# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 6 Conclusions and Prospect  
A joint algorithm-based optimization approach that employs a CNN network to predict types of isolation bearings and an HPO algorithm to optimize sizes of isolation bearings is proposed in this paper. To simplify the optimization process, an optimization  
Figure 15: (**a**) Seismic decrease coefficient; (**b**) Inter story shear force. Variation of design indexes of isolation scheme with different weight coefficient combinations: numbers in the legend represent weight coefficient \(\alpha\), \(\lambda\), and \(\gamma\), respectively; for example, 0.0–0.2–0.8 means \(\alpha=0.0\), \(\lambda=0.2\), \(\gamma=0.8\). objective function that takes the seismic decrease coefficient, story drift ratio, and economic cost into account is applied. By assigning various combinations of weight coefficients in the optimization objective function, the significance of various optimization objectives can be taken into consideration. Through employing the optimization objective function as a function fitness value of the HPO algorithm, the joint algorithm is utilized to optimize the arrangement scheme of the isolation bearings. After applying a joint algorithm-based optimization approach in an actual engineering example, the impact on each optimization objective of different combinations of weight coefficients is analyzed and the efficiency of this method has been demonstrated. Summarizing the analysis above, the main conclusions are enumerated as follows:  
1. This study establishes a framework to optimize an isolation scheme by means of updating a structural model and value of optimization function using Python based on the Application Program Interface (API) interface provided by ETABS, optimizing sizes of isolation bearings by Python based on HPO algorithm, and predicting types of isolation bearings by Python based on CNN. It performs automatically in isolation analysis and the arrangement optimization of isolation bearings without artificial intervention. 2. The isolation bearing optimization method based on the AI algorithm possesses high convergence efficiency during the optimizing process for all the combinations discussed in this study. For several factors that have been completely considered and optimized, the isolation scheme utilizing the combined algorithm using CNN and HPO is practicable for type selection and size optimization of isolation bearings and meets the requirements of the design code. 3. The total cost weight coefficient of isolation scheme has a greater impact on the optimization objective function. Moreover, the total cost of bearings, maximum story drift ratio, and seismic decrease coefficient decrease as corresponding weight coefficient rises. According to variations of these factors, for factors that designers pay more attention to, the corresponding weight coefficient should be larger than others. 4. Several required factors are simultaneously considered by the proposed optimization objective function, through the optimization of which all objects about these factors acquire optimization at different levels by means of different combinations of weight coefficient. This approach is beneficial to simplify and accelerate the optimization process of isolation scheme. 5. Concerning the constraint condition about eccentricity of the stiffness center of isolation bearing and the center of gravity of the superstructure throughout the optimization process, not only would the isolation scheme meet specifications of design code, but the type distribution of isolation bearings also appears to be uniform and more sizes of isolation bearings could be employed in an isolation layer scheme during the optimization process. Current work is applied on ETABS version 2018, therefore accessibility of other versions of ETABS needs to be verified.