Xiaojun Wei

This study proposes a nongrounded passive self‐tuning string‐mass absorber, consisting of a mass block, a tensioned string, movable hinge fulcrums, an inertial amplification component, and a purely mechanical self‐tuning mechanism. The absorber tunes its frequency by varying the effective length of the tensioned string through automatic adjustment of the hinge fulcrum positions. Specifically, the hinge fulcrums remain fixed when the response of the primary structure is below a prescribed threshold; otherwise, the self‐tuning mechanism is activated and continuously adjusts the fulcrum positions until the structural response is reduced below the threshold. The self‐tuning mechanism integrates sensing, actuation, and transmission functions, and is powered entirely by the vibration energy of the primary structure, eliminating the need for any external power supply. To demonstrate its applicability to long‐span bridges, two case studies involving detuning and multimodal vortex‐induced vibration conditions are conducted. Numerical results indicate that the absorber exhibits robust self‐tuning capability and maintains effective vibration mitigation performance under complex operating conditions.

Accurate extraction of the arch profile, the key spatial geometric parameter of the core load-bearing component in arch bridges, is crucial for construction process control and for achieving the designed final bridge configuration. To overcome the limitations of existing methods—geometric information loss, sensitivity to noise, and inefficiency—when extracting continuous, precise profiles from point clouds of complex spatially curved arch ribs, this paper proposes a multi-step point cloud processing workflow. The approach integrates geometric feature constraints specific to arch bridges to enable automated, high-precision extraction of the arch profile during construction. The approach comprises three steps. First, arch point cloud subset partitioning: the primitive arch point cloud is efficiently divided using parameters from down-sampling arch point cloud data. Second, component segmentation: a Random Sample Consensus (RANSAC) algorithm, optimized with cylindrical geometric constraints, is then employed to precisely segment the point cloud of individual arch tube components from each subset point cloud. Third, arch profile extraction: the geometric invariance of the bottom edge of each arch tube is leveraged to identify feature points via local coordinate system transformation and longitudinal constraints. These feature points are then spliced together to reconstruct the complete arch profile. The proposed method is employed in multiple construction stages of a concrete-filled steel tubular (CFST) arch bridge and quantifies the vertical deformation between adjacent stages. Compared with Total Station (TS) measurements, the average error ranged from 0.24 mm to 4.13 mm, with an overall average error of 2.105 mm, demonstrating accuracy and reliability.