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The applicability of pulse phase thermography (PPT) for the investigation of structures is studied systematically on concrete test specimens and on a plastered sandstone column. In the test specimens, voids and delaminations are implemented in different depths and with different sizes, modelling real voids, honeycombing and debonding. Delaminations of plaster in concrete and masonry and behind tiles on concrete are investigated. PPT is based on the frequency analysis of the cooling down process of actively heated surfaces. Therefore, it is contactless and thus completely non-destructive (if overheating of the surface is prevented), fast and allows the inspection of large surface areas. The interpretation of amplitude and phase images gives semi-quantitative information about the observed defects. The phase images provide a deeper probing up to 1015 cm in relation to the interpretation of the thermograms and to the amplitude images. In addition, the influence of surface inhomogeneities and non-uniform heating is reduced.
We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other.
We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
Impulse thermography is an active method for quantitative investigations of the near
surface region of various structures. It has recently been applied and optimized to applications in civil
engineering. For quantitative analysis of data recorded on the building site, the problems are manifold.
Here, the influence of the different surface properties on the detection and characterization of voids
and honeycombing simulated by polystyrene cubes and cubes prepared of porous concrete are
demonstrated.