In this study, the dynamic response of a multiconnected floating solar panel system with a vertical pontoon were studied under various scenarios. First, a floating solar panel pontoon is modeled by combining nine single-unit vertical cylinders (arranged in parallel, horizontally and vertically, 3 m apart from each other). Each cylinder will be considered a rigid body, and they are connected to each other with a frame, so that they can oscillate together. Each floating solar panel pontoon was connected to a steel pipe, and a hinged connector was attached to the connecting point of each steel pipe, while it was fixed at each pontoon. In this study, as a floating solar panel system, a 10 × 10 system was adopted at a water depth of 50 m. Furthermore, a catenary mooring system with steel wire rope was installed to enhance its station-keeping capability. As an environmental load, wave excitation force, under normal operating and extreme conditions, was considered. To confirm the dynamic behavior of the system, a connector boundary condition sensitivity test was conducted under a 0° heading (west to east). It has been proven that an unexpected dynamic response along the sway, roll, and yaw directions is observed in the hinged connector case, due to the second generated moment caused by the movement of the facilities. Furthermore, judging from extreme simulation results, the larger the external environmental loading, the greater the dynamic response of the system, due to amplified wave excitation forces. Finally, under the multiple mooring line failure scenario, the dynamic response of the system is significantly amplified, due to the loss of mooring tension, except for the roll response.
A fabricated mobile scaffold has various components, including vertical members, horizontal members, braces, work plates, and castor wheels. In Korea, the structural performance of each member must be validated based on member-level structural safety criteria; this means that rigorous evaluation methods are required to secure the system-level structural safety of the fabricated mobile scaffold. To suggest rational system-level structural safety criteria and effective evaluation methods, the characteristics of the structural behaviors of the assembled structure must be investigated first. Unlike other temporary equipment, it is a product that requires convenience of use and ease of movement. Therefore, to secure the safety and usability of the structure, it is necessary to evaluate the ultimate behavior of a mobile scaffold fabricated with various material and structural types. In an experimental study, the ultimate mode and load-bearing capacity were investigated, and the appropriateness of the required performance of the mobile scaffold was reviewed. Three types of experimental test models with different materials (steel and aluminum) and stories (single-story and three-story erection) were selected and examined for vertical loads. Based on the experimental results, the ultimate behavior characteristics of the fabricated mobile scaffold were analyzed, and the ultimate load was identified.
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.
Horizontally curved I-girder bridges are known to be complex. Bending and torsion forces are imposed on the bridges owing to their shapes with initial curvatures. This torsion is a combination of pure and warping forces. The horizontally curved I-girder is significantly affected by warping behavior, which decreases the bending rigidity of its member. To investigate the warping behavior of the horizontally curved I-girder bridges a finite element analysis (FEA) must be performed. In this study, an FEA was performed to investigate the warping torsional behavior of a horizontally curved I-girder bridge, and a structural response database was obtained. Based on the database, the least absolute shrinkage and selection operator was employed to select features affecting the warping behavior. Subsequently, deep neural network models were trained with selected features for an input layer and maximum lateral flange moment data for an output layer. Several models were constructed and compared according to the number of hidden layers and neurons, and the model with the highest performance was proposed. Finally, it was confirmed that the estimated lateral flange moments computed by the proposed model showed a good correlation with the FEA results.
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