# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
###### Abstract
[PERSON] [21] proposed a two-stage optimization method to acquire isolation arrangement, firstly introducing a multi-objective genetic optimization algorithm to obtain the optimal parameters of isolated bearings, then further optimizing the type distribution of the bearings to cut the cost of the isolation scheme, which made the optimization process time consuming and needed to assign a predefined small range of size and type of bearings manually. Once the predetermined range is unfit, a replay of the second step is needed. [PERSON] et al. [22] compared various predefined allocation schemes of isolators between buildings with different stories and irregularities to obtain an optimal isolation arrangement. The research results may not be fit for other number of floors, and it was unknown if there were other isolation schemes besides the 11 kinds of predefined arrangements of isolators. [PERSON] et al. [23] proposed an active control algorithm to search the isolator layer parameters and optimize weighting through structure seismic response. Only the parameters of the isolator layer were optimized, while other factors such as cost of scheme had not been taken into consideration. [PERSON] et al. [24] compared a laminated natural rubber bearing (LNR) with high damping rubber bearing to search an optimal isolation scheme for continuous beam bridge. During the whole process of optimization, the isolation schemes were artificially designated. [PERSON] et al. [25] developed a cost model employing a multi-objective genetic algorithm to reduce the cost of LRB (Lead Rubber Bearing) systems. However, the variability of properties of LRB was not considered, meanwhile the prototype model was a 2D structure. The aforementioned optimization algorithms are usually constrained by the fact that the computational power of the computer can only take into account a limited amount of optimization examples during an iterative optimization process, thus the efficiency of the optimization process is mainly dependent on the computer's performance. Therefore, a relatively narrow range of isolation bearing sizes is applied during the optimization process, and the layout positions of various types of isolation bearings in the structure are usually fixed. Additionally, because the actual requirements for isolation bearings of the structure are not fully taken into account, once some indexes could not meet the requirement, more iterative calculations would be needed. Summarizing the previous research works, there are still several items to be considered comprehensively or work to be improved:  
1. Not fully considering factors such as total cost of scheme, various design indicators, eccentricity of the stiffness center of isolation bearings and the center of gravity of the superstructure, and so on. 2. Only utilizing one or two kinds of sizes and fixed predetermined type distributions of isolation bearings based on experience or trial calculation during the optimization process. 3. Needing large amounts of iterative calculations and depending on previous experience to set initial range of scheme to be optimized. 4. The complex optimization objective function and time-consuming analysis process. With the quick development of AI (Artificial Intelligence) technology, deep learning technology represented by neural network has been extensively utilized in vision, machinery,engineering and other fields. Based on feedback as a result of error calculation of the neural network output layer, the weight coefficient of the neural network would be updated, leading to the continuous improvement of the successful rate of learning of training set. Accordingly, in many fields, including disease detection [26], image recognition [27], structural health monitor [28], engineering optimization [29], vibration or displacement prediction [30; 31], and vulnerability assessment [32], AI technology has reached or even surpassed manual capabilities of recognition [33], analysis [34], and calculation. In the field of structural engineering, [PERSON] et al.