## 2 Measuring Systems, Materials and Methods  
### Methodology  
The comprehensive research methodology was originally detailed in [44].  
Knowledge acquisition from measurements obtained through non-destructive testing (NDT) can be conducted according to an algorithm similar to the classic CRISP-DM model (Cross-Industry Standard Process for Data Mining). The CRISP-DM framework is an iterative process consisting of seven steps and some feedback loops [56].  
The first step is to thoroughly understand the problem and the physical phenomena related to the utilized NDT method. This phase includes an extensive literature review and simulation studies. The simulations employed the finite element method using the commercial software COMSOL v6.0. These tests are designed to assess the theoretically developed concepts and to create preliminary versions of the laboratory system. An additional critical role of the simulations is to provide data that can be utilized to verify and validate the measurement results obtained.  
The subsequent step involves the preparation of samples and the configuration of the measurement systems.  
In the next step, data are obtained and interpreted. During this phase, measurements are conducted by moving the sensor over the sample perpendicularly to the tested reinforcing bar. In the case of the eddy current method, both the sensor and the excitation are moved together. Conversely, only the sensor is moved for the magnetic method while the excitation system remains positioned directly above the reinforcing bar. Next, attributes representing waveforms are defined and utilized to extract association rules. The rules link variations in the waveform to corresponding changes in the adequate sample physical parameters. Association rules are derived using visualization techniques, statistical analyses, and specialized algorithms. Additionally, the measurement results are compared with simulation data at this stage. Preliminary analyses often indicate the need for adjustments to the laboratory setup and may necessitate repeated measurements, frequently requiring multiple iterations.  
In the third step, data preparation is conducted to enable effective identification through machine learning. This process may include outlier removal, normalization, quantization, discretization, and curve smoothing.  
The modeling stage focuses on developing the identification model. During this phase, attributes are processed and selected. Techniques such as aggregation, filtering, wrappers, and more advanced methods like rough set theory or principal component analysis (PCA) can be employed during the process. Then, various classifiers and identification algorithms are tested to select the best method.  
The subsequent stage is evaluation. This step is intended to address any remaining uncertainties and validate the suitability of particular solutions and configurations. If the model receives a negative assessment, it will require a thorough re-evaluation of all prior stages.  
The final step involves model deployment. During this phase, real objects with unknown parameters are tested.  
Figure 2 illustrates the sequential stages of knowledge acquisition and the most frequently encountered feedback loops inherent to this model, which has been adapted to the specifics of NDT.