Measurements of dynamic wind pressures on gable roofs – comparison with ČSN EN 1991-1-4

Pospı́šil, Stanislav; Macháček, Michael; Hračov, S.; Kuznetsov, Sergey; Trush, Arsenii

doi:10.1051/matecconf/201710700079

Cited by 1 publication

(1 citation statement)

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“…The conversion of the results from the tunnel to the real scale is done through the external pressure coefficient Cp, which can be expressed as the ratio of the wind pressure on the surface of the difference model (Dp -the differential pressure measured directly by the pressure transducer between the total pressure recorded at point i and the static pressure on the Prandtl tube) and the reference mean dynamic pressure in the free stream 𝑝 (Pospisil et al 2017):…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Estimating Wind Pressure on Complex Double-Curved Facades

Prokop,

Kubalík,

Kurilla

2023

eCAADe Proceedings

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Due to their complex geometry, it is challenging to assess wind effects on the freeform, double-curved building facades. The traditional building code EN 1991-1-4 (730035) only accounts for basic shapes such as cubes, spheres, and cylinders. Moreover, even though wind tunnel measurements are considered to be more precise than other methods, they are still limited by the number of measurement points that can be taken. This limitation, combined with the time and resources required for the analysis, can limit the ability to fully capture detailed wind effects on the whole complex freeform shape of the building. In this study, we propose the use of neural network models trained to predict wind pressure on complex double-curved facades. The neural network is a powerful datadriven machine learning technique that can, in theory, learn an approximation of any function from data, making it well-suited for this application. Our approach was empirically evaluated using a set of 31 points measured in the wind tunnel on a 3D printed model in 1:300 scale of the real architectural design of a concert hall in Ostrava. The results of this evaluation demonstrate the effectiveness of our neural network method in estimating wind pressures on complex freeform facades.

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Section: Introductionmentioning

confidence: 99%