# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 3 Optimal Arrangement Method of Isolation Bearings
### Prediction of Isolation Bearing Type Based on CNN Algorithm  
After the sizes of isolation bearings are determined, the types of isolation bearings are usually chosen according to total horizontal yield force of the isolation layer to ensure that the isolation layer can withstand horizontal shear force. The selection principle of types of isolation bearings in traditional design method is to arrange LRB bearings on the periphery of the structure and LNR bearings inside and then check the eccentricity of the stiffness center of the isolation bearings and the center of gravity of the superstructure, the seismic decrease target, the stress of isolation layer, and the isolation bearing displacement, eventually performing iterative calculation and optimization according to whether each index meets design requirements.  
However, for large-scale or complex arrangement structures, the selection approach of types of isolation bearings is somehow limited by the design period and computer calculation speed. Even experienced designers require more iterative calculations to obtain a somewhat satisfactory design scheme. Moreover, due to the limited number of samples compared, the selected isolation scheme may not achieve the best isolation performance. Accordingly, a CNN algorithm to select the types of isolation bearings is introduced in this paper. The major workflow is as follows:  
Initially, a batch of size groups of isolation bearings would be established to generate the data set. Based on the sizes of isolation bearings selected at random, by setting the constraints of eccentricity, stress, and horizontal yield force of isolation bearings, the objective function concerning the cost of isolation bearings, seismic decrease coefficient and horizontal stiffness is considered. The types of isolation bearings are optimized by HPO algorithm so as to obtain the type distribution of bearings with minimum value of objective function, during which the LNR bearing is represented by 0, while 1 means the LRB bearing. Through this means, a data set with a capacity of 500 different combinations of sizes and types of isolation bearings is generated, operating flow of which is shown in Figure 2.  
Subsequently, the data set will be divided into training set and test set. During the training period--by taking the sizes of isolation bearings as the input parameters, and the types of isolation bearings and the value of optimization objective function as the output parameters--the CNN network is trained. Meanwhile, the error calculation with information of the bearings in the data set is conducted so as to acquire feedback to update weight coefficients in the neural network. In order to determine weight coefficient in the neural network with higher efficiency, a genetic algorithm is introduced in the training process, and the structure of the neural network is updated through the selection, crossover, and mutation mechanism [42] of the genetic algorithm. Through this process, the neural network has better performance in predicting types of isolation bearings. The CNN network structure is shown in Figure 3.  
After trial calculation, for the calculation example in Section 4, the adopted CNN network structure chooses the sizes of 54 isolation bearingss at the column base as the input layer; the convolution layer-pooling layer-full connection layer is set to be 98-98-84, respectively. The output layer contains the types of isolation bearings (LRB, LNR) at the base of 54 columns.  
Figure 2: Flow diagram of generating data set.