Accurately determining the current state of a suspension bridge’s main cables is a critical component to reliably assessing the safety of the bridge. The primary cause for the deterioration of cable strength with time is universally recognized to be the corrosion of high strength steel wires, which together comprise the main cable. Hidden from view by the cable wrapping, this corrosion often goes undetected for years and is typically only discovered during costly and intrusive inspections. Furthermore, current inspection methods provide an incomplete picture of the variation in wire condition across the cable cross section. As a result, cable strength estimation techniques that rely solely on inspection data introduce a considerable degree of uncertainty. Finally, a method has not been developed for estimating the continuing decline in cable strength due to ongoing corrosion. A recent direction in research attempts to address the shortcomings of current inspection methodologies and the intent of this thesis is to further build upon these findings.
In these recent studies, environmental conditions inside main cables are monitored to obtain information regarding the corrosive nature of the cable’s internal environment. The first goal of this thesis is to further this research direction by introducing a corrosion rate model for bridge wires that relates the monitored environmental parameters within a cable to the corrosion rate of bridge wires. Initially, temperature, relative humidity, pH, and Cl- concentration have been identified as the most relevant variables for predicting the corrosion rate of a bridge wire. By applying machine learning methods to a corrosion dataset in conjunction with these monitored environmental inputs, a long term corrosion rate model for bridge wires has been developed that is capable of capturing variability associated with these environmental parameters.
This long term corrosion rate model is then applied to establish a methodology that will allow bridge owners and engineers to estimate the remaining strength of a main cable at any point in time. This is accomplished through the use of continually monitored environmental parameters which are input into the corrosion rate model. Incorporating the long term corrosion rate model developed in this thesis with current strength estimation techniques, the methodology presented in this thesis for the estimation of the remaining strength of suspension bridge cables may be readily adapted to other bridges and can be used to complement the current best practices for bridge inspection.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8RB7H05 |
| Date | January 2017 |
| Creators | Karanci, Efe |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
Page generated in 0.002 seconds