Modeling Studies on the Development of a System for Real-Time Magnetic-Field Imaging of Steel Reinforcing Bars Embedded Within Reinforced Concrete

This paper addresses fundamental issues associated with the development of a real-time magnetic-field imaging system for nondestructive testing of prestressed and reinforced concrete. Modeling results have shown that with a square coil of 300 times300 times2.5 mm³, 10-mm rebars can be imaged down to a depth of 100 mm. Studies also indicate that the vertical component of the induced magnetic field is most favorable as it can readily be reconstructed to yield geometry and dimensional information pertaining to the rebar structure.     

Multiple layer separation and visualisation of inductively scanned images of reinforcing bars in concrete using a polynomial-based separation algorithm

Inductive scanning systems that exploit eddy current effects for imaging steel reinforcing bar mesh within concrete have been developed and reported by the authors in several publications. Images generated in this manner depict the different horizontal and vertical layers of the mesh within a single, 2D plane. Deeper bars appear as much fainter structures than those closer to the surface for two reasons: first, the signals they generate are weaker, and second, the image is linearly normalised with respect to the much stronger signals returned from the upper bars. This makes depth and dimensional analysis of deeper bars a severe problem. Below we describe a new suite of image processing algorithms that enables the original composite image to be visualised as separate, multiple images representing the various bar layers. This technique is termed polynomial-based layer separation (PBLS). The method also makes it possible to perform brightness-compensation of the lower bars and is a precursor to analysis that allows measurement of the diameters, orientations and depths of the bars. This information is critical for civil structures inspection teams. Knowing the spatial location of the bar peak widths in one layer, curve fitting is applied to calculate the baselines of the bars in other layers of the image. For a two-layer image, the peaks in the lower layer are removed leaving an image of the top bars, and vice versa for the bottom bars. Images of steel bar mesh processed using this PBLS system offer significant enhancements to the qualitative and quantitative properties of the original image data, and in tests described below, is significantly more robust than comparable methods of image segmentation.     

Structural Health Monitoring Using Lamb Wave Reflections and Total Focusing Method for Image Reconstruction

This investigation aimed to adapt the total focusing method (TFM) algorithm (originated from the synthetic aperture focusing technique in digital signal processing) to accommodate a circular array of piezoelectric sensors (PZT) and characterise defects using guided wave signals for the development of a structural health monitoring system. This research presents the initial results of a broader study focusing on the development of a structural health monitoring (SHM) guided wave system for advance carbon fibre reinforced plastic (CFRP) composite materials. The current material investigated was an isotropic (aluminium) square plate with 16 transducers operating successively as emitter or sensor in pitch and catch configuration enabling the collection of 240 signals per assessment. The Lamb wave signals collected were tuned on the symmetric fundamental mode with a wavelength of 17 mm, by setting the excitation frequency to 300 kHz. The initial condition for the imaging system, such as wave speed and transducer position, were determined with post processing of the baseline signals through a method involving the identification of the waves reflected from the free edge of the plate. The imaging algorithm was adapted to accommodate multiple transmitting transducers in random positions. A circular defect of 10 mm in diameter was drilled in the plate, which is similar to the delamination size introduced by a low velocity impact event in a composite plate. Images were obtained by applying the TFM to the baseline signals, Test 1 data (corresponding to the signals obtained after introduction of the defect) and to the data derived from the subtraction of the baseline to the Test 1 signals. The result shows that despite the damage diameter being 40 % smaller than the wavelength, the image (of the subtracted baseline data) demonstrated that the system can locate where the waves were reflected from the defect boundary. In other words, the contour of the damaged area was highlighted enabling its size and position to be determined.     

A coded approach to system identification using a modified Golay FIR filter

Several techniques are currently in use for system identification, each having its own level of performance. In this paper we present a coded interrogation approach for statistical system identification; it is based around a modified Golay sequence applied to an adaptive finite impulse response (AFIR) filter system. We show that this technique circumvents some of the shortfalls associated with other comparative approaches. The theory of the proposed technique is developed, and the operating principle of a prototype model to verify it is described. Analysis of the results obtained from experiments, performed on a simple first-order linear time invariant (LTI) system, gave an average deviation of +/- 2.94% from the ideal response.     

