# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 3 Optimal Arrangement Method of Isolation Bearings
### Optimization Approach of Isolation Bearing Arrangement Based on AI Algorithm  
Based on the calculation procedure of the isolation structure and the selection method of sizes and types for isolation bearings mentioned in Sections 3.1-3.3, the optimization method of the isolation bearing layout based on the AI algorithm is obtained as follows:  
1. Preliminary selection of bearing size and type: according to vertical reaction force of the column base under the action of the standard value of the gravity load of the non-isolation structure model, the limit value of compressive stress and yield force of isolation bearings and size groups of isolation bearings corresponding to the population number in the HPO algorithm are generated. As a guarantee for the feasibility of the isolation scheme, we check whether the total horizontal yield force and eccentricity of the isolation scheme meet requirements when bearings are all LRB. If they do, the size groups of the bearings would be submitted to the trained CNN neural network to predict types of isolation bearings. Otherwise, size groups of isolation bearings would be regenerated until the requirements above are met. Then, a population of isolation bearings containing information of sizes and types is formed.
2. According to the sizes and types of information of isolation bearings, isolation structure models will be established by Python calling ETABS through API interface. Through nonlinear dynamic time history analysis, axial force at the column bottom, inter-story shear force, and story drift ratio would be calculated, as well as the seismic decrease coefficient and eccentricity in combination with calculation results of the non-isolation structure model. Then, prices of isolation bearings in Tables 1 and 2 will be utilized to calculate the total cost of bearing scheme. Furthermore, the total cost will be substituted into Formula (1) to acquire value of objective function, based on which the minimum fitness value of function in the current iteration and its corresponding scheme of isolation bearings are updated.
3. After parameters of the HPO algorithm, including sizes of isolation bearings, and types of isolation bearings based on step (1), are updated, the isolation structure model will be rebuilt by Python automatically calling ETABS through API interface. Through a nonlinear dynamic time history analysis of the new isolation model under a rare earthquake, the stress of the isolation layer and the maximum horizontal displacement of the bearings will be calculated. If all the design indexes meet requirements, the historical best value of objective function and its corresponding isolation scheme will be updated. Otherwise, return to step (1) to regenerate sizes of isolation bearings and continue calculation of step (2)-(3) to complete an iterative calculation.
4. If iteration number has reached the maximum number of predetermined iterations, or the difference between optimum fitness values of the function of two adjacent itera  
Figure 3: Schematic diagram for type prediction of isolation bearings based on CNN network.  
tions has reached the limit value predetermined, the process of iterative calculation has been accomplished.  
Summarizing the above steps, the optimization flow of this approach is displayed in Figure 4.