Non-destructive methodology for crack detection using machine learning-assisted resonant sensor

Abstract

Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual-scale resonance-based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro-wave (4.59 GHz) and radio-wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift ∼24 %, while the radio-wave range sensors showed ∼9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed ∼280 % more sensitivity than the radio-wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures.