Zaid, M., Gaydecki, P., Quek, S., Miller, G. and Fernandes, B. (2004) Extracting dimensional information from steel reinforcing bars in concrete using neural networks trained on data from an inductive sensor. NDT & E International.37(7), pp. 551-558. 0963-8695.
Zaid, M., Gaydecki, P., Quek, S., Miller, G. and Fernandes, B.
A new method is presented for extracting dimensional information from steel bars using images generated by an inductive sensor. The technique is based on the application of two feedforward backpropagation neural networks; one to estimate bar depth and the other to estimate bar diameter. Both of the networks have been trained on a set of data that consists of the peak parameters of six different bars scanned at 41 different bar depths. These input and target data must be pre-processed to obtain a good network generalisation. By testing the two networks with a completely different set of data, accurate performance has been obtained. Real, two-dimensional scan data have then been applied to both of the networks and the bar dimensional parameters have been extracted successfully. The advantage of the neural network method for extracting information is that it continues to operate reliably for very deep bars, for which the signal strength is severely attenuated and manifests a poor signal-to-noise ratio. Depth and diameter measurements have been obtained for bars located down to 58 mm, with errors that satisfy the requirements of the BS 1881 standard. At a depth of 40 mm, these measurements yield an error of ±4%, and this decreases as the depth reduces; in other words, the extracted bar diameter is within the requirements of the DIN 488 standard
Two dimensional model ; Backpropagation algorithm ; Neural networks ; DIN standard ; Signal-to-noise ratio ; Mechanical strength ; Information networks ; Feedforward ; Data transmission ; Reinforcing bar ; Reinforced concrete ; Steel concrete ; Measurement errors ; Metering ; Induction transducer ; Metrology ; Nondestructive testing