23 Papers
34 Citations
Gang Hu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Wind tunnel & Computer science. The author has an hindex of 5, co-authored 23 publications. Previous affiliations of Gang Hu include University of Sydney.
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Papers
Deep learning-based investigation of wind pressures on tall building under interference effects
TL;DR: In this article, the GANs model exhibited the best performance in predicting wind pressure coefficients on the principal building and was used to resolve the conflicting requirement between limited wind tunnel tests and a completed investigation of the interference effects that is costly.
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A Data-Driven Framework for Tunnel Geological-Type Prediction Based on TBM Operating Data
TL;DR: A data-driven framework for real-time interpreting the operating data of tunnel boring machines (TBMs) without interrupting tunneling operations, and eventually automate the tunneling operation.
Machine learning-based prediction of crosswind vibrations of rectangular cylinders
TL;DR: It was found that the GBRT model can be an effective and economical method to study crosswind vibrations of rectangular cylinders and hence supplement traditional wind tunnel tests and numerical simulation techniques.
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Pressure pattern recognition in buildings using an unsupervised machine-learning algorithm
TL;DR: It is demonstrated that clustering algorithms are a powerful tool for recognizing patterns hidden in complex pressure fields and flow fields, and a promising machine-learning technique is proposed that can perfectly complement traditional building methods using wind engineering.
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Twisted-wind effect on the flow field of tall building
TL;DR: In this paper, the effects of three wind profiles on second-generation benchmark building, i.e., conventional wind profile (CWP), two twisted wind profiles (TWP15 and TWP30) with maximum twisted angle (MTA) of 15°and 30°, are investigated by large eddy simulation.
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