Inspection of bridge decks is of primary importance in the field of bridges maintenance since, unless other structural components, they are more subjected to degradation and traffic-induced deterioration phenomena. Among the various deterioration mechanisms, delaminations are generally difficult to detect because no visible effects are usually observed on the deck surface. Since the entity of the damage progressively increase during time, methodologies able to effectively detect delaminations are needed in order to design appropriate solutions and reduce maintenance costs. In this work, the results obtained using two different nondestructive techniques, namely the impact echo (IE) method and the infrared thermography (IR), are compared. Experimental tests have been performed on a 20cm thick concrete slab containing delaminations of various extensions and on a small 60cm×60cm×20cm concrete specimen. Impact echo tests have been performed, with ultrasonic waveforms collected on an orthogonal grid of points spaced 30cm apart. Spacing was reduced to 5 cm for IE data collection in the small block. Leveraging different features extracted from IE, delaminations have been located. The results obtained using the impact echo test have been compared with those extracted using the infrared thermography. The main concept behind the use of the IR is that embedded horizontal interfaces behave as heat traps, resulting in different temperature areas on the slab surface. A discussion on the pro and cons of the two methodologies is provided and the paper ends with a preliminary attempt to perform data fusion, combining the results from the 2 different nondestructive techniques.
This paper represents a hybrid non-destructive testing (HNDT) approach based on infrared thermography (IRT), acoustic emission (AE) and ultrasonic (UT) techniques for effective damage quantification of partially grouted concrete masonry walls (CMW). This integrated approach has the potential to be implemented for the health monitoring of concrete masonry systems. The implementation of this hybrid approach assists the cross validation of in situ recorded information for structural damage assessment. In this context, NDT was performed on a set of partially grouted CMW subjected to cyclic loading. Acoustic emission (AE) signals and Infrared thermography (IRT) images were recorded during each cycle of loading while the ultrasonic (UT) tests were performed in between each loading cycle. Four accelerometers, bonded at the toe of the wall, were used for recording waveforms for both passive (AE) and active (UT) acoustics. For the active approach, high frequency stress waves were generated by an instrumented hammer and the corresponding waveforms were recorded by the accelerometers. The obtained AE, IRT, and UT results were correlated to visually confirm accumulated progressive damage throughout the loading history. Detailed post-processing of these results was performed to characterize the defects at the region of interest. The obtained experimental results demonstrated the potential of the methods to detect flaws on monitored specimens; further experimental investigations are planned towards the quantitative use of these NDT methods.
Reliable damage detection and quantification is a difficult process because of its dynamic and
multi-scale nature, which combined with material complexities and countless other sources of
uncertainty often inhibits a single non-destructive testing (NDT) technique to successfully
evaluate the extension of deterioration in critical structural components. This paper presents an
integrated non-destructive testing approach (INDT) for effective damage identification relying
on the intelligent integration of the Acoustic Emission (AE), Guided Ultrasonic Waves (GUW)
and Digital Image Correlation (DIC) methods. The proposed system has been utilized to identify
wire breaks in seven-wire steel strands and crack initiation and development in masonry concrete
walls and is based on the cross-correlation of heterogeneous damage-related NDT features.
Conventional AE monitoring relies on damage monitoring by evaluating multiple extracted
and/or computed features as a function of load/time. In addition, advanced post-processing
methods including mathematical algorithms for statistical analysis and classification have been
suggested to improve the robustness of AE in damage identification. Unfortunately, such
approaches are often found to be unsuccessful, due to challenging environmental and operational
conditions, as well as when used on actual civil structural components, such as bridge cables and
masonry walls. This paper presents the framework for successful correlation of AE features with
GUW and mechanical parameters such as full field strain maps, which can provide a route
towards actual cross-validated damage assessment, capable to detect the initiation and track the
development of damage in structures. The presented INDT approach could lead to reliable
damage identification approaches in mechanical, aerospace and civil infrastructure applications.
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