Ground-penetrating radar (GPR) has been extensively studied for condition assessment of concrete bridge decks in North America. Although several methods for analyzing GPR data have been proposed, the commonly accepted method evaluates the condition of concrete bridge decks on the basis of the difference between reflection amplitudes of the top rebar layer. It is assumed in the method that strong reflection indicates sound concrete, whereas the area with high-amplitude attenuation is associated with concrete corrosion. The final result is a contour map of reflection amplitude in decibel scale with the thresholds selected arbitrarily to define the severity of concrete deterioration. Because subjective determination of threshold values may lead to inconsistency in the result obtained, this paper proposes a robust method for resolving that issue. Specifically, after depth correction was performed for top rebar amplitudes, on the basis of K-means clustering technique these amplitude data were grouped into a number of condition categories. Through two case studies in North America, the methodology was implemented and compared with the results provided by other technologies, namely, concrete resistivity, half-cell potential, and laboratory chloride content analysis. The implementation showed that while the proposed method was simple to employ, it still provided reasonable results that were in line with the outputs provided by the other techniques.

Reliable bridge condition assessment is considered the first step, and perhaps one of the most essential elements, of an efficient bridge management system. This consideration stems from the fact that available assessment inputs are constantly interpreted for maintenance decisions and budget allocation to the deserving, intervention-needy bridges within a region's inventory. Thus, carrying out effective bridge assessment is vital to ensure the safety and sustainability of the bridge infrastructure. In practice, the evaluation of concrete bridges is mostly conducted on the basis of visual inspection, associated with considerable uncertainty and subjectivity inherent in human judgments. Additionally, conclusions are often drawn in the absence of a thorough review of critical factors. Therefore, to circumvent the existing limitations, this study proposes a fuzzy hierarchical evidential reasoning approach for detailed condition assessment of concrete bridges under uncertainty. The essence of this framework addresses the treatment and aggregation of detected bridge defect measurements systematically to establish an enhanced platform for reliable bridge assessment. The proposed approach is facilitated by a hierarchy structure that models the several levels of a concrete bridge under assessment: bridge components, structural elements, and, most particularly, the measured defects. A belief structure is employed to grasp probabilistic uncertainty (ignorance) in the assessment, while fuzzy uncertainty (subjectivity) is processed through a set of collectively exhaustive fuzzy linguistic variables. Eventually, the Dempster–Shafer theory is used within the suggested framework for accumulating supporting pieces of evidence toward a comprehensive and educated overall condition assessment.

An efficient bridge management strategy emanates from a reliable condition assessment of the existing bridge structures. This includes reporting of defects/distress indicators that may be possibly detected on the various bridge elements. Field inspection of bridge elements is mostly conducted on the basis of Visual Inspection (VI), which is inevitably associated with uncertainties and subjectivities inherent in the human being's judgments. In order to remediate those issues in the assessment process, this study employs a fuzzy logic based methodology as means to model inaccuracies in bridge inspectors' measurements/observations. A systematic procedure is proposed to develop a detailed concrete bridge condition assessment model by comprehensive aggregation of possible defects. Using fuzzy membership-based defect rating, in combination with a structural importance relative weighting approach, the proposed model will be able to translate uncertain measurements of defects into a fuzzy bridge condition rating. The output of this research is expected to be a fuzzy based framework for a detailed bridge condition assessment that incorporates a comprehensive weighted set of possible bridge defects and is compliant with current bridge inspection practices.