Failure mechanism and practical load-carrying capacity calculation method of welded hollow spherical joints connected with circular steel tubes

p. 2679-2691According to the ultimate load-carrying capacity obtained from finite element analysis, data point is designed based on orthogonal method, utilizing F-inspection from mathematical statistics to perform multi-parameter and single-factor significance analysis of compressive load capacity. The result indicates that yield strength of spherical material fy are the critical factor that influence the load carrying capacity of hollow spherical joint, as well as wall thickness t, outer diameter of sphere D and outer diameter of steel tube d. Comparatively destructive experiments on 8 typical full-scale joints made from two different graded material, Q235B and Q345B, were conducted to understand directly the structural behavior and the collapse mechanism of the joint, and also to validate the finite element analysis and parameter study. Finally, the simplified theoretical solution is also derived for the loading-carrying capacity of the joint based on the punching shear failure model, and the basic form for the design equation is obtained. By applying the results from the simplified theoretical solution, finite element analysis and experimental study, and utilizing the theory of mathematic statistics and regression analysis, the practical calculation method is established for the load-carrying capacity of the joints subjected to axial compressive forces. By the check of large amount of experiment data, the calculation result obtained from this formula is consistent with experiment result, and the practical formula has safety reserve meeting the regulation in national codes. The achievements from this study can be applied for direct design , and also provide a reference for the revision of relevant design codes.Xue, W.; Yang, L.; Zhang, Q.; Wang, P. (2009). Failure mechanism and practical load-carrying capacity calculation method of welded hollow spherical joints connected with circular steel tubes. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/660

FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation

Weather forecasting is a crucial yet highly challenging task. With the maturity of Artificial Intelligence (AI), the emergence of data-driven weather forecasting models has opened up a new paradigm for the development of weather forecasting systems. Despite the significant successes that have been achieved (e.g., surpassing advanced traditional physical models for global medium-range forecasting), existing data-driven weather forecasting models still rely on the analysis fields generated by the traditional assimilation and forecasting system, which hampers the significance of data-driven weather forecasting models regarding both computational cost and forecasting accuracy. In this work, we explore the possibility of coupling the data-driven weather forecasting model with data assimilation by integrating the global AI weather forecasting model, FengWu, with one of the most popular assimilation algorithms, Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model and consider the temporal evolution of atmospheric dynamics to obtain accurate analysis fields for making predictions in a cycling manner without the help of physical models. Owning to the auto-differentiation ability of deep learning models, FengWu-4DVar eliminates the need of developing the cumbersome adjoint model, which is usually required in the traditional implementation of the 4DVar algorithm. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields for making accurate and efficient iterative predictions.Comment: 15 pages, 8 figure

Multi-scale Visual Feature Extraction and Cross-Modality Alignment for Continuous Sign Language Recognition

Effective representation of visual feature extraction is the key to improving continuous sign language recognition performance. However, the differences in the temporal length of sign language actions and the sign language weak annotation problem make effective visual feature extraction more difficult. To focus on the above problems, a method named multi-scale visual feature extraction and cross-modality alignment for continuous sign language recognition (MECA) is proposed. The method mainly consists of a multi-scale visual feature extraction module and cross-modal alignment constraints. Specifically, in the multi-scale visual feature extraction module, the bottleneck residual structures with different dilated factors are fused in parallel to enrich the multi-scale temporal receptive field for extracting visual features with different temporal lengths. Furthermore, the hierarchical reuse design is adopted to further strengthen the visual feature. In the cross-modality alignment constraint, dynamic time warping is used to model the intrinsic relationship between sign language visual features and textual features, where textual feature extraction is achieved by the collaboration of a multilayer perceptron and a long short-term memory network. Experiments performed on the challenging public datasets RWTH-2014, RWTH-2014T and CSL-Daily show that the proposed method achieves competitive performance. The results demonstrate that the multi-scale approach proposed in MECA can capture sign language actions of distinct temporal lengths, and constructing the cross-modal alignment constraint is correct and effective for continuous sign language recognition under weak supervision