In order to investigate the deformation behavior, mechanical characteristics of connections, and failure mode of translational precast concrete wall-panels under earthquake loading, a full-scale two-story steel frame model was designed. This test model consisted of 12 precast concrete wall-panels connected with fixed connections at the bottom end and translational-type connections at the top end. This model passed the shaking table testing, and the results demonstrate that the translational-type precast concrete exterior wall panel can ensure the sufficient relative displacement between the wall panel and the main structure under large earthquake loads. It has sufficient capacity to adapt the deformation between the main structure and the wall panels. During the test, localized cracking was observed on the wall panel, while all connections remained in elastic state. The connections between precast concrete exterior wall panels exhibited good performance, that indicates it is an effective tool to protect frame building hangings with exterior wall panels under strong earthquake events.

Long‐span bridges exhibit nonstationary deformation under simultaneous environmental loads, yet existing monitoring approaches commonly predict the overall structural response without adaptively separating load‐associated components. This study develops a framework combining eXtreme Gradient Boosting (XGBoost) and optimal sparsity and reconstruction–based variational mode decomposition (OSR‐VMD) for real‐time deformation prediction and adaptive quantification of load‐associated deformation components. XGBoost is first used to map multisource environmental observations to lateral and vertical bridge deformations. A physically interpretable OSR objective then evaluates within‐mode bandwidth, mode‐location separation, spectral overlap, and reconstruction fidelity, and a genetic algorithm adaptively selects the VMD parameters α and K . The framework is evaluated using 10 months of heterogeneous monitoring data from the Forth Road Bridge, UK. The results show that XGBoost reliably reproduces the measured deformation patterns, while OSR‐VMD separates the predicted responses into frequency‐specific modes associated with wind and temperature variations. By integrating nonlinear prediction with adaptive time‐frequency decomposition, the framework provides an interpretable approach for identifying dominant environmental–load associations in bridge deformation and supports structural condition assessment under coupled environmental forcing.