Defects are essential indicators to gauge the structural integrity and safety of reinforced concrete bridges. Non-destructive inspection has been pervasively explored over the last three decades to localize and characterize surface and subsurface anomalies in reinforced concrete bridges. In addition, different fuzzy set theory-based, computer vision and artificial intelligence algorithms were leveraged to analyze the data garnered from non-destructive evaluation techniques. In light of the foregoing, this research paper presents a mixed review method that encompasses both bibliometric and systematic analyses of the state-of-the-art work pertinent to the assessment of reinforced concrete bridge defects using non-destructive techniques (CBD_NDT). In this context, this study reviews the literature of journal articles and book chapters indexed in Scopus and Web of Science databases from 1991 to the end of September 2022. To this end, 505 core peer-reviewed journal articles and book chapters are compiled for evaluation after conducting forward and backward snowballing alongside removing irrelevant papers. This research study then exploits both VOSVIEWER and Bibiometrix R Package for the purpose of network visualization and scientometric mapping of the appended research studies. Thereafter, this paper carries out a multifaceted systematic review analysis of the identified literature covering tackled bridge defects, used non-destructive techniques, data processing methods, public datasets, key findings and future research directions. The present study is expected to assist practitioners and policymakers to conceive and synthesize existing research and development bodies, and future trends in the domain of the assessment of bridge defects using non-destructive techniques. It can also aid in raising awareness of the importance of defect management in bridge maintenance systems.

Concrete bridges are the most prevalent bridge type worldwide, forming critical components of transportation infrastructure. These bridges are subjected to continuous deterioration due to environmental exposure and operational stresses, necessitating ongoing condition monitoring. Despite extensive research on condition rating and deterioration modeling of concrete bridges, a comprehensive and comparative understanding of these processes remains underexplored. This paper addresses this gap by conducting a critical scientometric and systematic review of condition rating and deterioration modeling approaches for concrete bridges to highlight their strengths and limitations. Accordingly, most of the condition rating methods were found to have a heavy reliance on qualitative visual inspections (VI) and inherent subjective assumptions. Techniques such as fuzzy logic and non-destructive evaluation (NDE) methods were identified as promising tools to mitigate uncertainties and enhance accuracy. Moreover, the performance of most deterioration models was dependent on the quality of the historical condition data. The advancement of hybrid deterioration models, such as integrating artificial intelligence (AI) with stochastic and physics-based approaches, has proven to be a powerful strategy, combining the strengths of each method to deliver enhanced condition predictions. Finally, this study offers key insights and future research directions to assist researchers and policymakers in advancing sustainable concrete bridge management practices.

Ground-penetrating radar (GPR) is a prominent non-destructive testing (NDT) method for corrosivity evaluation in concrete structures. Most GPR interpretation methods rely solely on the absolute values of rebar reflection intensity, making them vulnerable to misinterpretation of the effects of complex factors. This study introduces a more comprehensive GPR data interpretation method, encompassing analysis in time and time-frequency domains. The developed method constitutes efficient GPR data collection and pre-processing, deep learning rebar recognition, and frequency domain analysis using the Short-Time Fourier Transform (STFT). The center frequency of rebar responses was normalized and depth-corrected to standardize the analysis method. The GPR condition mapping thresholds were optimized and validated using ground truth conditions from hammer tapping and reinforcement exposure of reinforced concrete walls. The method demonstrated superior performance compared to the traditional amplitude-based approach in detecting and quantifying the extent of corrosion-induced deterioration, with an average accuracy of 0.80 for active corrosion and 0.84 for active-corrosion with corrosion-induced delamination.

Quantifying the impact of rutting on traffic safety contributes to the development of objective models for evaluating pavement performance. However, the existing literature shows significant discrepancies in the impact of rutting on traffic safety. To this end, this study analyzed about 40 studies to comprehensively understand the impact of rutting on traffic safety in field observations and simulation studies. This study analyzed the influence of ten factors that may impact the relationship between rutting and traffic safety, such as weather, speed, and road type. It also established rutting limits and developed machine learning-based prediction models for accident rates caused by rutting under varying conditions. These findings reveal distinct trends, with simulation studies generally suggesting a higher impact of rutting on safety compared to field observations. This discrepancy is attributed to the limitations of simulation models in capturing human factors, such as drivers’ ability to anticipate and adjust their behavior to mitigate risks. These results provide valuable insights for highway agencies and policymakers to develop more accurate rut limits and maintenance guidelines. These results also underscore the importance of considering rutting in the development of autonomous vehicles to ensure effective handling of rutting under varying conditions. This study highlights the need for more comprehensive field studies using larger datasets that account for various environmental and traffic factors. Additionally, integrating real-world driver behavior into simulation models could improve their accuracy.