Structural information integration for predicting damages in bridges

Industrial Information Integration Engineering interacts with each of the twelve engineering disciplines defined by the U.S. National Academy of Engineering. Civil Engineering is one of the twelve engineering sections. Integration of information can be a very valuable method for civil engineering. In the management of the highway bridges, bridge design and operation parameters and inspection results are recorded according to national and state guides and the data is coded and archived in what is known as the National Bridge Inventory (NBI) database. The integration of the information in the NBI database can be a very valuable method for managing of bridges. The major challenges of the information in such a database are unbalance, complexity, subjectivity, and incompleteness, which make developing a damage prediction model from such information difficult. Particularly, the prediction and management of bridge abutment distress is difficult since their generation mechanism is not clear. The purpose of this study was to integrate the information of the inspections database and establish models to predict abutment distress. Input variables for the networks were selected from knowledge-based evaluations, statistical analyses, and trial and error. Four neural network models were developed and evaluated: the multilayer perceptron and support vector machine models predicted abutment condition better than a supervised self-organizing map and radial basis function networks.