Development and Experimental Verification of an Intelligent Isolation System Based on Long Short-Term Memory Module Model for Ground Motion Characteristics Prediction

Semiactive seismic control requires appropriate control laws that dictate the behavior of seismic suppression devices based on measurements and feedback during an earthquake. Optimal control parameters should be determined in real time to achieve high performance. The initial ground motion characteristics of an earthquake substantially affect control performance. In this study, a genetic algorithm (GA)-optimized long short-term memory (LSTM)-based intelligent control system (hereafter denoted GA-LSTM system) for obtaining optimal control parameters for a semiactive variable-stiffness isolation system was proposed. First, the LSTM module classifies earthquakes as near-fault or far-field events to determine the optimal control strategy. Second, earthquakes from a global database are analyzed to determine a fuzzy inference surface for the optimal parameters of the earthquake suppression system. Both numerical simulations and shaking table experimental results indicated that the proposed GA-LSTM system exhibited superior isolation displacement and superstructure acceleration suppression for both near-fault and far-field earthquakes when compared with other control techniques. The proposed intelligent control system is highly efficient and could reliably protect structures from earthquakes with any ground motion characteristics.