# A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model
## 5 Conclusions  
As described in this paper, the multi-parameter BWBN model is a versatile model that can predict the complex hysteretic behaviors observed in a wide range of engineering applications. However, the difficulty in utilizing the multi-parameter BWBN model in practical applications is parameter identification. The traditional parameter identification methods use prior experience, sensitivity analysis, and optimization algorithms. Although those method have demonstrated good results, the cumbersome process used in those approaches is not suitable for practical application. The most noteworthy advantage of the proposed deep learning-based approach is the high efficiency and straightforward implementation and accuracy in identifying nonlinear characteristics of input parameters. Overall, the trained neural network model can easily obtain the parameters of the BWBN model. The key advantages of the proposed method can be summarized as follows:  
1. The multi-parameter BWBN model has a wide range of applicabilities. It can be applied in different fields by adjusting the parameter distribution range, such as mechanical engineering, civil engineering, and structural health monitoring and analysis of RC columns. Based on the findings of this study, the appropriate parameter range can be given by: \[0<\alpha<0.2,0<\beta<1.5,0<\gamma<0.5,n=1,\ 0<\delta_{v}<1,\ 0<\delta_{\eta}<0.1,\ 0.25<\zeta_{s}<2,\] \[0<p<0.3,0<q<1.15,0<\psi<0.2,0<\delta_{\psi}<0.1,0<\lambda<0.1\]
2. The parameter identification is a bottleneck problem in the application of the BWBN model. The traditional optimization-based identification methods focus on finding a group of suitable parameter values. However, those approaches result in more than one solution in the feasible solution region. The proposed research focuses on regression analysis. A regression analysis through deep learning leads to ideal results. Once the regression analysis model is trained well, a group of feasible solutions can be easily obtained for similar problems, which significantly reduces the workload of traditional methods.
3. The parameter identification method proposed herein offers a more efficient and concise technique for the application of BWBN model in engineering fields. For example, based on the efficient parameter identification introduced in this work, in the open-source software OPENSES the macroseismic model of beams and columns can be established. The BWBN macro model is different from the finite element method, which needs refined modeling and multiple material models to simulate the complex response of the structure. The multi-parameter BWBN macro model can easily capture the complex response only by changing the parameter values.  
[PERSON], the PhD student, was responsible for establishing the methodology introduced in this work and carrying out the majority of the research project alongside [PERSON]. Both analytical derivations and statistical methods were closely tested and confirmed by [PERSON], [PERSON] and [PERSON] and suggested the procedures utilized. [PERSON] provided his expertise and technical background in AI and assisted [PERSON] with carrying out the numerical analyses and implementation of algorithm. [PERSON] and [PERSON] with his expertise in statistical methods worked closely with [PERSON] and [PERSON] throughout the project. [PERSON] worked closely with [PERSON] in reviewing and editing phase. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research received no Data Availability Statement.  
The authors declare no conflict of interest.