There are over 250,000 reinforced concrete bridges in the U.S. many of which do not have a load rating on record nor the plans required to perform the calculations. The U.S. Army owns and maintains hundreds of these bridges throughout the U.S. This dissertation describes the development of multiple regression models to estimate the load rating of reinforced concrete bridges. An exploratory data analysis of the 2017 NBI data was performed for the selection of a representative data sample. The data was found to have multiple errors and required significant processing in order to extract a reliable sample for modeling. After processing, a data sample of 31,112 bridges remained, providing sufficient sample for model training and testing. A six-variable model (Model A) was determined to provide the best performance while maintaining a desired low level of complexity. The model was tested by comparing the percentage of cases that fell within its 95% prediction interval, which resulted in 94.9% of the real values falling within the prediction interval. Given the concerns that arose of the quality of the 2017 NBI data during its exploration, as built-drawings from 50 slab bridges throughout the U.S. were collected. With these drawings a new data sample was generated by calculating the load rating of each bridge. Availability of the as-built drawings provided the opportunity to investigate other variables not available in the 2017 NBI, most notably the slab thickness. This data sample was significantly smaller than the previous one, therefore a repeated 10old cross-validation approach was taken to evaluate model performance. It was determined that a five-variable model (Model B) provided the best trade-off between complexity and performance. Model B performed significantly better than Model A due to the inclusion of the slab thickness variable. The models presented in this dissertation provide a valuable tool for reinforced concrete bridge owners tasked with the assigning a load rating when no structural plans are available helping.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-2931 |
| Date | 01 May 2020 |
| Creators | Ruiz, Edgardo |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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