Abstract
Nationwide statistics on numbers of structurally-deficient bridges coupled with ongoing corrosion processes caused by deicing agents in many climates lead to a demand for better analysis techniques for corrosion-damaged reinforced concrete structural members. Modern computational methods for modeling this behavior such as finite element analysis (FEA) are an ideal tool to fulfill this need. However, these analyses require many inputs that, due to the long timescales over which corrosion occurs, are often prohibitive to obtain through physical testing. In this research, a novel statistical approach using a neural network (NN) model has been constructed to approximate these inputs based on data in the literature from 107 concrete members. Then, outputs from the NN have been introduced into FEA material behavior models for the analysis of corrosion-damaged concrete beams. Load-deflection behavior resulting from such FEA shows good correlation when compared with available experimental data, confirming the accuracy of the NN. Thus, the NN is suggested as a means for obtaining inputs for FEA of corrosion-damaged concrete members.