Multi sensor data fusion approach for automatic honeycomb detection in concrete

Christoph Völker, Parisa Shokouhi

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)54-60
Number of pages7
JournalNDT and E International
StatePublished - Apr 2015

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Condensed Matter Physics
  • Mechanical Engineering


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