## 1 Introduction
[PERSON] et al. did a lot of research based on machine learning and found that ANNs can solve those problems effectively and efficiently [27; 28; 29; 30]. [PERSON] et al. [31] used back-propagation network to investigate the model updating of a suspended dome and proposed a method to increase the prediction precision of backpropagation (BP) network. [PERSON] et al. [32] proposed a multiparameter fragility methodology, which uses an artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes. [PERSON] et al. [33] constructed an ANN to improve the computational efficiency for the calculation of structural failure. We can also apply ANNs to solving damage identification problems. [PERSON] et al. [34] first performed damage detection through inputting vibration test data into the back propagation (BP) neural networks in the 1970s. Then, [PERSON] et al. [35] input the absolute eigenvalues of a spring proton system into the BP neural networks and verified the validity of neural networks in solving damage detection. [PERSON] [36] input frequency change and curvature mode into neural networks and identified the damage position in a cantilever beam. [PERSON] [37] studied sensitivity of the difference of the model components before and after the structural damage to the model parameter errors using the BP neural networks. [PERSON] et al. [38] regarded the frequency square as a damage index to identify the element damage using neural networks. [PERSON] et al. [39] proposed a structural damage identification method based on the auto encoder framework, which can support deep neural networks. [PERSON] et al. [40] presented a vibration-based technique using only the first vibration mode for predicting damage and its location using ANNs. [PERSON] et al. [41] used ANNs with a large amount of training data (damage cases) to establish a mutually between the quantity of training data and the accuracy of damage location. [PERSON] et al. [42] proposed to use nonprobabilistic ANNs to address the problem of uncertainty in vibration damage detection. ANNs were also used in others research areas [43; 44; 45; 46]. Although existing damage identification methods can be applied to identify structural damage, the primary study objectives are often onshore civil structures including bridges and architectures, and the structural overall modal results are used as damage indices. For offshore structures that are partially submerged, the modal results are often obtained from a limited number of sensor measurements, and it is difficult to get the overall modal results in practice. Thus, to predict structural damage given partial modal results in the form of sporadic sensor measurements is desirable. To this end, this paper presents a method that can facilitate the damage identification of offshore steel platforms. The layout of the paper is as follows. Section 2 presents the proposed damage identification index and the ANN method for damage identification. Section 3 introduces the general damage identification process applicable to offshore structures. Section 4 shows the case study of a jacket platform with damages, and performs damage identification using the proposed method. Finally, Section 5 concludes the paper.