Research on total focusing method for concrete defect detection based on coherence weighted fusion

Concrete is a composite material composed of multi-phase heterogeneous components such as aggregates and cement paste. While the complex acoustic impedance interfaces within it provide the physical basis of ultrasonic defect detection， they also generate strong scattering noise and significant ultrasonic energy attenuation， imposing considerable limitations on the imaging quality of ultrasound-based detection. Although the total focusing method （TFM） technology can improve the resolution through full matrix capture （FMC） data acquisition， but it remains susceptible to noise and artifacts in strongly scattering media such as concrete. In order to further enhance TFM quality， a TFM algorithm based on coherence weighted fusion （CWF） is proposed. This method aims to maximize the use of the scattered field contained in the FMC data to amplify the difference between coherent and incoherent signals， thereby enhancing the final imaging resolution and defect identifiability. Numerical simulations on a meso-scale concrete model and experimental results show that the proposed CWF-TFM optimization algorithm can effectively suppress certain noise and artifacts， improve the longitudinal resolution， and concentrate signals in defect areas. When using a 50 kHz excitation signal to detect a single crack defect， the signal-to-noise ratio （SNR） is improved by 30.0% compared with traditional TFM. For single void defect detection， the SNR is increased by 32.2% over traditional TFM. and for multiple defects， the SNR shows a 22.7% enhancement. This achievement overcomes the modal limitations of traditional TFM in concrete defect detection， significantly improving image SNR and defect identifiability， and provides a new approach for high-resolution ultrasonic detection of concrete defects.