This study suggests a novel riser structural health monitoring methodology based on a dual algorithm (DA). In this method, the displacement tracing algorithm first traces the node displacement and tension up to the last sensor position called the target point. Then, the movement and tension at the target point are used for boundary conditions of the finite element (FE) simulator to obtain displacements and stresses below the target point. The developed method is validated through numerical simulations by comparing riser behaviors/stresses from the fully coupled model with those from the proposed method with numerical sensors. For that, a moored FPSO (floating production storage offloading) system with SCR (steel catenary riser) or SLWR (steel lazy-wave riser) is employed. Only three angle sensors are used at the top portion to monitor the entire length of riser. Much simpler forced top oscillation method is also investigated, which only uses riser top movement for running FE simulator, which cannot accurately reproduce the dynamics of the upper portion of riser since real-time wave action is ignored. The developed DA riser monitoring methodology can reproduce the movements and stresses along the entire length within around 5% error regardless of riser shapes and materials.
The bracing components in steel I-girder bridge systems are essential structural components for the bridges to restrain their rotation due to lateral torsional buckling (LTB). Current design specifications require bracing components to be installed to prevent I-girder sections from unexpectedly twisting due to instability. To estimate the bracing internal forces acting on the bracing elements, we can use approximate design equations that provide considerably conservative design values. Otherwise, it is necessary to conduct a thorough finite element analysis considering initial imperfections to obtain accurate bracing internal forces in the steel I-girder bracing systems. This study aims to provide estimation models based on deep neural network (DNN) algorithms to more accurately estimate the internal forces acting on the bracing element compared with the current design methodology when LTB occurs. This is conducted by constructing structural response data based on the geometrically nonlinear analysis with imperfections to provide accurate bracing internal forces, namely bracing moments (Mbr) and bracing forces (Fbr). To propose prediction models, 16 input and three output variables were selected for training the structural response data. Furthermore, a parametric study on the hyperparameters used in DNN models was analyzed for the number of hidden layers, neurons, and epochs. Based on statistical performance indices (i.e., RMSE, MSE, MAE, and R2), the estimated values using DNN models were evaluated to determine the best prediction models. Finally, DNN models that more accurately estimate internal forces (Mbr, Fbr) in bracing elements, and that provide the best prediction results depending on hyperparameters (numbers of hidden layers, neurons, and epochs), are proposed.
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