vibration response is expressed as power spectral density functions in the frequency domain. Procedures to predict the maximum response from measurements are determined with statistical limits in the literature. Besides the well-known limits in literature, third-order polynomial normal and logarithmic transform is used, and the performance of the different limits is compared. It is found that the military standard vibration spectrum is conservative. Distribution-free and normal tolerance limits predicted low frequency acceleration spectral density magnitudes more accurately. Their prediction performances were better than those of the other tolerance limits and that of the military standard. Third-order polynomial transform predictions are found to be reasonable with respect to normal prediction limit and envelope approach. Finally, it can be concluded that the response prediction method proposed in this article works well for Mark-83 warheads with guidance unit carried externally by jet fighter.

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Prediction of Wind-Induced Mean Pressure Coefficients Using GMDH Neural Network

Cited by 20 publications

References 44 publications

Reduced Frequency and Power Spectra of Wind Pressure of a Tall Building Having "Z" Shape in Plan

Reduced Frequency and Power Spectra of Wind Pressure of a Tall Building Having "Z" Shape in Plan

Collapse Warning System Using LSTM Neural Networks for Construction Disaster Prevention in Extreme Wind Weather

Development of a method for maximum structural response prediction of a store externally carried by a jet fighter

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