Multi sensor data fusion approach for automatic honeycomb detection in concrete |
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| Affiliation: | 1. BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany;2. Department of Civil and Environmental Engineering, The Pennsylvania State University, 215 Sackett Bldg., University Park, PA 16802, USA;1. Instituto de Física, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia;2. Instituto de Física, Universidade Federal de Alagoas, Maceió-AL 57072-970, Brazil;3. Instituto de Física, Universidade Estadual de Campinas - Unicamp, Campinas - SP 13083-859, Brazil;1. Allen Price & Scarratts Pty Ltd, Australia;2. University of Wollongong, Australia;3. Research and Technical Services, Cement Concrete & Aggregates, Australia;1. Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, 122 NH, Lincoln, NE 68588, USA;2. Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln, 113 NH, Lincoln, NE 68588, USA |
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| Abstract: | 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. |
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| Keywords: | Data fusion Concrete evaluation Honeycombing |
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