This paper investigates the use of non-contact laser vibrometers for continuous or periodic bridge monitoring and damage detection through the detection of changes in the bridge's natural frequencies. For this purpose, the vibrations of a full-depth precast-prestressed voided slab bridge and a steel girder bridge were measured under normal traffic using a laser vibrometer. Detailed finite element (FE) models were developed to study the bridge dynamic behavior for various damage scenarios, including longitudinal joint damages in a concrete bridge deck and a full-depth girder fracture in a steel girder bridge. In the concrete bridge deck, longitudinal reflective cracking on the deck surface and leakage had hinted at some level of damage at the joints. The bridge dynamic analysis showed that modal parameters in bridges with precast deck panels are sufficiently sensitive to changes in the state of the panel joints and, therefore, can be used for identifying joint damage. The results clearly confirmed the visible evidence of the damage. For the steel-girder bridge, the results of the dynamic analysis also indicated a noticeable frequency change in the bridge's natural frequencies after a fracture at a level that can be effectively detected with bridge frequency monitoring. Moreover, the proposed approach proved to be more cost-effective and less disruptive than standard monitoring approaches and, at the same time, capable of predicting damages from relatively small vibration amplitudes. This approach can be used to warn of potential damages between planned inspection intervals and to detect damages otherwise hidden from the eye during routine inspections.
The application of accelerated bridge construction (ABC) methods is becoming more widespread owing to their many advantages. In this construction method, prefabricated bridge elements are assembled on-site by establishing in-situ joints to minimize on-site construction time. Despite the improved life-cycle performance and cost benefits of ABC bridges, some concerns exist about the degrading environmental effects on the joints and invisible internal damages. In this study, the long-term performance of an ABC bridge that had been in service for more than 50 years was investigated utilizing machine-learning processes. Observation of reflective cracking on the deck surface and leakage through the joints in this bridge indicated some damage to the bridge longitudinal joints. Damages to the joints are not always visible, nor their extent is known. Therefore, a new damage detection approach is proposed that uses the results of a series of load tests as input in machine-learning techniques with the ultimate aim of detecting the location and severity of joint damages with a high level of certainty. The proposed approach uses the bridge responses obtained from a detailed finite element (FE) model under the assumption of various damage scenarios and predicts the potential damages using the training process of machine-learning algorithms and the actual bridge responses. The results show that the supervised learning algorithm successfully estimated the location and amount of damage in the bridge joints.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.