Low-rise gable roof buildings pressure prediction using deep neural networks

Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach

“…These are significant differences which infer that the mere comparison of outcomes without considering their surrounding implementation environment should not be the only approach to invoke ultimate conclusions about the performance achieved in this study. Moreover, Several error metrics used to evaluate the prediction accuracy such as RMSE, Mean-Square Error (MSE) and correlation coefficients (R), and there is not a consensus on a common error metric which make cross-reference difficult to conduct (Tian et al, 2020). Nevertheless, despite these general challenges and the peculiarities of this study's prediction, the precision levels attained in this paper were found to be comparable to those in 'similar field'.…”

A framework of data-driven wind pressure predictions on bluff bodies using a hybrid deep learning approach

“…25 Besides, The DNN has performed precisely a prediction in mean and peak wind pressure coefficients on the surface of a scale model building. 17…”

“…Snaiki and Wu 16 have presented a knowledge-enhanced deep learning model to simulate the wind field inside a tropical cyclone boundary-layer based on the storm parameters. Tian et al 17 have used a deep neural network to predict mean and peak wind pressure coefficients on the surface of a scale model building. Furthermore, to improve predicted accuracy in peak coefficients, wind pressure data from various wind directions and terrain roughness was fed into a two-step nested deep neural network procedure, and the result indicated that the CNN presented a higher accuracy.…”

Automation and New Capabilities in the University of Florida NHERI Boundary Layer Wind Tunnel

“…The complete aerodynamic BLWT dataset is publicly accessible in the DesignSafe-CI repository Masters, 2018b, 2020). The data can be re-used to validate computational fluid dynamic models (e.g., LES) and train machine learning algorithms such as artificial neural networks (ANN; see Figure 7) to develop analytical tools for predicting wind-induced loads on low-rise structures (Fernández-Cabán et al, 2018;Tian et al, 2020).…”