Cluster-based sensor selection framework for acoustic emission source localization in concrete

The acoustic emission (AE) method can determine the location of damage initiation and progression in largescale structures using an array of sensors. As the location accuracy depends on proper identification of time of arrival and wave velocity, the method is more successful in homogeneous and isotropic materials than in heterogeneous materials such as concrete. The heterogeneity causes dispersive and attenuative properties such that the source-sensor distance and angle control the AE signal characteristics influencing the source location accuracy. Generally, using more than the minimum number of sensors in the location algorithm results in more accurate source localization. If the AE signal of a sensor is significantly different from the other sensors, its time of arrival may not contribute beneficially to the source localization algorithm. On the contrary, this may increase errors in determining arrival time due to signal distortion caused by dispersion, low signal-to-noise ratios resulting from attenuation, or sensors being occupied with detecting nearby noise signals. In this paper, a new cluster-based sensor selection framework is developed for selecting the best sensor combinations before applying the source localization algorithm. The framework involves selecting the best combination of sensors to input into the source localization algorithm based on the cross-correlation characteristics of signal features for single AE events or cluster analyses for large datasets. Both approaches identify the signal similarity of sensors to be used in the location sensor group. As two-dimensional source localization requires a minimum of three sensors, the number of sensor outputs extracted from the sensor selection framework is bounded by three. The source localization accuracy is evaluated using the data collected impact excitation with the known source location and structural tests of a Basalt Fiber-Reinforced Polymer (BFRP) reinforced concrete slab. The impact experiments show that the new framework to select the best three-sensor combination increases the localization accuracy to 93%, unlike the results obtained by using all six sensors (80%) and the first three sensors in the hit sequence (75%). The framework is applied to the AE data recorded from actual damage initiation and progression in a simply supported BFRP-reinforced concrete slab. The cluster of events accumulates more densely at the midspan, where the crack initiation is validated with crack sensors and images. It is demonstrated that selecting the sensors for two-dimensional source localization using similarity analyses improves the accuracy of the source location in concrete.