Automatic assessment of freeze-thaw damage in concrete structures using piezoelectric-based active sensing approach and deep learning technique

Concrete structures in cold climates are susceptible to freeze-thaw (f-t) damage, which could lead to structural deterioration and stability issues. Accurate assessment of such damage is crucial for effective maintenance and repair strategies. However, current methods for evaluating concrete f-t damage rely on tedious testing procedures that lack automation and real-time capabilities, potentially delaying the repair process. This study proposes an automatic method to assess f-t damage in concrete by combining piezoelectric-based active sensing and deep learning (DL). Firstly, concrete beams with two different force states were prepared and subjected to different f-t cycles, during which measurements including surface characteristics, mass loss, dynamic elastic modulus and stress wave were performed. The relationship between concrete f-t damage evolution and stress wave signal variation was investigated. The collected signals were then converted into time-frequency maps using continuous wavelet transform (CWT), thereby establishing a CWT image-based dataset. More importantly, an innovative DL model (DSC-ACGRU) that integrates depth-wise separable convolution (DSC), convolutional gated recurrent unit (ConvGRU) and attention mechanism (AM) was developed to automatically extract damage-sensitive feature from CWT images and ultimately predict the degree of concrete f-t damage. Finally, the performances of the proposed DSC-ACGRU model were experimentally evaluated and compared with five machine/deep learning methods. The results show that the proposed model can rapidly and accurately assess the f-t damage degree in concrete and outperforms other learning algorithms, which is significant for identifying potential hazards and repairing damaged concrete.