Robust Static Structural System Identification Using Rotations

Abstract

Deflections are commonly measured in the static structural system identification of structures. Comparatively less attention has been paid to the possibility of measuring rotations for structural system identification purposes, despite the many advantages of using inclinometers, such as a high resolution and being reference free. Although some work using rotations can be found in the literature, this paper, for the very first time, proposes a statistical analysis that justifies the theoretical advantage of measuring rotations. The analytical expressions for the target parameters are obtained via static structural system identification using the constrained observability method first. Combined with the inverse distribution theory, the probability density function of the estimations of the target parameters can be obtained. Comparative studies on a simply supported bridge and a frame structure demonstrate the advantage of measuring rotations regarding the unbiasedness and the extent of variation in the estimations. To achieve robust parameter estimations, four strategies to use redundant rotations are proposed and compared. Numerical verifications on a bridge structure and a high-rise building have shown promising results.

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

“…Among the wide array of monitoring devices, inclinometers, commonly referred to as tilt sensors, deserve special attention for their widespread application across SHM [35]. For instance, in the context of bridge health assessment, researchers such as Huseynov et al [36] employed rotation measurements from inclinometers to detect structural damage, while in building structural analysis, Jun et al [37] demonstrated the efficiency of inclinometer-derived rotation data to efficiently identify damage in building beams.…”