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.

Nonlinear ultrasonic detection for evaluating fatigue crack in metal plate

Abstract: In engineering structures, metal materials always endure fatigue cracks under long-term service. There has been a demand for developing a structural health monitoring method to evaluate micro-sized...

Static and Dynamic Strain Monitoring of Reinforced Concrete Components through Embedded Carbon Nanotube Cement-Based Sensors

Abstract: The paper presents a study on the use of cement-based sensors doped with carbon nanotubes as embedded smart sensors for static and dynamic strain monitoring of reinforced concrete (RC) elements. Such novel sensors can be used for the monitoring of civil infrastructures. Because they are fabricated from a structural material and are easy to utilize, these sensors can be integrated into structural elements for monitoring of different types of constructions during their service life. Despite the scientific attention that such sensors have received in recent years, further research is needed to understand (i) the repeatability and accuracy of sensors’ behavior over a meaningful number of sensors, (ii) testing configurations and calibration methods, and (iii) the sensors’ ability to provide static and dynamic strain measurements when actually embedded in RC elements. To address these research needs, this paper presents a preliminary characterization of the self-sensing capabilities and the dynamic properties of a meaningful number of cement-based sensors and studies their application as embedded sensors in a full-scale RC beam. Results from electrical and electromechanical tests conducted on small and full-scale specimens using different electrical measurement methods confirm that smart cement-based sensors show promise for both static and vibration-based structural health monitoring applications of concrete elements but that calibration of each sensor seems to be necessary.

Compressive sensing of full wave field data for structural health monitoring applications

Abstract: Numerous nondestructive evaluations and structural health monitoring approaches based on guide waves rely on analysis of wave fields recorded through scanning laser Doppler vibrometers (SLDVs) or ultrasonic scanners. The informative content which can be extracted from these inspections is relevant; however, the acquisition process is generally timeconsuming, posing a limit in the applicability of such approaches. To reduce the acquisition time, we use a random sampling scheme based on compressive sensing (CS) to minimize the number of points at which the field is measured. The CS reconstruction performance is mostly influenced by the choice of a proper decomposition basis to exploit the sparsity of the acquired signal. Here, different bases have been tested to recover the guided waves wave field acquired on both an aluminum and a composite plate. Experimental results show that the proposed approach allows a reduction of the measurement locations required for accurate signal recovery to less than 34% of the original sampling grid.