Acoustic emission monitoring and fatigue prediction of steel bridge components

Acoustic emission (AE) has been recognized for its unique capabilities as an NDT method. However, there is untapped potential for the practical application of AE to structural health monitoring and prognosis. As part of the development of a wireless sensor network for structural bridge health monitoring, this study aims to provide a framework for the estimation of fatigue damage and remaining life of steel bridge components through AE monitoring. Fourteen compact tension (CT) specimens and nine cruciform fillet welded joints were used in AE monitored fatigue tests to investigate the correlation of AE features with crack growth in base materials and weldments. The material (structural steel A572 Grade 50) and the welding procedures are representative of those used in actual bridge construction. Based on the balance between AE signal energy and the energy release due to crack growth, deterministic models are presented to predict crack extension and remaining fatigue life for stable and unstable crack stages. The effect of weld length and fatigue load ratio on the AE activity is evaluated. The presence of noise is inevitable in the application of AE monitoring. The efficiency of data filtering and reduction algorithms is key to minimize the power and data storage demand of the wireless sensing system. AE data filtering protocols based on load pattern, source location, waveform feature analysis, and pattern recognition are proposed to minimize noise-induced AE and false indications due to wave reflections.