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.

Feasibility Study for Detection and Quantification of Corrosion in Bridge Barrier Rails, RB11-012, 2013

"Technical challenges exist with infrastructure that can be addressed by nondestructive evaluation (NDE) methods, such as detecting corrosion damage to reinforcing steel that anchor concrete bridge railings to bridge road decks. Moisture and chloride ions reach the anchors along the cold joint between the rails and deck, causing corrosion that weakens the anchors and ultimately the barriers. The Center for Nondestructive Evaluation at Iowa State University has experience in development of measurement techniques and new sensors using a variety of interrogating energies. This research evaluated feasibility of three technologies — x-ray radiation, ground-penetrating radar (GPR), and magnetic flux leakage (MFL) — for detection and quantification of corrosion of embedded reinforcing steel. Controlled samples containing pristine reinforcing steel with and without epoxy and reinforcing steel with 25 percent and 50 percent section reduction were embedded in concrete at 2.5 in. deep for laboratory evaluation. Two of the techniques, GPR and MFL, were used in a limited field test on the Iowa Highway 210 Bridge over Interstate 35 in Story County. The methods provide useful and complementary information. GPR provides a rapid approach to identify reinforcing steel that has anomalous responses. MFL provides similar detection responses but could be optimized to provide more quantitative correlation to actual condition. Full implementation could use either GPR or MFL methods to identify areas of concern, followed by radiography to give a visual image of the actual condition, providing the final guidance for maintenance actions." The full 103 page report and the 2 page Tech Transfer Summary are included in this link.

On the spot damage detection methodology for highway bridges (tech transfer summary)

The objective of this work was to develop a low-cost portable damage detection tool to assess and predict damage areas in highway bridges. The proposed tool was based on standard vibration-based damage identification (VBDI) techniques but was extended to a new approach based on operational traffic load. The methodology was tested using numerical simulations, laboratory experiments, and field testing.