Predicting wind flow around buildings using deep learning

[1]  Cruz Y. Li,et al.  A perspective on the aerodynamics and aeroelasticity of tapering: Partial reattachment , 2021 .

[2]  Yiqing Xiao,et al.  Machine learning-based prediction of crosswind vibrations of rectangular cylinders , 2021, Journal of Wind Engineering and Industrial Aerodynamics.

[3]  Mohamed Hanafy,et al.  Machine Learning Approaches for Auto Insurance Big Data , 2021, Risks.

[4]  Joo-Hiuk Son,et al.  Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy , 2021, Sensors.

[5]  Michael C. Thrun,et al.  Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data , 2020, Journal of Classification.

[6]  Mehdi Seyedmahmoudian,et al.  Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values , 2020, Sensors.

[7]  Haesung Lee,et al.  Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation , 2020, Energies.

[8]  Hyo Seon Park,et al.  Multi-objective optimization of a structural link for a linked tall building system , 2020 .

[9]  Panagiotis Tsakalides,et al.  Semantic Predictive Coding with Arbitrated Generative Adversarial Networks , 2020, Mach. Learn. Knowl. Extr..

[10]  P. Keskinoğlu,et al.  Effect of Missing Data Imputation on Deep Learning Prediction Performance for Vesicoureteral Reflux and Recurrent Urinary Tract Infection Clinical Study , 2020, BioMed research international.

[11]  Dacheng Tao,et al.  Deep learning-based investigation of wind pressures on tall building under interference effects , 2020 .

[12]  Cyrus Rashtchian,et al.  Explainable k-Means and k-Medians Clustering , 2020, ICML.

[13]  T. Harman,et al.  Comparative metrics for computational approaches in non-uniform street-canyon flows , 2019, Building and Environment.

[14]  Hyo Seon Park,et al.  Convolutional neural network‐based wind‐induced response estimation model for tall buildings , 2019, Comput. Aided Civ. Infrastructure Eng..

[15]  K. Kwok,et al.  Particle image velocimetry measurement and CFD simulation of pedestrian level wind environment around U-type street canyon , 2019, Building and Environment.

[16]  Yukio Tamura,et al.  Statistical analysis of wind-induced pressure fields and PIV measurements on two buildings , 2019, Journal of Wind Engineering and Industrial Aerodynamics.

[17]  Hyo Seon Park,et al.  Investigation of flow visualization around linked tall buildings with circular sections , 2019, Building and Environment.

[18]  Nyuk Hien Wong,et al.  A parametric study of angular road patterns on pedestrian ventilation in high-density urban areas , 2019, Building and Environment.

[19]  Ashutosh Sharma,et al.  Numerical simulation of pedestrian level wind flow around buildings: Effect of corner modification and orientation , 2019, Journal of Building Engineering.

[20]  Gang Hu,et al.  Predicting wind pressures around circular cylinders using machine learning techniques , 2019, ArXiv.

[21]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[22]  Kam Tim Tse,et al.  POD analysis of aerodynamic correlations and wind-induced responses of two tall linked buildings , 2018, Engineering Structures.

[23]  Yukio Tamura,et al.  POD analysis for aerodynamic characteristics of tall linked buildings , 2018, Journal of Wind Engineering and Industrial Aerodynamics.

[24]  Mohammad R. Jahanshahi,et al.  NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion , 2018, IEEE Transactions on Industrial Electronics.

[25]  Hui Li,et al.  Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder , 2018 .

[26]  Ashutosh Sharma,et al.  A review on the study of urban wind at the pedestrian level around buildings , 2018, Journal of Building Engineering.

[27]  A. U. Weerasuriya,et al.  New inflow boundary conditions for modeling twisted wind profiles in CFD simulation for evaluating the pedestrian-level wind field near an isolated building , 2018, Building and Environment.

[28]  Juan M. Gimenez,et al.  Prediction of wind pressure coefficients on building surfaces using artificial neural networks , 2018 .

[29]  Yuguo Li,et al.  Wind driven natural ventilation in the idealized building block arrays with multiple urban morphologies and unique package building density , 2017 .

[30]  Zhu Xue,et al.  Prediction of wind loads on high-rise building using a BP neural network combined with POD , 2017 .

