Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

[1]  Carlos Valencia,et al.  Copula autoregressive methodology for the simulation of wind speed and direction time series , 2018 .

[2]  X. W. Ye,et al.  SHM-based probabilistic representation of wind properties:statistical analysis and bivariate modeling , 2018 .

[3]  Yi Zhou,et al.  Effects of high winds on a long-span sea-crossing bridge based on structural health monitoring , 2018 .

[4]  Yan Yu,et al.  Investigation of vortex-induced vibration of a suspension bridge with two separated steel box girders based on field measurements , 2011 .

[5]  J. Beck,et al.  Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation , 2002 .

[6]  Nicholas P. Jones,et al.  A model for vortex-induced vibration analysis of long-span bridges , 2014 .

[7]  Rene de Jesus Romero-Troncoso,et al.  Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings , 2019, Engineering Structures.

[8]  Hui Li,et al.  A numerical and experimental hybrid approach for the investigation of aerodynamic forces on stay cables suffering from rain-wind induced vibration , 2010 .

[9]  J. Ou,et al.  Suppression of vortex-induced vibration of a circular cylinder using suction-based flow control , 2013 .

[10]  Yi-Qing Ni,et al.  Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower , 2009 .

[11]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[12]  Yi-Qing Ni,et al.  A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data , 2020 .

[13]  Billie F. Spencer,et al.  A monitoring-based approach for evaluating dynamic responses of riding vehicle on long-span bridge under strong winds , 2019 .

[14]  Hui Liu,et al.  An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system , 2015 .

[15]  Ricardo Nicolau Nassar Koury,et al.  Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system , 2018 .

[16]  John H G Macdonald,et al.  Two-degree-of-freedom inclined cable galloping: Part 2. Analysis and prevention for arbitrary frequency ratio , 2008 .

[17]  Yi-Qing Ni,et al.  Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data , 2018, Journal of Structural Engineering.

[18]  Mircea Grigoriu,et al.  Parameters identification of cable stayed footbridges using Bayesian inference , 2019, Meccanica.

[19]  Grzegorz Fusiek,et al.  Wind turbine lifetime extension decision-making based on structural health monitoring , 2019 .

[20]  Jie Wu,et al.  Dynamic performance evaluation of Shanghai Tower under winds based on full‐scale data , 2019, The Structural Design of Tall and Special Buildings.

[21]  Linren Zhou,et al.  Field monitoring and numerical simulation of the thermal actions of a supertall structure , 2017 .

[22]  Gao Fan,et al.  Lost data recovery for structural health monitoring based on convolutional neural networks , 2019, Structural Control and Health Monitoring.

[23]  Yi Yu,et al.  Adaptive Diffeomorphic Multiresolution Demons and Their Application to Same Modality Medical Image Registration with Large Deformation , 2018, Int. J. Biomed. Imaging.

[24]  X. Ye,et al.  Analysis and probabilistic modeling of wind characteristics of an arch bridge using structural health monitoring data during typhoons , 2017 .

[25]  Jun Hu,et al.  Markov chain Monte Carlo-based Bayesian model updating of a sailboat-shaped building using a parallel technique , 2019, Engineering Structures.

[26]  Sondipon Adhikari,et al.  Stochastic structural dynamic analysis using Bayesian emulators , 2013 .

[27]  Eulogio Pardo-Igúzquiza,et al.  Bayesian Inference of Spatial Covariance Parameters , 1999 .

[28]  Costas Papadimitriou,et al.  Accounting for amplitude of excitation in model updating through a hierarchical Bayesian approach: Application to a two-story reinforced concrete building , 2019, Mechanical Systems and Signal Processing.

[29]  Dimitri J. Mavriplis,et al.  Uncertainty Quantification in Viscous Hypersonic Flows using Gradient Information and Surrogate Modeling , 2011 .

[30]  Dong Xu,et al.  Analysis of measurement and simulation errors in structural system identification by observability techniques , 2017 .

[31]  Arunasis Chakraborty,et al.  Modified Hamiltonian Monte Carlo‐based Bayesian finite element model updating of steel truss bridge , 2020, Structural Control and Health Monitoring.

[32]  Hao Wang,et al.  Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model , 2019, Engineering Structures.

[33]  Xue P. Fan,et al.  Dynamic reliability prediction for the steel box girder based on multivariate Bayesian dynamic Gaussian copula model and SHM extreme stress data , 2020, Structural Control and Health Monitoring.

[34]  Emmanouil N. Anagnostou,et al.  Using a Bayesian Regression Approach on Dual-Model Windstorm Simulations to Improve Wind Speed Prediction , 2017 .

[35]  Fuyou Xu,et al.  Experimental Explorations of the Torsional Vortex-Induced Vibrations of a Bridge Deck , 2016 .

[36]  Yi-Qing Ni,et al.  Bayesian multi-task learning methodology for reconstruction of structural health monitoring data , 2018, Structural Health Monitoring.

[37]  Qing-shan Yang,et al.  Investigation of wind load on 1,000 m‐high super‐tall buildings based on HFFB tests , 2018 .

[38]  A. Marra,et al.  Bayesian model updating of historic masonry towers through dynamic experimental data , 2017 .

[39]  Wei-Xin Ren,et al.  An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions , 2017 .

[40]  Heung-Fai Lam,et al.  Identification of rail-sleeper-ballast system through time-domain Markov chain Monte Carlo-based Bayesian approach , 2017 .

[41]  Chao Chen,et al.  A hybrid statistical method to predict wind speed and wind power , 2010 .

[42]  Dalei Wang,et al.  Prediction analysis of vortex-induced vibration of long-span suspension bridge based on monitoring data , 2019, Journal of Wind Engineering and Industrial Aerodynamics.