Iowa Department of Transportation
Brent Pharesbphares@iastate.edu email >
Bridge Research Engineer, BEC
About the research
The objectives of this project were to correlate international roughness index (IRI) data (which are widely collected and directly related to bridge deck roughness) to impact factors and develop a process for determining the impact factor to use for all bridges in Iowa.To achieve the project objectives, a sample of 20 bridges was selected for bridge monitoring to collect dynamic strain data.
To estimate the static strain data, the locally weighted scatterplot smoothing (LOWESS) function was used to smooth the dynamic strain time history. The dynamic impact factor (DIF) value was then calculated using maximum dynamic and static strain data. IRI data were extracted from PathWeb, a web-based application provided by the Iowa Department of Transportation (DOT) for all bridges considered in the field test program. Once the bridge was identified in PathWeb, the IRI data from four locations near each bridge deck approach were extracted and used to study the relationship between the IRI and DIF. Based on the results from this research, these were the key findings:
- The DIF value decreases as the bridge skew angle increases. Based on linear regression, the DIF value decreases about 0.037 to 0.043 per 10-degree increment of bridge skew.
- The DIF value decreases as the bridge deck condition index increases, meaning that the dynamic response is lower when the bridge deck condition is better.
- For bridges with zero skew, the DIF value increased by 0.006 per 100 in/mile increment of the IRI value.
According to the research findings, an equation was developed for the prediction of DIF on existing bridges with consideration of the bridge skew and the maximum IRI value near the bridge deck approach. Although the proposed equation was validated uing data from 13 bridges, the researchers recommend using the equation with the limitation that the actual bridge dynamic response could deviate ±10% from the equation predicted value.