A comparative analytical and experimental study of several algorithms for the control of seismically excited floor- and base-isolated structures is pursued in the current study. A hybrid isolation system that is comprised of a bidirectional roller–pendulum system (RPS) and augmented by controllable magnetorheological (MR) dampers is proposed to reduce the potential for damage to structures and sensitive equipment. Bidirectional motions are intelligently ameliorated in real time by the modulation of MR damper resistance. A Bouc–Wen model is adopted in numerical and experimental trials to predict behavior of the MR dampers. Three contrasting control techniques are examined. They include neural network control, LQR/clipped optimal control with variable gains and fuzzy logic control. Each control scheme is a candidate for mitigating the response of a superstructure to near- and far-field seismic loadings. Minimization of displacement and acceleration responses of the structure are considered in the formulation of each approach to control. Results of the numerical and large-scale experimental efforts reveal that the response of the isolated structure is effectively alleviated by all of the considered control methods, although they do not perform equally well. The LQR/clipped optimal controller with variable gains is superior to the other controllers in 50% of the investigated cases, while the fuzzy logic controller performs well for earthquakes with large accelerations. Neural network control is found to be effective in minimizing the acceleration of the superstructure that is subject to moderate excitation.

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This study proposes an information-theoretic structural health monitoring (SHM) system based on multi-scale entropy (MSE) and multi-scale cross-sample entropy (MSCE). By measuring the ambient vibration signal from a structure, the damage condition can be rapidly evaluated via MSE analysis. The damage location can then be detected by analyzing the signals of different floors under the same damage condition via MSCE analysis. Moreover, a damage index is proposed to efficiently quantify the SHM process. Unlike some existing SHM methods, no experimental database or numerical model is required. Instead, a reference measurement of the current stage can initiate and launch the SHM system. A numerical simulation of a four-story steel structure is used to verify that the damage location and condition can be detected by the proposed SHM algorithm, and the location can be efficiently quantified by the damage index. A seven-story scaled-down benchmark structure is then employed for experimental verification. Based on the results, the damage condition can be correctly assessed, and average accuracy rates of 63.4 and 86.6% for the damage location can be achieved using the MSCE and damage index methods, respectively. As only the ambient vibration signal is required with a set of initial reference measurements, the proposed SHM system can be implemented practically with low cost.

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The aim of this study was to develop an entropy-based structural health monitoring system for solving the problem of unstable entropy values observed when multiscale cross-sample entropy (MSCE) is employed to assess damage in real structures. Composite MSCE was utilized to enhance the reliability of entropy values on every scale. Additionally, the first mode of a structure was extracted using ensemble empirical mode decomposition to conduct entropy analysis and evaluate the accuracy of damage assessment. A seven-story model was created to validate the efficiency of the proposed method and the damage index. Subsequently, an experiment was conducted on a seven-story steel benchmark structure including 15 damaged cases to compare the numerical and experimental models. A confusion matrix was applied to classify the results and evaluate the performance over three indices: accuracy, precision, and recall. The results revealed the feasibility of the modified structural health monitoring system and demonstrated its potential in the field of long-term monitoring.