Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen

Abstract

To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.

Approaches to X-ray CT Evaluation of In-Situ Experiments on Damage Evolution in an Interpenetrating Metal-Ceramic Composite with Residual Porosity

“…Especially for systematic crack pattern, breakthroughs could be achieved. But a lot of data is necessary, as well as training time and manual work [18] and control of the network: Paetsch for example, who discussed the possibilities and limitations of automatic feature extraction in 3D-CT images of concrete specimen, states, that a parameter-free out-of-the-box detection of cracks with the method he presented is not recommendable [20]. Next to the question of effort of cnn's, also the microstructure of the investigated material, as well as the damage development plays a role for the success of the approach.…”

“…Restrictions in the evaluation on certain material system and the exclusion of more general or isotropic material systems accompany with this evaluation approaches, which makes it difficult for most of interpenetrating phase composites (IPC). In the investigation of Paetsch [20] for example, the machine learning approach requires a clear crack structure. Tian et al [19] state, next to their achievements in segmentation limitations of the algorithm in image resolution, shape, and size.…”