Structural health monitoring

About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.

Using cross correlations of turbulent flow-induced ambient vibrations to estimate the structural impulse response. Application to structural health monitoring.

Abstract: It has been demonstrated theoretically and experimentally that an estimate of the impulse response (or Green's function) between two receivers can be obtained from the cross correlation of diffuse wave fields at these two receivers in various environments and frequency ranges: ultrasonics, civil engineering, underwater acoustics, and seismology. This result provides a means for structural monitoring using ambient structure-borne noise only, without the use of active sources. This paper presents experimental results obtained from flow-induced random vibration data recorded by pairs of accelerometers mounted within a flat plate or hydrofoil in the test section of the U.S. Navy's William B. Morgan Large Cavitation Channel. The experiments were conducted at high Reynolds number (Re > 50 million) with the primary excitation source being turbulent boundary layer pressure fluctuations on the upper and lower surfaces of the plate or foil. Identical deterministic time signatures emerge from the noise cross-correlation function computed via robust and simple processing of noise measured on different days by a pair of passive sensors. These time signatures are used to determine and/or monitor the structural response of the test models from a few hundred to a few thousand Hertz.

Active vibration-based structural health monitoring system for wind turbine blade: Demonstration on an operating Vestas V27 wind turbine

Abstract: This study presents a structural health monitoring system that is able to detect structural defects of wind turbine blade such as cracks, leading/trailing-edge opening, or delamination. It is shown...

Damage identification of a benchmark building for structural health monitoring

Abstract: An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to evaluate its possible damage. Recently, an IASC–ASCE benchmark problem for structural health monitoring has been developed in order to facilitate the comparison of various analysis techniques for the damage identification of structures on a common basis. The technique of Hilbert–Huang transform (HHT) has been shown to be a possible system identification method for linear structures. This paper presents the application of HHT to the phase I IASC–ASCE benchmark building for the complete identification of stiffness and damping coefficients. The cases analyzed involve the damage assessment in the weak direction of the benchmark building using 12-DOF and 120-DOF models. In this benchmark problem, the structural parameters, including the stiffness and damping, before and after damage are identified first, and then the location and severity of the damage are assessed by a comparison. The effect of measurement noise has been taken into account. Simulation results demonstrate that the accuracy of the HHT technique presented in identifying the structural parameters is quite plausible, and it represents a possible damage detection technique for linear structures.

Development of fragility functions as a damage classification/prediction method for steel moment-resisting frames using a wavelet-based damage sensitive feature

Abstract: SUMMARY Fragility functions are commonly used in performance-based earthquake engineering for predicting the damage state of a structure subjected to an earthquake. This process often involves estimating the structural damage as a function of structural response, such as the story drift ratio and the peak floor absolute acceleration. In this paper, a new framework is proposed to develop fragility functions to be used as a damage classification/prediction method for steel structures based on a wavelet-based damage sensitive feature (DSF). DSFs are often used in structural health monitoring as an indicator of the damage state of the structure, and they are easily estimated from recorded structural responses. The proposed framework for damage classification of steel structures subjected to earthquakes is demonstrated and validated with a set of numerically simulated data for a four-story steel moment-resisting frame designed based on current seismic provisions. It is shown that the damage state of the frame is predicted with less variance using the fragility functions derived from the wavelet-based DSF than it is with fragility functions derived from an alternate acceleration-based measure, the spectral acceleration at the first mode period of the structure. Therefore, the fragility functions derived from the wavelet-based DSF can be used as a probabilistic damage classification model in the field of structural health monitoring and an alternative damage prediction model in the field of performance-based earthquake engineering. Copyright © 2011 John Wiley & Sons, Ltd.