This study proposes the first fully deep learning-based structural response intelligent computing framework for civil engineering. For the first time, from the data side to the model side, the structural information of the structure itself and any loading system is comprehensively considered, which can be applied to materials, components, and even structures, system and other multi-level mechanical response prediction problems. First, according to the characteristics of structural calculation scenarios, a unified data interface mode for structural static characteristics is formulated, which preserves the original structural information input and effectively reduces manual intervention. On this basis, an attention mechanism and a deep cross network are introduced, and a structural static feature representation learning model PADCN is proposed, which can take into account the memory and generalization of structural static features, and mine the coupling relationship of different structural information. Then, the PADCN model is integrated with the dynamic feature prediction model Mechformer and connected with the designed general data interface to form an end-to-end data-driven structural response intelligent computing framework. In order to verify the validity of the framework, numerical experiments were carried out with the steel plate shear wall structure as the carrier, in which a data augmentation algorithm suitable for the field of structural calculation was proposed to alleviate the problem of lack of structural engineering data. The results show that the deep learning model based on this framework successfully predicts the whole-process nonlinear response of specimens with different structures, the simulation accuracy is better than that of the fine finite element model, and the computational efficiency exceeds the traditional numerical method by more than 1000 times, achieving a qualitative improvement. It is proven that the intelligent computing framework has excellent accuracy and efficiency.

To optimize the seismic performance in high-rise buildings, the performance changes of beam-column joints of high-rise steel structures of buildings when they are impacted are analyzed to obtain the optimal choice of joint combination. The most commonly used steel beam and steel column joints are selected as the research objects. Firstly, the finite element model and the shell element model of the force are established and then the model of the finite element combination of six kinds of steel beam and steel column joints is established. Secondly, the applied data of common impact force in actual buildings are collected, and the simulated impact force in this experiment is established according to the sampled data. Then, the changes in each performance of the joints under the action of the impact external force are deeply explored through simulation. Finally, the accuracy of the model is confirmed by calculation. The experimental results comprehensively show that the transmission mechanisms of the impact force are different for the six joints. Through calculation and comparison, the bending stiffness, strength, deformation, the displacement degree of the joints, and the bearing degree of the impact external force are comprehensively analyzed. It is concluded that the bolt-type and external diaphragm-type steel tube column concrete and steel-beam concrete composite joints have better seismic resistance than the other five joints. The bolt-type does not need to be welded on site, and the on-site construction workers only need to assemble, which can reduce the technical requirements for construction workers, improving the practicability and applicability. Therefore, it shows reliable reference significance for the joint selection of building steel structures.