Abstract
This paper presents a damage identification method for offshore jacket platforms using partially measured modal results and based on artificial intelligence neural networks. Damage identification indices are first proposed combining information of six modal results and natural frequencies. Then, finite element models are established, and damages in structural members are assumed by reducing the structural elastic modulus. From the finite element analysis for a training sample, both the damage identification indices and the damages are obtained, and neural networks are trained. These trained networks are further tested and used for damage prediction of structural members. The calculation results show that the proposed method is quite accurate. As the considered measurement points of the jacket platform are near the waterline, the prediction errors keep below 8% when the damaged members are close to the waterline, but may rise to 16.5% when the damaged members are located in deeper waters.
Highlights
It is of great importance to utilize ocean space and marine renewable energy, as land-based resources are increasingly depleted [1]
Nozari et al [23] studied the effects of variability in ambient vibration measurements on model updating and performed damage identification
Pathirage et al [39] proposed a structural damage identification method based on the auto encoder framework, which can support deep neural networks
Summary
Department of Engineering Sciences, Faculty of Engineering and Science, University of Agder, N-4879 Grimstad, Norway Received: 22 October 2018; Accepted: 2 November 2018; Published: 6 November 2018 Featured Application: The presented damage identification method has potential to be applied to a wide range of offshore structures supported by jacket foundations.
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