Non-Destructive Classification of Concrete Moisture Levels Using Piezoelectric Contact Microphones and Impact-Based Acoustic Signals with a Hybrid Stacking Framework: A Controlled Experimental and Theoretical Study

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The long-term durability of concrete structures is significantly affected by moisture. Excessive moisture may cause drying shrinkage, crack formation, and accelerated corrosion of embedded reinforcement; therefore, reliable and non-destructive moisture assessment is essential for structural durability evaluation. In this study, a controlled acoustic measurement method and a machine learning-based classification framework are presented for the non-destructive identification of moisture levels in concrete specimens. A magnet-assisted free-fall steel ball mechanism was used to generate standardized impacts instead of conventional manual hammer excitation. To reduce environmental vibration noise and capture internal material responses, acoustic signals were recorded using a piezoelectric contact microphone. Experiments were conducted on concrete specimens prepared at nine moisture levels under both large-sample (BIG) and small-sample (SMALL) conditions. Power Spectral Density (PSD) and Mel-Frequency Cepstral Coefficients (MFCC) were extracted from the recorded impact signals and used as input features. Individual machine learning classifiers were compared with a hybrid stacking ensemble model to evaluate discriminative performance and probabilistic reliability. The results showed that MFCC features provided higher classification performance than PSD features under both dataset conditions. For the BIG specimens, the MFCC-based model achieved an accuracy of 0.9872, whereas the PSD-based model achieved 0.9811. For the SMALL specimens, MFCC reached an accuracy of 0.9822, while PSD achieved 0.9750. The AUC-ROC values of the proposed model ranged from 0.9980 to 0.9996 in the multi-class classification of nine moisture levels. These findings demonstrate that controlled impact acoustics combined with MFCC-based representation and stacking-based ensemble learning provides a rapid, low-cost, and reliable NDT approach for concrete moisture classification.

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