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<p>Wind pressure acting on building envelopes often demonstrates extreme peak values and high-frequency fluctuations, posing significant challenges for structural safety assessment. This study proposes a wind pressure time series prediction framework, termed C-WGAN-timesplit, that combines consistency constraints, Wasserstein generative adversarial networks (WGAN), and a time-series cross-validation strategy. C-WGAN-timesplit predicts the full pressure field using data from sparse pressure taps through learning the mapping from subset sensors to the full pressure field. The performance of C-WGAN-timesplit was evaluated and compared against generative adversarial networks (GAN) and WGAN using boundary layer wind tunnel test pressure data. Results across multiple evaluation metrics confirm that C-WGAN-timesplit achieves superior predictive accuracy under various wind incidence angles. Comprehensive analyses were conducted across input data volume sensitivity and incidence angles through aerodynamic coefficients, pressure statistics, non-Gaussian characteristics, and local pressure time series. The proposed framework achieved accurate mean pressure prediction using only 1 tap (0.25% coverage), reliable fluctuation prediction with 20 taps (5%), and high-fidelity reconstruction of non-Gaussian pressure characteristics with 40 taps (10%). Furthermore, three representative taps exhibiting pronounced non-Gaussian behavior under typical incidence angles were used to assess time-series prediction performance. The model effectively captured fluctuation features under both 20-tap and 40-tap configurations, though some deviations remained in predicting rare extreme values. Overall, the proposed method provides a promising solution for wind load prediction on building surfaces, with future efforts needed to improve the representation of transient extreme events.</p>

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Deep generative framework for predicting non-Gaussian wind pressure on a high-rise building using data from sparse sensors

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Zhixin Liu,
  • Haotian Dong,
  • Yu Zhang,
  • Xiaoqing Du

摘要

Wind pressure acting on building envelopes often demonstrates extreme peak values and high-frequency fluctuations, posing significant challenges for structural safety assessment. This study proposes a wind pressure time series prediction framework, termed C-WGAN-timesplit, that combines consistency constraints, Wasserstein generative adversarial networks (WGAN), and a time-series cross-validation strategy. C-WGAN-timesplit predicts the full pressure field using data from sparse pressure taps through learning the mapping from subset sensors to the full pressure field. The performance of C-WGAN-timesplit was evaluated and compared against generative adversarial networks (GAN) and WGAN using boundary layer wind tunnel test pressure data. Results across multiple evaluation metrics confirm that C-WGAN-timesplit achieves superior predictive accuracy under various wind incidence angles. Comprehensive analyses were conducted across input data volume sensitivity and incidence angles through aerodynamic coefficients, pressure statistics, non-Gaussian characteristics, and local pressure time series. The proposed framework achieved accurate mean pressure prediction using only 1 tap (0.25% coverage), reliable fluctuation prediction with 20 taps (5%), and high-fidelity reconstruction of non-Gaussian pressure characteristics with 40 taps (10%). Furthermore, three representative taps exhibiting pronounced non-Gaussian behavior under typical incidence angles were used to assess time-series prediction performance. The model effectively captured fluctuation features under both 20-tap and 40-tap configurations, though some deviations remained in predicting rare extreme values. Overall, the proposed method provides a promising solution for wind load prediction on building surfaces, with future efforts needed to improve the representation of transient extreme events.