Intelligent Crack Identification For The Structural Members Of Offshore Jacket Platforms Based On The Strain Mode Method

Offshore platforms are giant structures, subject to various environmental loads, such as wind, wave, currents, and ice. Other environmental loads may also come from earthquakes, typhoons, tsunamis, ship collisions and other accidents. Under these dynamic loads, cracks may occur and further develop at the structural members of an offshore platform. It is impossible to inspect the platform structural components beneath the sea level. Therefore, cracks can be a great threat to the platform. Damages at important structural components may lead to catastrophic results. Therefore, it is necessary to discover and identify the cracks in time to ensure the structural safety.This dissertation adopts the intelligent diagnosis method based on the strain mode method to identify the crack at quarter span of a simply supported beam and a single crack at a jacket offshore platform pipe. The crack can be non-impenetrable into the beam section, while staying at the surface and in the beam with a certain depth. The author computes the displacement modes and the strain modes of all above cases. Based on the strain mode difference principle, direct index Ismsd——uses the equidistant difference scheme, without the modal characteristics of the original structure. FEM numerical simulations for non-impenetrate crack of different levels are also carried out. It is found that the strain mode differential curve of the damaged beam sharply changes at the damage location. The criteria of damage location detection are obtained by strain mode difference curves through a cubic spline interpolation. Through the two extremum points corresponding to ISMSD, the damage location can be found.To intelligently identify the damage level for a single crack at the non-impenetrable beam, the support vector machine (SVM) with the Grubbs and the BP neural network with Grubbs network are used. The differences between the strain mode shapes can be due to the network input. The performance and the accuracy of the two methods have been compared. The SVM approach is of higher accuracy.FEM numerical simulation by ANSYS is used to obtain the frequencies and strain mode shapes of the horizontal pipe at a offshore jacket platform model. A single crack and double cracks of the pipe with different positions and different degrees are considered. Damage in the pipe would cause the natural frequencies shift to lower values. The frequencies decrease with the increase of damage level. In addition, the frequency drop is very obvious. The frequency variation level of the double-crack case is higher. The strain mode differential curve of the damaged pipe sharply changes at the damage location.The ten single-cracks of the pipe in the platform model can be intelligently identified by SCE-UA and the coarse-grained parallel genetic algorithm. The network input are the differences between the strain mode shapes. The results of these two algorithms for the structural degree damage diagnosis show that both of the two methods have high identification accuracy and good adaptability. The error of SCE-UA algorithm is smaller.The strain mode method is a goog method for damage diagnosis, which is simple, cheap and has the virtue of real time, convenience, remote control. The study of this dissertation has favorable application foreground and spread value for engineering.