This article evaluates two essential non-destructive electromagnetic techniques, magnetic flux leakage (MFL) and eddy current (EC) methods, and their effectiveness in assessing the basic parameters of reinforced concrete (RC). The study compares both systems’ hardware and software components, emphasizing the adaptations implemented to tailor the methods for evaluating RC structures. Subsequently, the measurement results are analyzed, and association rules are extracted to demonstrate the relationships between variations in the physical parameters of the tested structure and the features of the measured waveforms. Finally, similar identification models are implemented, and the obtained identification results are compared. The paper documents and details all phases of this research. The findings indicate that while the operational principles of both methods are similar, the techniques differ significantly in terms of their measurement systems’ complexity and usability. The eddy current (EC) method exhibits superior spatial resolution, whereas the magnetic method is more straightforward and offers a greater effective range and favorable association rules. Consequently, it is recommended that both techniques be utilized for different structures and in varying contexts. The techniques’ advantages, disadvantages, and limitations are discussed in this work and supported by the measurement results.

This paper aims to present a non-destructive magnetic method developed for simultaneously identifying key parameters in reinforced concrete (RC) structures. This method is designed to evaluate concrete structures containing dense reinforcement meshes. The development process of the system is presented step by step. First, the hardware layer of the system is discussed and documented. The proposed modifications to the excitation system enhanced the method’s effectiveness compared to the conventional Magnetic Flux Leakage (MFL) technique. Next, this paper presents the extracted association rules that connect the parameters of the RC structure to the properties of resultant waveforms. The relations are verified with measurement results. Finally, the identification of the model’s design, implementation, and deployment processes are presented. A universal methodology for non-destructive testing investigations was developed and is described throughout the research.

This study aims to develop a novel AI-driven approach for detecting and evaluating corrosion in reinforced concrete (RC) structures, answering one of the most significant challenges in the construction industry. The research aims to overcome the difficulties of identifying corrosion with limited data. Gathering representative learning databases is challenging due to problems obtaining adequate samples and the high diversity in rebar, concrete, and structural parameters. The research quantitatively analyzes measurements obtained through Magnetic Force Induced Vibration Evaluation (M5), a nondestructive testing (NDT) method. The process is enhanced by employing the specialized Association Rules Analysis (ARA) with a dedicated feature extraction technique. The findings suggest that utilizing a variety of patterns and features enhances the method's identification effectiveness.