An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

Development of Bridge Load Testing Process for Load Evaluation, TR-445, 2003

Recent reports indicate that of the over 25,000 bridges in Iowa, slightly over 7,000 (29%) are either structurally deficient or functionally obsolete. While many of these bridges may be strengthened or rehabilitated, some simply need to be replaced. Before implementing one of these options, one should consider performing a diagnostic load test on the structure to more accurately assess its load carrying capacity. Frequently, diagnostic load tests reveal strength and serviceability characteristics that exceed the predicted codified parameters. Usually, codified parameters are very conservative in predicting lateral load distribution characteristics and the influence of other structural attributes. As a result, the predicted rating factors are typically conservative. In cases where theoretical calculations show a structural deficiency, it may be very beneficial to apply a "tool" that utilizes a more accurate theoretical model which incorporates field-test data. At a minimum, this approach results in more accurate load ratings and many times results in increased rating factors. Bridge Diagnostics, Inc. (BDI) developed hardware and software that are specially designed for performing bridge ratings based on data obtained from physical testing. To evaluate the BDI system, the research team performed diagnostic load tests on seven "typical" bridge structures: three steel-girder bridges with concrete decks, two concrete slab bridges, and two steel-girder bridges with timber decks. In addition, a steel-girder bridge with a concrete deck previously tested and modeled by BDI was investigated for model verification purposes. The tests were performed by attaching strain transducers on the bridges at critical locations to measure strains resulting from truck loading positioned at various locations on the bridge. The field test results were used to develop and validate analytical rating models. Based on the experimental and analytical results, it was determined that bridge tests could be conducted relatively easy, that accurate models could be generated with the BDI software, and that the load ratings, in general, were greater than the ratings, obtained using the codified LFD Method (according to AASHTO Standard Specifications for Highway Bridges).