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This study focuses on developing strength reduction factor R μ spectrum, a maximum inelastic displacement (MID) estimation method and a displacement-based design method for single-degree-of-freedom (SDOF) bilinear structures with self-centering viscous-hysteretic devices (SC-VHDs). The effects of design parameters on R μ spectrum are statistically investigated under near-fault pulse-like ground motions (NFPG) and far-field ground motions (FFG), and its prediction formula is fitted according to the statistical data. Following proposed R μ spectrum, a MID estimation method and a performance-based design method are proposed, respectively. Next, three SC-VHD frames are designed step-by-step to verify the proposed design method, and then their MIDs are calculated step-by-step to validate the MID estimation method. Finally, a risk assessment is conducted on a SC-VHD frame and a conventional frame to compare their collapse performance and the performance exceeding 0.2% residual drift in the probability framework. The results indicate that R μ spectrum is primarily affected by the ductility ratio, period and viscous damping ratio, while the parameters of ring springs have little effect on it. R μ spectrum under the NFPE is significantly lower than that under the FFE except for periods from 0.2 s to 0.6 s. The R μ spectrum formula and the MID estimation method both have errors that are typically less than 10%. The designed SC-VHD frame can achieve the target performance, and its probability of collapse and exceeding 0.2% residual drift in a period of 50-year is significantly smaller than that of the conventional frame. • Strength reduction factor spectrum is developed for self-centering viscous-hysteretic systems. • A maximum inelastic displacement estimation method is proposed. • A displacement-based design method is proposed. [ABSTRACT FROM AUTHOR]

This study focuses on developing probabilistic inelastic displacement ratio C R demand spectra for single-degree-of-freedom (SDOF) self-centering viscous damper (SC-VD) systems subjected to near-fault forward-directivity ground motions. In addition, this study presents a procedure for calculating the mean annual frequency (MAF) of the maximum inelastic displacement demand exceeding a specific displacement while considering the forward-directivity pulse effect to determine the maximum inelastic displacement demand hazard of the SC-VD structural system under near-fault forward-directivity ground motions. First, the effect of ground motion characteristics on C R demand is investigated. A probabilistic distribution model describing the probabilistic distribution of C R demand is then established. The effect of structural characteristics on key parameters in the probabilistic distribution of C R demand is investigated, and calculation models for key parameters are established. Based on the probabilistic distribution model and the calculation models for key parameters, the probabilistic C R demand spectra are developed. Finally, by combining the probabilistic C R demand spectra and the probabilistic seismic hazard analysis with the forward-directivity pulse effect, the procedure for calculating the MAF of the maximum inelastic displacement demand exceeding a specific displacement is presented to determine the maximum inelastic displacement demand hazard. The results indicate that the effects of ground motion characteristics should be treated as random variables, and that the Gamma distribution can reasonably describe the probabilistic distribution of C R demand. The proposed probabilistic C R demand spectra can reasonably estimate the actual C R demand, especially when R is large. Besides, the procedure for calculating the MAF of the maximum inelastic displacement demand exceeding a specific displacement is demonstrated to be effective in assessing the maximum inelastic displacement demand hazard through a set of SDOF SC-VD example systems. • A probabilistic distribution model of inelastic displacement C R demand is proposed. • The effect of structural characteristics on the probabilistic C R demand is studied. • A probabilistic C R demand spectrum under near-fault ground motions is developed. • A framework for calculating the inelastic displacement seismic hazard is proposed. [ABSTRACT FROM AUTHOR]

• A CAE-based unsupervised deep learning network is deployed for damage identification of a seismic-isolation structure. • Feature extraction performance of the network is investigated through real-time data training and earthquake verification. • A FE model is established to update the network and generate vibration datasets for test on structural diagnosis. • The test results reveal the relationship between the damage condition and the scoring indicators. Seismic-isolated systems have been investigated and practiced globally to prevent the destruction of existing structures, and vibration-based structural health monitoring is important for maintaining the functionality of the isolation systems. Given the recent boom in computational science and machine learning algorithms, this paper deployed a structural health monitoring system for a rubber bearing-isolated gymnasium in an area with high seismic fortification intensity and introduced an unsupervised deep learning network named convolutional autoencoder (CAE) to identify damage from vibrations of the isolation layer. The CAE network is first trained by operational monitoring vibrations and tested by new data. The vibration assessment results indicate that the CAE network can accurately reconstruct daily data and effectively detect unexpected ground motion. Thereafter, to further test the network's damage identification and localization performance, an analytical finite element model of the gymnasium structure is established, and the undamaged and damaged datasets are generated for network verification and updating. The verification results demonstrate that the CAE network leads to reliable feature extraction power in global and local damage detection of the isolation layer. The proposed vibration assessment method is beneficial to building administrators in making accurate and timely decisions with the assistance of the score result provided by the CAE network. [ABSTRACT FROM AUTHOR]