[31]  Kenny C. S Kwok,et al.  New criteria for assessing low wind environment at pedestrian level in Hong Kong , 2017 .

[32]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[33]  Zhang Lin,et al.  Evaluation of pedestrian wind comfort near ‘lift-up’ buildings with different aspect ratios and central core modifications , 2017, Building and Environment.

[34]  Yukio Tamura,et al.  Characteristics of pedestrian-level wind around super-tall buildings with various configurations , 2017 .

[35]  Kenny C. S Kwok,et al.  Adopting ‘lift-up’ building design to improve the surrounding pedestrian-level wind environment , 2017, Building and Environment.

[36]  Jesper Wulff,et al.  Multiple imputation by chained equations in praxis: Guidelines and review , 2017 .

[37]  Mengxuan Song,et al.  A combined AR-kNN model for short-term wind speed forecasting , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[38]  Yi-Qing Ni,et al.  Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN , 2016 .

[39]  Bje Bert Blocken,et al.  Pedestrian-level wind conditions around buildings: Review of wind-tunnel and CFD techniques and their accuracy for wind comfort assessment , 2016 .

[40]  A.L.S. Chan,et al.  Pedestrian level wind environment assessment around group of high-rise cross-shaped buildings: Effect of building shape, separation and orientation , 2016, Building and Environment.

[41]  Ahsan Kareem,et al.  Aerodynamics of closely spaced buildings: With application to linked buildings , 2016 .

[42]  Kyung Hwa Cho,et al.  Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation , 2015 .

[43]  Bje Bert Blocken,et al.  Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations , 2015 .

[44]  Bert Blocken,et al.  50 years of Computational Wind Engineering: Past, present and future , 2014 .

[45]  Mats Sandberg,et al.  The influence of building height variability on pollutant dispersion and pedestrian ventilation in idealized high-rise urban areas , 2012 .

[46]  Bert Blocken,et al.  Ten iterative steps for model development and evaluation applied to Computational Fluid Dynamics for Environmental Fluid Mechanics , 2012, Environ. Model. Softw..

[47]  Sandrine Aubrun,et al.  Sand erosion technique applied to wind resource assessment , 2012 .

[48]  Bert Blocken,et al.  CFD simulation for pedestrian wind comfort and wind safety in urban areas: General decision framework and case study for the Eindhoven University campus , 2012, Environ. Model. Softw..

[49]  Kenny C. S Kwok,et al.  Wind tunnel study of pedestrian level wind environment around tall buildings: Effects of building dimensions, separation and podium , 2012 .

[50]  Patrick Royston,et al.  Multiple Imputation by Chained Equations (MICE): Implementation in Stata , 2011 .

[51]  Patrick Royston,et al.  Multiple imputation using chained equations: Issues and guidance for practice , 2011, Statistics in medicine.

[52]  Jörg Franke,et al.  The COST 732 Best Practice Guideline for CFD simulation of flows in the urban environment: a summary , 2011 .

[53]  Qiusheng Li,et al.  Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks , 2007 .

[54]  Khaldoon A. Bani-Hani,et al.  Vibration control of wind‐induced response of tall buildings with an active tuned mass damper using neural networks , 2007 .

[55]  David Surry,et al.  Prediction of pressure coefficients on roofs of low buildings using artificial neural networks , 2003 .

[56]  Kit Ming Lam,et al.  Evaluation of pedestrian-level wind environment around a row of tall buildings using a quartile-level wind speed descripter , 1995 .

[57]  Frank H. Durgin,et al.  Pedestrian level wind studies at the Wright brothers facility , 1992 .

[58]  Yasushi Uematsu,et al.  Effects of the corner shape of high-rise buildings on the pedestrian-level wind environment with consideration for mean and fluctuating wind speeds , 1992 .

[59]  Theodore Stathopoulos,et al.  Wind environmental conditions around tall buildings with chamfered corners , 1985 .

[60]  H.P.A.H. Irwin,et al.  A simple omnidirectional sensor for wind-tunnel studies of pedestrian-level winds , 1981 .

[61]  A. Wise,et al.  Wind effects due to groups of buildings , 1970 .