# A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model
###### Abstract
PHM describes the nonlinear failure behavior of RC structure by establishing the typical physical failure characteristic line segments, such as yield and cracking. This causes simplification of the nonlinear behavior and low accuracy of structural analysis. Moreover, it is difficult to describe a complex degenerative behavior of the RC structure by these models, and the calculation cost is increased by refined modeling. The SHM models use nonlinear differential equations to describe the nonlinear behavior of RC structures by adjusting the parameters of the differential equations [19; 20; 21]. Compared with PHM, the SHM has the advantages of high accuracy and low calculation cost. Identifying a suitable multi-parameter SHM is the focus of this paper. Various SHM models have been proposed in the past few decades. One of the most versatile SHM models is the Bouc-Wen (BW) [22; 23] model, which has been widely used in several engineering fields. [PERSON] developed the earliest version of this smooth and versatile hysteresis model; [PERSON] and [PERSON] extended [PERSON]'s model by considering the degradation in strength or stiffness in nonlinear hysteretic systems [23]. [PERSON] and [PERSON] further extended the BW model by including the complicated pinching behavior [24] and introduced a general hysteresis model known as the BWBN model [25]. The BWBN model takes into account strength and stiffness degradation as well as the shear-pinching phenomenon. Extensive research studies, utilizing the BWBN model, have been reported in the literature [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. However, despite its broad application, one of the practical problems in the application of the multi-parameter smooth BWBN model is to identify its parameters. Multiple combinations of BWBN parameters can describe a broad range of structural hysteresis behavior, however, since the BWBN model is an analytical model; its parameters have no obvious physical meaning. The question is how to identify a combination of parameters for an optimal solution. The traditional approach for determining the parameters has been based on using computational schemes that rely on engineering experience and experimental studies, which are subjective and random in nature. Improving the traditional method and introducing a robust and reliable approach for parameter identification of the BWBN model is another objective of this paper. The most commonly used algorithms for parameter identification include genetic and differential algorithms. In both approaches, the hysteretic loop of the identified object is first classified and the distribution range of these hysteretic loop parameters is determined based on prior knowledge. In order to reduce the computation of multi-parameter identification, a sensitivity analysis is usually performed. Finally, the identified parameters are screened in order to determine the most suitable ones. This process of traditional methods is complicated [15; 38; 39; 40; 41; 42; 43; 44; 45]. This poses a great challenge to the application of the model. Therefore, there is a need for a more efficient approach that can quickly identify the parameters of complex hysteretic systems such as the BWBN model. In recent years,with the development of more powerful and faster computational processing, artificial intelligence (AI)-based algorithms, such as deep learning, have emerged as more promising and computationally more efficient tools for system identification, especially for treating complex problems [46; 47; 48; 49; 50]. At the same time, given the availability of more powerful data analysis tools, a wide range of quasi-static experimental data of RC structures have also been accumulated. These large banks of experimental data, combined with deep learning prediction methods, provide an opportunity for a more accurate and realistic parameter identification of multi-parameter hysteretic models, especially nonlinear hysteretic models for the response analysis of RC structures.