Coupled data/physics-driven framework for accurate and efficient structural response simulation

Abstract

Achieving accurate and computational efficient simulations is vital for the design, construction, and maintenance of buildings and infrastructures. Traditional physics-driven methods, such as the finite element method, struggle to balance precision with computational efficiency. In contrast, data-driven methods, such as deep neural networks, fall short in generalization and robustness. Therefore, this study proposes a coupled data/physics-driven simulation framework to harness the advantages of data- and physics-driven models, to achieve accurate and computational-efficient structural response simulation. First, the overall concept of the proposed framework is outlined, including modeling and separating the target structure into data- and physics-driven sections. Based on the discussion of the fundamental approaches for data-driven simulation, an innovative attention-enhanced stacked regression neural network is proposed to improve the accuracy of data-driven section. This architecture integrates dataset augmentation method, stacked regression, and attention-based feature enhancement. Furthermore, physics-driven modeling and the integration between the data- and physics-driven models are investigated. Finally, a case study is conducted based on a three-story frame/shear-wall building. The results demonstrate that the proposed method achieves accuracy comparable to refined finite element models, with an average stress/strain deviation no more than 0.1 %. Meanwhile, the required computational time is similar to that of a much-simplified model, with a speed-up ratio exceeding 70 times.