Multi sensor data fusion approach for automatic honeycomb detection in concrete

•We laid out a novel conceptual framework for multi-sensor defect classification.•No well-established technique for honeycomb detection.•New methods to extract information (features) from each sensor data are presented.•We introduce two conceptually simple yet effective fusion algorithms.•A quantitative evaluation is performed by comparing ROC curves.

We present a systematic approach for fusion of multi-sensory nondestructive testing data. Our data set consists of impact-echo, ultrasonic pulse echo and ground penetrating radar data collected on a large-scale concrete specimen with built-in honeycombing defects. From each data set, the most significant signatures of honeycombs were extracted in the form of features. We applied two simple data fusion algorithms to the data: Dempster’s rule of combination and the Hadamard product. The performance of the fusion rules versus the single-sensor testing was evaluated. The fusion rules exhibit a slight improvement of false alarm rate over the best single sensor.