A Macro-Scale Modeling Approach for Capturing Bending-Shear Coupled Dynamic Behavior in High-Rise Structures Using Deep Learning

Macro-scale modeling is a fundamental approach for assessing structural damage and occupant comfort in urban high-rises during earthquakes or typhoons. The key to its effectiveness is accurately reproducing dynamic responses and extracting modal characteristics. The critical issue is whether the macro-scale model can effectively capture Flexure-Shear Coupled (FSC) dynamic behavior. This paper proposes a macro-scale modeling method for high-rise structures with FSC dynamic behavior using deep learning (DL). FSC dynamic behavior is quantified by establishing Displacement Interaction Coefficients (DInC) under each mode shape. To account for the flexural resistance of horizontal members and the anti-overturning contribution of vertical members in high-rise structures, equivalent stiffness parameters representing horizontal and vertical members are introduced into the Lumped Parameter Model (LPM), enhancing the flexibility of the macro-scale model in expressing FSC dynamic behavior. The DInCs are used as input features to identify the LPM’s stiffness parameters, enabling efficient macro-scale modeling. The method was validated on a frame and a frame-core tube structure by comparing dynamic characteristics with their detailed finite element models. This method holds engineering application potential in areas requiring highly accurate and rapid structural characteristic or response calculations, such as seismic response analysis and design optimization of high-rise structures.