A simplified methodology is rigorously studied in this article to analyze the modal properties of base-isolated high-rise structures with dynamic soil-structure interaction being considered. The proposed methodology is developed based on a more reasonable 2-degree-of-freedom model and the existing simplified methodology which is only applicable for nonisolated structures. The base-isolated structure model with 2 degrees of freedom is supported by swaying and rocking springs and by the corresponding dashpots. Rigorous mathematical derivation is performed, and closed-form formulas of natural periods, modes, and modal damping ratios are derived. Furthermore, the overall accuracy of the proposed methodology was checked against the results of the rigorously derived complex eigenvalue approach proposed by Constantinou and Kneifati. A parametric study is also conducted on the soil-structure interaction effects of baseisolated structures, which indicates that tall and slender structures with stiff isolation systems are more affected by soilstructure interaction effects in comparison to flexible superstructures. The proposed method provides a feasible way to evaluate the soil-structure interaction effects of base-isolated structures efficiently during the schematic design phase.

Real-time structural damage detection is one of the main goals of establishing an effective structural health monitoring system. However, due to the lack of training data for possible damage patterns, supervised methods tend to be difficult for such applications. This article therefore proposes a novel unsupervised real-time damage detection method using machine learning, which consists of a statistical modeling approach using neural networks and a decision-making process using deep support vector domain description. To choose an optimal window length while extracting damage-sensitive features, an iterative training strategy is proposed to remove redundant samples from an oversized window. The proposed method is then verified using a simulated dataset from the International Association for Structural Control-American Society of Civil Engineering benchmark and an experimental dataset from shake table tests. The results show that the mean alarm density can be used as an indicator of damage existence and damage levels for the single-sensor approach. Higher performance of damage detection and lower performance of identifying damage levels are observed for the multi-sensor approach when the rotational modes are amplified by asymmetric damage patterns. The results of mean false alarm density show that the presented method has a low probability of generating false alarms. The effectiveness of iterative pruning strategy is observed through the visualization of loss function and weights in the neural networks. Finally, the capability of real-time execution of the proposed damage detection method is investigated and verified. As a result, trained with healthy data only, the proposed method is effective in detecting damage existence and damage levels.

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