An increasing number of tubular steel structures have exceeded their design service lives; hence, monitoring of these structures is crucial in preventing any unforeseen failures and corresponding catastrophic consequences - safety or economic. As is well known, vibration-based structural health monitoring (SHM) presents non-destructive methods for damage identification, though their application in corrosion problems appears somewhat limited. Furthermore, majority of the SHM techniques reported in literature deal with prismatic or beam-like members; tubular structures have received less research attention. In this paper, numerical models of a pipe in its intact and corroded conditions are built and analysed using ABAQUS®. Modal parameters extracted from analyses results are utilised to detect, locate and quantify corrosion. A potential indicator “Normalised Displacement Modeshape (NDM)” is introduced and tested alongside existing damage indicators. Results from the employed indicators are compared and the capabilities of each indicator in identification of the investigated corrosion patterns are discussed.
As structural systems approach their end of service life, integrity assessment and condition monitoring during late life becomes necessary in order to identify damage due to age-related issues such as corrosion and fatigue and hence prevent failure. In this paper, a novel method of level 3 damage identification (i.e. detection, localisation and quantification) from local vibration mode pair (LVMP) frequencies is introduced. Detection is achieved by observation of LVMP frequencies within any of the vibration modes investigated while the location of the damage is predicted based on the ranking order of the LVMP frequency ratios and the damage is quantified in terms of material volume loss from pre-established quantification relations. The proposed method which is baseline-free (in the sense that it does not require vibration-based assessment or modal data from the undamaged state of the pipe) and solely frequency-dependent was found to be more than 90% accurate in detecting, locating and quantifying damage through a numerical verification study. It was also successfully assessed using experimental modal data obtained from laboratory tests performed on an aluminium pipe with artificially inflicted corrosion-like damage underscoring a novel concept in vibration-based damage identification for pipes.
Tubular structures are widely used in infrastructure, especially for pipes and marine installations. Corrosion is a key damage type for these structures, and if undetected may cause structural deficiency or even collapse. This paper investigates the effects of localised corrosion on the vibration properties of axisymmetric tubular structures and proposes a novel baseline-free damage identification method to overcome the fact that the difference between undamaged and damaged properties is usually small and contaminated by noise. The proposed vibration-based method exploits the phenomenon of repeated mode pairs which is peculiar to axisymmetric tubular structures with non-axisymmetric damage. Unlike intact tubular structures, it is found that each vibration mode of a locally damaged tubular structure comprises of two components: active and passive. The active component is sensitive to the local damage and can be used to infer damage existence while the passive component is retentive of the undamaged pipe's properties. Therefore, the passive component of a damaged pipe’s local vibration mode pair (LVMP) can be used to predict its baseline (undamaged) modal properties, which in this research was achieved within a margin of ±2%, whereas the active component confirms damage existence. The work also establishes the criteria for validation of LVMPs through experimental tests involving various corrosion scenarios ranging from longitudinal strips to through-thickness pits. Based on the obtained results, it is proposed that the properties of LVMPs can be linked to corrosion-like local damage in axisymmetric tubular structures, underpinning a new damage detection and identification approach.