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This paper reports a novel optimization strategy combined with artificial intelligence for parameters and location design of precision equipment and provides a broader view for traditional passive vibration isolation. It also considers "precision equipment-vibration isolators-thin plate foundation" as a composite passive vibration isolation system and the clamped thin plate. The vibration isolation system is considered as four-point support, and the displacement amplitude transmissibility from the thin plate to precision equipment is derived and based on the analysis of influencing factors of transmissibility; subsequently, multi-objective optimization of the composite system is performed. A novel swarm intelligence multi-objective optimization method—a multi-objective particle swarm optimization (MOPSO) algorithm is adopted in this paper which can achieve a global optimal solution and by selecting the desired solution from an equivalence relation, the whole Pareto set can be avoided. The maximum and variance of the four transmitted peak displacements are simultaneously considered as fitness functions, and the purpose is to reduce the amplitude of the multi-peak isolation system, and in the meantime, to allow the plate to be uniformly vibrated as far as possible. Moreover, the presented idea is validated numerically, and the location research of the precision equipment mounted on the plate is also conducted.

This paper reports a novel optimization strategy combined with artificial intelligence for parameters and location design of precision equipment and provides a broader view for traditional passive vibration isolation. It also considers "precision equipment-vibration isolators-thin plate foundation" as a composite passive vibration isolation system and the clamped thin plate. The vibration isolation system is considered as four-point support, and the displacement amplitude transmissibility from the thin plate to precision equipment is derived and based on the analysis of influencing factors of transmissibility; subsequently, multi-objective optimization of the composite system is performed. A novel swarm intelligence multi-objective optimization method—a multi-objective particle swarm optimization (MOPSO) algorithm is adopted in this paper which can achieve a global optimal solution and by selecting the desired solution from an equivalence relation, the whole Pareto set can be avoided. The maximum and variance of the four transmitted peak displacements are simultaneously considered as fitness functions, and the purpose is to reduce the amplitude of the multi-peak isolation system, and in the meantime, to allow the plate to be uniformly vibrated as far as possible. Moreover, the presented idea is validated numerically, and the location research of the precision equipment mounted on the plate is also conducted.

This paper reports a novel control strategy combined with artificial intelligence for wind-induced vibration control of a high-rise structure and also provides a broader idea for traditional structural vibration control. The fast Fourier transform based on decimation-in-time was used to optimize the waves with a weighted amplitude method, and the wind speed field was numerically generated according to a Davenport - type fluctuating wind speed spectrum. A second-generation benchmark structure was selected as the high-rise building model. Tuned mass damper (TMD) and active tuned mass damper (ATMD) served as the controller, and the linear- quadratic- Gaussian algorithm served as the active control algorithm for ATMD. Simultaneously, the particle swarm optimization algorithm was introduced, and the integral of the absolute value of the error based on the relative displacement of floors with regard to the ground level was defined as a performance index for optimizing. The numerical results reveal that both of the two proposed controllers have excellent capability in reducing wind-induced vibrations in high-rise buildings; moreover, the PSO-based ATMD performed better than PSO-based TMD.

This paper reports a novel control strategy combined with artificial intelligence for wind-induced vibration control of a high-rise structure and also provides a broader idea for traditional structural vibration control. The fast Fourier transform based on decimation-in-time was used to optimize the waves with a weighted amplitude method, and the wind speed field was numerically generated according to a Davenport - type fluctuating wind speed spectrum. A second-generation benchmark structure was selected as the high-rise building model. Tuned mass damper (TMD) and active tuned mass damper (ATMD) served as the controller, and the linear- quadratic- Gaussian algorithm served as the active control algorithm for ATMD. Simultaneously, the particle swarm optimization algorithm was introduced, and the integral of the absolute value of the error based on the relative displacement of floors with regard to the ground level was defined as a performance index for optimizing. The numerical results reveal that both of the two proposed controllers have excellent capability in reducing wind-induced vibrations in high-rise buildings; moreover, the PSO-based ATMD performed better than PSO-based TMD.