A Preliminary Magnetoinductive Sensor System for Real-Time Imaging of Steel Reinforcing Bars Embedded Within Concrete

This paper studies the feasibility of using solid-state magnetoinductive probes for detecting and imaging steel reinforcing bars embedded within prestressed and reinforced concrete. Changes in the inductance of the sensor material are directly proportional to the strength of the measured magnetic field parallel to the sensor. Using a square coil of 300 mm times 300 mm times 2.5 mm, 10-mm rebars can be imaged down up to a depth of 100 mm. Experimental results obtained by scanning steel bar specimens are presented. General performance characteristics and sensor limitations are also investigated.     

Detection of Magnetic Fields Highly Attenuated by the Skin Effect Through a Ferrous Steel Boundary Using a Super Narrow-Band Digital Filter

A complete instrumentation system is described that is capable of launching alternating magnetic fields through a large mild steel plate that is 2 mm in thickness and detecting them on the face opposite from the transmitter with remarkable signal-to-noise ratios. Results for signal frequencies ranging between 4.5 and 13 kHz are reported. The skin depth at 9 kHz for the steel used is approximately 137 mum. The detection of the minute fields arriving at the receiving coil is made possible by the use of digitally synthesized input signals, low-noise amplification, and, in particular, the use of a powerful real-time digital signal processing system that isolates the signal of interest using a super narrow-band filter and very high levels of distortion-free gain. Although traditional methods of weak signal detection, such as lock-in amplification, may also be applied in this context, the digital approach discussed here is both more cost effective and flexible, allowing the simultaneous detection of multiple frequencies.     

Extracting dimensional information from steel reinforcing bars in concrete using neural networks trained on data from an inductive sensor

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.     

Vector extraction from digital images of steel bars produced by an inductive scanning system using a differential gradient method combined with a modified Hough transform

A method of extracting positional information from images of steel bars embedded in concrete using a set of image pre-processing algorithms combined with a modified Hough transform is described. The images are formed using a portable inductive scanning system in which a sensing element detects the magnetic field emanating from the bars. Eddy currents induced on the surfaces of the bars by excitation coils generate these magnetic fields. The results from an edge detection algorithm based on the two-dimensional separable smoothing and local maxima detection are applied to a two-stage Hough transform which locates the major axes of the bars and hence their alignment and number. An effective method of minimising the redundant information in the Hough array is also described. The complete system incorporates fully integrated control, acquisition and processing software, which makes it convenient for on-site use.     

Detection and imaging of surface corrosion on steel reinforcing bars using a phase-sensitive inductive sensor intended for use with concrete

A new inductive sensor is described that exploits the principle of frequency shift and phase sensitive detection to identify and image corrosion on the surface of steel reinforcing bars intended for embedment within concrete. A search coil radiating a time-varying magnetic field experiences impedance changes when conductive and/or permeable targets are brought within its vicinity. Depending on the electrical properties of the target, the impedance changes are manifest predominantly as a shift in the Q-factor of the coil, or as a change in its inductance. It is the latter condition that is the primary effect with corrosion product. Under normal circumstances the change in inductance L is very small, but can be detected by configuring the coil as part of a free-running tuned oscillator, whose resonant frequency is governed by L. A shift in the sensor signal frequency is detected by comparing its phase to that of a signal produced by a stable reference oscillator (heterodyning), and producing a voltage proportional to the difference. Such phase sensitive detection is widely employed in metal detector instrumentation, but in this case the signal is used to produce images of corrosion. Significantly, experiments also show that the system is capable of detecting varying amounts of corrosion product, something that is beyond the capabilities of ultrasonic, X-ray or microwave sensing systems. At the present time, the sensor is capable of detecting and imaging a 2 mm thick layer of corrosion on a 20 mm diameter steel bar, located 30 mm below the surface of ceramic material whose conductivity and relative permeability is almost identical to that of concrete.