Several methods have been proposed in the literature to interpret data obtained from GPR scans. The earliest approach identified irregular signatures in profiles as concrete deterioration typically present in a depressed shape [11]. Subsequently, individual waveforms at the concrete/asphalt level were analyzed in several bridge decks and a rating system was developed to classify the deck condition [12,13]. Numerous researchers analyzed attenuation signals based on amplitude values and compared their effectiveness in measuring concrete deterioration [14,15,16,17,18]. To improve results of amplitude-based analysis, the following advances were proposed: (a) analyzed signal depth–amplitude effects [19], (b) measured change in amplitude values over time [10], and (c) correlated original waveforms with semi-simulated waveforms at deterioration state [20]. An experimental study was conducted to assess the reliability of GPR in detecting delamination [21]. Some recent methods for complete data assessment can be summarized as follows: (a) analyzed amplitude values with synthetic aperture focusing technique (SAFT) and 3D interpolation [22], (b) compared A-scans at different locations [23], (c) utilized step-frequency GPR antenna arrays [24], (d) applied frequency–wavenumber (F–K) migration and several other algorithms to detect major defects (delamination, air voids and moisture) [25], (e) statistically analyzed concrete relative permittivity over multiple years [26], (f) evaluated three parameters (direct-coupling, amplitude, wave velocity, and signal attenuation) at different depths [27], and (g) used dual-polarization antenna for scanning and developed SAFT-based algorithms for 3D imaging of concrete structures to detect objects, defects, steel bar debonding, and concrete delamination [28].

The commonly adopted approach by transportation authorities to analyze GPR data is based on ASTM D6087 [4]. After obtaining GPR profiles from scanning a bridge element required to be inspected, this method involves picking reflection amplitude values in regular intervals across B-scans at either of the two locations: (a) concrete–rebar interface, i.e., the peak of hyperbolas or (b) relative amplitudes from the bridge deck bottom relative to the bridge deck surface. Subsequently, a condition map is generated for the complete bridge element by mapping these amplitude values. Higher values of amplitudes in this map indicate locations of good conditions of concrete while lower values indicate locations of moderate-to-bad conditions of the concrete. Bridge inspectors can use such a map to identify deteriorated areas for further inspection and repairs.

The methods based on numerical amplitude values have many limitations. Firstly, by considering only amplitude values in a B-scan, they ignore the majority of the information contained in a radar profile. Secondly, they can yield erroneous results due to factors affecting amplitudes such as depth, surface anomalies, reinforcing bar spacing/configuration, and others. Thirdly, the condition map generated is noisy and not completely reliable when compared with the actual condition of concrete with destructive measures such as core sampling [5]. To overcome these shortcomings, an alternative approach proposed by Tarussov et al., 2013 [5] considers GPR B-scans as an imaging tool rather than a numeral measuring tool. In this visual-IBA, an experienced analyst marks attenuated areas in a B-scan manually while considering several factors such as reflection amplitudes at the concrete/rebar and bottom of slab levels, surface and structural anomalies, variations in slab thickness, reinforcement bar spacing, etc. For example, Figure 2 shows a typical GPR profile wherein moderate zones of deterioration are marked in yellow color by the analyst on top of the profile while severe zones are marked in red. The deterioration maps generated based on this approach have the following benefits: (a) it maps exact limits for zones of corrosion; (b) it reduces noise in results through visual filtering; (c) it accounts for depth-correction; (d) it considers various surface and structural anomalies as stated before; and (e) it shows good correspondence when compared with ground-truth results.

The IBA method was correlated with extracted cores (destructive technique), chain-drag and chloride profiles on sets of concrete bridge decks scanned using GPR along a section of the I-290 and I-294 Tollways in Chicago, IL, USA. The corrosion maps generated during this investigation showed better correspondence with the actual concrete condition [5]. Abouhamad et al., 2017 [29] proposed a systematic framework to develop corrosion maps based on visual-IBA method by Tarussov et al., 2013 [5]. The developed IBA framework was validated by scanning two bridge decks in Quebec and comparing the resultant maps with amplitude-based maps, visual inspection, ground-truth cores and Half-Cell Potential (HCP). Dawood et al., 2020 [30] proposed an integrated approach to develop deterioration maps which delineates air/water voids in subway tunnels based on the GPR-IBA framework [29]. A part of Montreal, Quebec subway tunnel was inspected, and the map generated by the developed model [30] showed high correlation when compared with laser-based thermal map, visual inspection (camera images), and extracted cores. Therefore, the visual-IBA method philosophy was adopted in the proposed model instead of amplitude-based approaches due to better correspondence with the ground truth condition. However, this method has a few limitations of its own [6]. Primarily, since an analyst marks the attenuated areas manually, it is prone to human errors. For example, an analyst could mark a particular area in a B-scan differently each time as moderate or severe based on his/her optic judgment. Secondly, different analysts could have varied visual perspectives of what constitute areas in GPR profiles as good, moderate, or bad; and therefore, it is subjective. Lastly, the method could be time-consuming and tiresome especially in cases of bridge decks with large spans. This paper overcomes the limitations of the visual-IBA method through automated detections of hyperbolas and classification based on mathematical modeling. A condition map generated using the developed model would yield a consistent (same) result for a given set of GPR data devoid of any human errors.

The premier step, detection of hyperbolas in GPR profiles, has been the focus of various researchers in analyzing GPR data. These methods utilize a variety and combination of image processing techniques such as edge detection, thresholding, and template matching. There are several limitations in employing such techniques; for example, edge detection or thresholding involves a pre-defined automatic or manual “threshold” which could lead to noise and non-uniform results across a variety of GPR profiles. Template matching causes missing or false detections of hyperbolas and requires a large database to incorporate the diversity of the shape of hyperbolas. Initial techniques of detecting hyperbola involved direct curve fitting such as Hough transform [31,32], wavelets [33,34], radon transforms [35], and hyperbolic echo characteristics [36]. A smart algorithm identified ill-shaped hyperbolas as it accounted for misshapenness by considering a buffer zone [37]. Some methods have used neural networks [38,39,40,41], while a few have adopted pattern-recognition algorithms [42,43,44]. Peak detection was also achieved by fuzzy clustering [45] and a multi-objective genetic algorithm [46]. Two holistic methods generated deterioration maps through automated rebar picking of amplitude values [47,48]. Some of the latest approaches to distinguishing hyperbolic regions from the background have been developed based on following algorithms: (a) a novel column-connection clustering (C3) algorithm [49], (b) a new drop-flow algorithm which mimics movement of a raindrop [50], (c) a trained convolutional neural network (CNN) [51], (d) a novel Open-Scan Clustering Algorithm (OSCA) which identifies downward-opening signatures [52], and (e) a double cluster-seeking estimate (DCSE) algorithm which improved the OSCA along with faster R-CNN [53]. The majority of the recent methods utilize machine learning (ML) and deep-learning algorithms such as Convolutional Support Vector Machine (CSVM) [54], Multi-Layer Perceptron [55], and enhanced mask R-CNN [56]. However, these approaches have their own limitations especially related to precision and large data set reliance, as discussed earlier. For efficient detections, the proposed model adopted a two-step approach in detecting hyperbolic regions for speed and accuracy. Firstly, a popular ML algorithm called Viola–Jones speedily detected all possible hyperbolic regions in GPR profiles. Secondly, the top layer was identified based on regional comparison, and subsequently eliminated false detections and filled missing gaps based on statistical analysis for automated accurate detections [7].