# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
###### Abstract  
The determination of an isolation-bearing scheme usually depends on experience, and needs numerous iterative calculations, especially when considering many factors such as total cost of the scheme, various design indicators, eccentricity of stiffness center of isolation bearings and the center of gravity of superstructure, and so on. Moreover, during the usual optimization process, the isolation scheme is often limited in several kinds of sizes and fixed predetermined distribution of types of isolation bearings based on experience or trial calculations due to computational efficiency, which would make it incapable of exploring other possible schemes. In this paper, artificial intelligence technology is applied to optimize the layout of isolation bearings. Types of isolation bearings are predicted through a Convolutional Neural Network, and sizes of isolation bearings are optimized by Hunter-prey optimization algorithm to improve computational efficiency and optimal arrangements of bearings. To simplify the optimization process, an optimization objective function considering a seismic decrease coefficient, story drift ratio and total cost of isolation bearings is proposed. In this function, weight coefficients reflect significance of various factors during the optimization process. In order to investigate influence of different combinations of weight coefficients on the optimal layout, 12 groups of combinations of weight coefficients are designed and analyzed. The results show that the optimal layout method of isolation bearings based on the artificial intelligence algorithm has good convergence efficiency of optimization and makes it possible to search more practical isolation scheme with good performance. When focusing on total cost of bearings, the ideal weight coefficient of the total cost would be larger than 0.4. While the structural performance factors are mainly considered, the weight coefficient of the maximum story drift ratio or seismic decrease coefficient should be larger than 0.2. For factors that designers pay more attention to, the corresponding weight coefficient should be larger than others. artificial intelligence technology; Convolutional Neural Network; Hunter-prey optimization; isolation bearing arrangement optimization; weight coefficient; optimization objective function +
Footnote †: journal: Applied Sciences
of isolation design schemes so as to perform the function of isolation bearing in structures more effectively. [PERSON] et al. [17] built a 2D model and utilized genetic algorithms to search parameters of isolated bearings; afterwards, they used a non-dominated sorting genetic algorithm to acquire a series of Pareto-optimal isolation schemes for high rise buildings. However, this method did not consider the 3D model and needed numerous iterative calculations. [PERSON] et al. [18] investigated an approach to apply the Strength Pareto Evolutionary Algorithm 2 to figure out an optimal isolator scheme. During the optimization process, the cost of the isolation scheme was not considered. [PERSON] et al. [19] utilized a sequential quadratic programming algorithm to optimize objective function concerning yielding stiffness and the strength of isolation bearings. Aiming to minimize the maximum seismic response of the superstructure, numerous iterative calculations would be needed when considering various other factors. [PERSON] et al. [20] optimized isolator parameters using an adaptive harmony search algorithm. Three different damping ratio limits and isolator displacement limits were taken to optimize the isolation scheme without considering cost and other design indicators.