Luis Volnei Sudati Sagrilo

TL;DR: In this paper, the authors evaluated the use of artificial neural networks (ANNs) for pattern recognition of magnetic flux leakage (MFL) signals in weld joints of pipelines obtained by an intelligent pig.

TL;DR: In this paper, the reliability of non-destructive test (NDT) techniques for inspection of pipeline welds employed in the petroleum industry was evaluated, and the results showed the superiority of automatic ultrasonic tests for defect detection compared with the manual ultrasonic and radiographic tests.

TL;DR: In this article, a hybrid Artificial Neural Network (ANN) and finite element method (FEM) is used to perform a nonlinear mapping of the current and past system excitations (inputs) to produce subsequent system response (output) for the random dynamic analysis of mooring lines and risers.

TL;DR: In this paper, a linear single-degree-of-freedom (SDOF) system along with a bi-lognormal probability model for significant wave heights and zero-crossing wave periods have been used for numerical tests.

TL;DR: In this article, a reliability-based partial safety factor calibration study for a LRFD mooring line design criteria considering the three approaches mentioned above is presented, which is applied to three FPSOs considering North Sea environmental conditions and different water depths.