Abstract: Methods and systems for bridge damage detection using, for example, one or more strain range methods are provided. One exemplary embodiment provides a computer-implemented methods and systems for determining bridge damage from strain sensor data, for example, by collecting a batch of strain data from one or more sensor pairs. From the batch of strain data one or more sets of strain data may be extracted comprising a quasi-static response of the bridge under ambient traffic loads. A relationship may be established between the one or more sets of strain data extracted from the one or more sensor pairs by orthogonal regression. Bridge damage may be detected by generally isolating a damage indicator between the one or more sensor pairs by monitoring changes in a statistical Fshm value over time.

Abstract: Methods and systems for bridge damage detection using, for example, one or more strain range methods are provided. One exemplary embodiment provides a computer-implemented methods and systems for determining bridge damage from strain sensor data, for example, by collecting a batch of strain data from one or more sensor pairs. From the batch of strain data one or more sets of strain data may be extracted comprising a quasi-static response of the bridge under ambient traffic loads. A relationship may be established between the one or more sets of strain data extracted from the one or more sensor pairs by orthogonal regression. Bridge damage may be detected by generally isolating a damage indicator between the one or more sensor pairs by monitoring changes in a statistical Fshm value over time.

Abstract: In-situ methods and systems for determining bridge load ratings under ambient traffic are provided. These may include, for example, by installing one or more strain gauges on one or more bridge girders a batch of strain readings may be acquired from the one or more strain gauges. From the batch of strain readings, one or more strain time histories may be randomly sampled based, for example, on a girder peak strain. One or more vehicles may be randomly selected based on the one or more stored vehicle parameters by accessing a database with one or more stored vehicles and stored vehicle parameters. A bridge load rating model may be calibrated based on the one or more randomly sampled strain time histories and the randomly selected one or more vehicles for acquiring, in one embodiment, a bridge load rating distribution.