Integrating adaptive Kriging with expansion optimal linear estimation into real-time hybrid simulation for time-variant experimental analysis of structures with deterioration. (1st April 2023)

• Record Type: Journal Article
• Authors: Yu, Guangquan; Chen, Cheng; Hou, Hetao; Peng, Changle; Zhang, Rui
• Is Part Of: Journal of building engineering. Volume 64(2023)
• Publication Date: 2023-04-01
• Subjects: Real-time hybrid simulation -- Kriging -- Meta-modeling -- Transfer learning -- Time variant -- Deterioration; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.105658
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Seismic response evaluation often poses challenges for computational simulation when parts of structures are difficult for accurate modeling due to their complexity or nonlinearity. Failure to accurately replicate their behavior might lead to errors in response estimation for traditional computational simulation and reliability analysis when uncertainties need to be considered for the structure under investigation. Real-time hybrid simulation (RTHS) provides an efficient experimental technique for performance evaluation of large- or full-scale structures in size limited laboratories. Components that are difficult for accurate modeling are physically tested in the laboratory to enable system responses to be evaluated while the remaining parts of the structure are represented by numerical modeling. Traditional applications of RTHS often considers deterministic structural properties which however often vary with time. In this study, an innovative AK-TL-RTHS approach is proposed to integrate adaptive Kriging (AK) with transfer learning (TL) for RTHS to account for both time-dependent and independent uncertainties in response evaluation. The expansion optimal linear estimation (EOLE) is applied to discretize the stochastic process for time dependent structural deterioration. Kriging meta-model is utilized to surrogate measured structural responses from RTHS tests and a modified U-function from active learning is applied to sequentially select sample points for further RTHS tests. Selected samples and associated experimental results are transferred between random variables of discretized stochastic process to further reduce the number of tests in laboratory. RTHS of a single-degree-of-freedom (SDOF) structure with a self-centering viscous damper (SC-VD) is conducted as proof of concept to experimentally demonstrate the effectiveness of proposed AK-TL-RTHS method. It is shown that the proposed method provides an efficient and effective approach for time variant performance evaluation of structures in laboratory especially when pure numerical analysis is not feasible. The proposed approach also significantly enhances the capacity of RTHS to account for structural uncertainties and deterioration in seismic reliability analysis. Highlights: Real-time hybrid simulation is utilized for time variant experimental analysis of structure with deterioration. Adaptive Kriging is integrated with EOLE of random field discretization for meta-modeling of structural responses of quantities. The U learning equation and sample distance are combined to facilitate an effective balance of global search and local exploration for adaptive sampling strategy. Transfer learning is introduced to further reduce the number of learning samples for Kriging Meta-model without compromising accuracy. RTHS of SDOF structure with SC-VD are conducted to experimentally demonstrate the effectiveness of AK-TL-RTHS method.