Deep learning for seismic structural monitoring by accounting for mechanics-based model uncertainty

• Published in: Journal of Building Engineering 2022-10, Vol.57, p.104837, Article 104837
• Main Authors: Cheraghzade, Milad, Roohi, Milad
• Format: Article
• Language: English
• Subjects: Deep learning; Design of experiment; Post-earthquake assessment; Structural monitoring; Uncertainty analysis
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2022.104837

This paper presents a hybrid deep learning methodology for seismic structural monitoring, damage detection, and localization of instrumented buildings. The proposed methodology develops mechanics-based structural models to generate sample response datasets by accounting for the uncertainty of model parameters that can highly affect the estimation of baseline model nonlinear responses. The uncertainty of model parameters is evaluated through the design of experiments methodology by employing the central composite design for sampling. The generated sample response dataset is utilized for training a hybrid data-driven model that combines a convolutional neural network and wavelet packet transform modules for feature extraction. The global story-level noise-contaminated response measurements are used as input for the data-driven model to perform damage detection and localization in a manner consistent with performance-based design criteria. The performance of the proposed methodology is studied in the context of numerical and experimental case studies developed based on the shake table testing of a concentrically braced frame subject to various input ground motion intensities at the E-Defense facility in Miki, Japan. •A hybrid deep learning methodology is proposed for seismic structural monitoring and assessment of instrumented buildings.•The methodology develops mechanics-based models by accounting for the modeling parameters' uncertainty.•Hybrid deep learning is performed for feature extraction and subsequent damage detection and localization.•The methodology is validated in the context of case studies developed based on a shake table test at the E-Defense, Japan.•The capability of the proposed methodology to localize various damages is demonstrated using global dynamic measurements.