Evaluation of threshold level of change in impedance signature for incipient, moderate and severe damages in structural system

Abstract

Structures play a major role in the economic growth of any developing nation; therefore, regularly monitoring their health is very important. Structural health monitoring is a technique that identifies the presence, location, severity of damage, and the remaining lifespan of a structure. Electro-mechanical impedance technique is an advance SHM technique using smart material. Unlike traditional, sensor-based, methods can only provide indirect data, like strain or load history, which doesn’t directly show level of damage whereas EMI techniques directly detect structural damage, which is obviously a more realistic approach. These lead-zirconium titanate transducers are used twice: as generators of stress waves and as the sensors that respond to these when they travel about the structure. Traditional EMI techniques can identify whether damage is present but cannot estimate the extent of damage. Statistical analysis techniques like root mean square deviation are employed for measuring the magnitude of change from the original or healthy state of the structure. However, RMSD will only indicate the level of damage but not locate it. This paper proposes a new approach to setting threshold levels for categorizing damage into stages incipient, moderate, and severe based on changes in the structure’s admittance signature. Experiments were conducted on a beam measuring 500×100×100 mm, with controlled artificial damage introduced to simulate varying levels of severity. As continuous damage increases, the equivalent stiffness decreases. By analyzing anomalies in stiffness changes, we can identify threshold levels of damage in incipient, moderate, and severe conditions. The established threshold levels not only support early damage detection but also facilitate more efficient maintenance interventions, ultimately saving lifecycle cost and enhancing overall structural reliability. These findings contribute to structural health monitoring and engineering applications, providing valuable insights for innovative research directions.