Fatigue cracking detection in steel bridge girders through a self-powered sensing concept

Development of fatigue cracking is affecting the structural performance of many of welded steel bridges in the United States. One of the main sources of fatigue cracking is out-of-plane distortion occurring at connections of transverse structural members and longitudinal girders. Distortion-induced fatigue cracks mostly occur in older bridges with members prone to fatigue. Prediction of secondary stresses in these members is difficult using conventional design specifications. This limitation suggests the necessity of utilizing new strategies to analyze the damage caused by distortion-related cracking. This study presents a new approach for detection of distortion-induced fatigue cracking of steel bridges based on the interpretation of the data provided by a newly developed self-powered piezo-floating-gate (PFG) sensor. The PFG sensors are empowered using piezoelectric transducers through harvesting energy from the mechanical loading experienced by the structure. In order to assess the performance of the proposed sensing system, three-dimensional finite element models were developed and the structural response of the girder was subsequently obtained. The fatigue life of the girder was determined based on J-integral concept and Paris Law. Several damage states were defined by extending the fatigue crack lengths. Thereafter, features representing the PFG sensor output were extracted from the strain data for different sensing nodes to detect the damage scenarios. Furthermore, a new data fusion concept based on the effect of group of sensors was proposed to improve the damage detection performance. The results indicate that the proposed method is capable of detecting different damage progression states. This is specifically evident for the sensors that are located close to the damage location. The acceptable performance of the proposed sensing system implies its applicability for other modalities of infrastructure/structural health monitoring (I/SHM).