Monitoring of Dynamic Bridge Behaviour Using Digital Image Correlation

Bridges are a critical part of North America’s transportation network that need to be assessed frequently to inform bridge management decision making. Visual inspections are usually implemented for this purpose, during which inspectors must observe and report any excess displacements or vibrations. Unfortunately, these visual inspections are subjective and often highly variable and so a monitoring technology that can provide quantitative measurements to supplement inspections is needed. Digital Image Correlation (DIC) is a novel monitoring technology that uses digital images to measure displacement fields without any contact with the bridge. In this research, DIC and accelerometers were used to investigate the dynamic response of a railway bridge reported to experience large lateral displacements. Displacements were estimated using accelerometer measurements and were compared to DIC measurements. It was shown that accelerometers can provide reasonable estimates of displacement for zero-mean lateral displacements. By comparing measurements in the girder and in the piers, it was shown that for the bridge monitored, the large lateral displacements originated in the steel casting bearings positioned above the piers, and not in the piers themselves. The use of DIC for evaluating the effectiveness of rehabilitation of the LaSalle Causeway lift bridge in Kingston, Ontario was also investigated. Vertical displacements were measured at midspan and at the lifting end of the bridge during a static test and under dynamic live loading. The bridge displacements were well within the operating limits, however a gap at the lifting end of the bridge was identified. Rehabilitation of the bridge was conducted and by comparing measurements before and after rehabilitation, it was shown that the gap was successfully closed. Finally, DIC was used to monitor the midspan vertical and lateral displacements in a monitoring campaign of five steel rail bridges. DIC was also used to evaluate the effectiveness of structural rehabilitation of the lateral bracing of a bridge. Simple finite element models are developed using DIC measurements of displacement. Several lessons learned throughout this monitoring campaign are discussed in the hope of aiding future researchers.

SENSORIMOTOR WORKING MEMORY CAPACITY LIMITS

Two novel studies examining the capacity and characteristics of working memory for object weights, experienced through lifting, were completed. Both studies employed visually identical objects of varying weight and focused on memories linking object locations and weights. Whereas numerous studies have examined the capacity of visual working memory, the capacity of sensorimotor memory involved in motor control and object manipulation has not yet been explored. In addition to assessing working memory for object weights using an explicit perceptual test, we also assessed memory for weight using an implicit measure based on motor performance. The vertical lifting or LF and the horizontal GF applied during lifts, measured from force sensors embedded in the object handles, were used to assess participants’ ability to predict object weights. In Experiment 1, participants were presented with sets of 3, 4, 5, 7 or 9 objects. They lifted each object in the set and then repeated this procedure 10 times with the objects lifted either in a fixed or random order. Sensorimotor memory was examined by assessing, as a function of object set size, how lifting forces changed across successive lifts of a given object. The results indicated that force scaling for weight improved across the repetitions of lifts, and was better for smaller set sizes when compared to the larger set sizes, with the latter effect being clearest when objects were lifting in a random order. However, in general the observed force scaling was poorly scaled. In Experiment 2, working memory was examined in two ways: by determining participants’ ability to detect a change in the weight of one of 3 to 6 objects lifted twice, and by simultaneously measuring the fingertip forces applied when lifting the objects. The results showed that, even when presented with 6 objects, participants were extremely accurate in explicitly detecting which object changed weight. In addition, force scaling for object weight, which was generally quite weak, was similar across set sizes. Thus, a capacity limit less than 6 was not found for either the explicit or implicit measures collected.