未知风荷载条件下超高层结构参数反演的两阶段算法OA

Supertall buildings are prone to strong dynamic responses under external actions such as wind loads,making the accurate acquisition of their physical parameters is crucial for evaluating structural safety and service reliability.Aiming at the difficulty of accurately identifying key physical parameters(such as inter-story stiffness and damping)of supertall structures in the absence of wind load excitation information,a two-stage inversion algorithm combining the extended Kalman filter(EKF)and the load normalized statistical averaging(LNSA)method was proposed.The method first employed the EKF algorithm to accurately invert other structural responses under unknown wind loads based on acceleration response data.Subsequently,the LNSA method was introduced to achieve the joint optimization inversion of wind loads and structural parameters by constructing spatial correlation constraints for wind loads.Taking the Shanghai Tower as a case study,a high-degree-of-freedom lumped mass model was established based on its detailed finite element model to serve as the verification basis,followed by wind-induced response simulation and parameter inversion analysis.The research results demonstrate that the proposed two-stage inversion algorithm exhibits good convergence and identification accuracy in high-degree-of-freedom structural systems.Specifically,the maximum error of inter-story stiffness identification does not exceed 8%,and the average error is less than 3.5%.By utilizing only acceleration observations,this method effectively overcomes the limitation of traditional single filtering algorithms that are prone to divergence in high-dimensional spaces,providing a reliable technical approach for the parameter inversion of in-service supertall structures under unknown wind load conditions.