This paper presents an overview of how Non-Destructive Testing (NDT) methods are currently impacting bridge management in the United States (U.S.). It is necessary for researchers and policy makers to have a clear understanding of how NDT methods are being utilized if they are to improve bridge management strategies. After a historical review of the development of a bridge management system, a detailed discussion of currently available NDT methods illustrates the principles, application areas, and limitations of the methods. As state Departments of Transportation (DOTs) are the key bridge management institution in the U.S., the authors conducted a thorough review of current state DOT bridge inspection manuals to identify how NDT methods are incorporated into state bridge management systems to reflect their own local conditions and in what situations they are considered. The authors found that total 30 states have their own bridge inspection manuals, among which eight states addressed the use of NDT methods in their inspection manuals explicitly. Only one state DOT, Indiana, discussed NDT methods and their applications in detail in a separate section.

Damage localization is the process of detecting the location of damage using a structural health monitoring system. However, existing damage localization methods cannot be used for localizing the damage of bridges in real time because of their slow testing speed. Thus, in this study a damage localization approach was developed using a convolutional neural network (CNN). To develop the CNN model for damage localization, simulation data was generated through a numerical model of a reinforced concrete Rahmen bridge with static loading conditions. The proposed CNN-based approach aims to identify 12 single damage locations or no damage. The approach was trained and tested with three different data set generated with three damage severities, and it was possible to estimate the damage location with an accuracy of 87.3% when the damage severity in the bridge is serious. The results showed that the deep learning has the potential to overcome the limitations of existing damage localization techniques.

The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE). The proposed method employs simulation data from nine accelerometers. The accuracies of CAEs for multi-vehicle are 79.5%–85.8% for 100% and 75% damage severities with all error levels and 50% damage severity without error. However, the accuracies for 50% damage severity with 5% and 10% error levels drop to 69.4%–73.3%. The accuracies of CAEs for single-vehicle ranges from 90.1%–95.1% for all damage severities and error levels that are satisfactory. The findings indicate that the CAE approach for multi-vehicle can be effective when the damages are severe, but not when moderate. Meanwhile, if acceleration data can be obtained for single-vehicle, then the CAE approach can provide a highly accurate and robust method of tendon damage detection in PSC bridges in use, even if the measurement errors are significant.