Clustering Based Multi Sensor Data Fusion for Honeycomb Detection in Concrete

Christoph Völker, Parisa Shokouhi

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


We use three clustering algorithms to aggregate a three-modal non-destructive testing data set into defect and not-defect groups. Our data set consist of impact-echo, ultrasound (US) and ground penetrating radar data collected on a large concrete slab with embedded simulated honeycombing defects. US performs best in defect discriminating and sizing, however the false positive rate is still high. We fuse the data set using K-Means, Fuzzy C-Means and DBSCAN clustering at feature-level. We discern that DBSCAN improves the detectability up to 10 %. A discussion of its advantages over commonly used K-Means and Fuzzy C-Means clustering are provided.

Original languageEnglish (US)
Article number32
Pages (from-to)1-10
Number of pages10
JournalJournal of Nondestructive Evaluation
Issue number4
StatePublished - Nov 1 2015

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering


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