Merging Sensor Data from Multiple Temperature Scenarios for Vibration Monitoring of Civil Structures

Abstract

The ambient temperature e ect may result in limitations of vibrationbased structural health monitoring (SHM) approaches for civil engineering structures. This paper addresses the issue of discriminating changes in modal parameters due to damages and changes in modal parameters due to temperature e ects. A n o n parametric damage detection algorithm is proposed, which only assumes that several datasets are recorded on the safe structure at di erent and unknown temperatures, and smoothes out the temperature e ect using an averaging operation. Key

Structural health monitoring with statistical methods during progressive damage test of S101 Bridge

“…However, the sensitivity of modal damage indicators is often not satisfying for a lot of real applications in civil engineering with ambient excitation and a limited number of sensors. Varying operational or environmental influences may have a greater impact on the dynamic properties of a system than structural damage in early stages [10,11]. Besides, civil structures differ from many other mechanical structures by their size and their uniqueness and therefore cannot undergo any kind of prototype testing, as it is common for products from e.g.…”

“…Thanks to this uncertainty quantification, the significance of the modal parameters and their changes during the monitoring period can be evaluated. Second, we perform the actual damage detection with a null space based statistical damage detection test [11,[21][22][23],…”

Subspace-Based Damage Detection on Steel Frame Structure Under Changing Excitation

“…In the following, the non-parametric damage detection test [1] based on [2,3] is described. Using measured data (y k ) k=1,...,N , a consistent estimate H p+1,q of the Hankel matrix is obtained from the empirical output covariances…”

“…The behavior of a structure is assumed to be described by a linear time-invariant (LTI) dynamical system MẌ (t) + CẊ (t) + K X (t) = υ(t) (1) where t denotes continuous time, M , C , K ∈ R d×d are the mass, damping and stiffness matrices respectively and X ∈ R d collects the displacements of the d degrees of freedom (DOF) of the structure. The external and non-measured force υ(t) is modeled as white noise.…”

“…The excitation noise at these six positions was generated with a diagonal covariance matrix Q, whose diagonal entries were randomly chosen from a uniform distribution in the interval [1,36]. 5% white noise were added on the generated outputs.…”