Over the past few years with industrialization has necessitated humans to consider offshore resources of natural gas and oil. Fixed offshore jacket structures constitute an important part of offshore rigs. These structures are prone to damage due to their long term exposure to the saline environment which causes corrosion. Hence the health of these structures have to be overseen periodically to comprehend the extent of damage which in turn helps to decide a suitable course of action for the maintenance of the structure. Recently a lot of study had been carried out on the use of Artificial Neural Networks (ANN) in monitoring the health of a structure. ANN are a branch of machine learning and works by imitating the human brain. For this study, an offshore jacket was modelled in a finite element based software in order to create the training set. Damage was represented in the structure by two different methods separately. First method was by representing the damage through reduction of the area of cross section of the main tubes and second method was by using the reduction in elastic modulus to represent the damage. Three different neural networks were prepared for each method of damage representation with different input parameter cases namely modal frequency, modal frequency and eight nodal displacements, modal frequency and twelve nodal displacements. The optimum number of neurons in the hidden layer was obtained for the respective case. Each network was tested using a test set and output of the networks were compared with the true value of the damage. The results of the two methods of damage representation were compared.

Keywords : Artificial Neural Networks; Damage; Elastic Modulus; Modal Frequency; Offshore Jacket; Structural Health Monitoring