In North America, common practices in bridge condition assessment include visual inspection and nondestructive evaluation (NDE) techniques, and results are reported as condition ratings of the bridge components. Assigning a specific condition rating to the component is a difficult task, especially when the threshold values defining the borderlines between the different ratings are not specified. These thresholds are subjectively assigned based on the judgment and experience of the inspector or expert, and can influence decisions on maintenance, repair, and replacement (MRR) of bridges and impact their safety and serviceability. Quality inspection data and accurate condition assessment and rating are the basis for determining appropriate MRR decisions. Thus, in this paper, a novel quality function deployment (QFD)-based approach for assessing bridges is proposed to develop an integrated condition rating based on data collected from visual inspection and ground penetrating radar (GPR) technology, while identifying clear thresholds between the different ratings. The $k$ -means clustering technique, used to define the rating thresholds, is one of the unsupervised learning algorithms that solves the subjective determination of the threshold values problem. This work used four case studies on bridges in the Province of Quebec. The integrated condition model produced ratings of 0.48, 0.49, 0.37, and 0.15 for the four case studies. The developed rating model represented by an integrated condition index was validated with an average validity percentage greater than 81%. The proposed method is expected to advance the state of the art for bridge condition assessment and rating by providing an objective means for making proper MRR decisions.

Purpose: Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks. Design/methodology/approach: Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion. Findings: The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas. Originality/value: The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.

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