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.

Investigation of Electromagnetic Gauges for Determination of In-Place Density of HMA Pavements, TR-576, 2009

This study evaluated the use of electromagnetic gauges to determine the adjusted densities of HMA pavements. Field measurements were taken with two electromagnetic gauges, the Pavement Quality Indicator (PQI) 301 and the Pavetracker Plus 2701B. Seven projects were included in the study with 3 to 5 consecutive paving days. For each day/lot 20 randomly selected locations were tested along with seven core locations. The analysis of PaveTracker and PQI density consisted of determining which factors are statistically significant, and core density residuals and a regression analysis of core as a function of PaveTracker and PQI readings. The following key conclusions can be stated: 1. Core density, traffic and binder content were all found to be significant for both electromagnetic gauges studied, 2. Core density residuals are normally distributed and centered at zero for both electromagnetic gauges, 3. For PaveTracker readings, statistically one third of the lots do not have an intercept that is zero and two thirds of the lots do not rule out a scaler correction factor of zero, 4. For PQI readings, statistically the 95% confidence interval rules out the intercept being zero for all seven projects and six of the seven projects do not rule out the scaler correction factor being zero, 5. The PQI 301 gauge should not be used for quality control or quality assurance, and 6. The Pavetracker 2701B gauge can be used for quality control but not quality assurance. This study has found that with the limited sample size, the adjusted density equations for both electromagnetic gauges were determined to be inadequate. The PaveTracker Plus 2701B was determined to be better than the PQI 301. The PaveTracker 2701B could still be applicable for quality assurance if the number of core locations per day is reduced and supplemented with additional PaveTracker 2701B readings. Further research should be done to determine the minimum number of core locations to calibrate the gauges each day/lot and the number of additional PaveTracker 2701B readings required.