Title: Bridge type classification: supervised learning on a modified NBI dataset,
Abstract: A key phase in the bridge design process is the selection of the structural
system. Due to budget and time constraints, engineers typically rely on
engineering judgment and prior experience when selecting a structural system,
often considering a limited range of design alternatives. The objective of this
study was to explore the suitability of supervised machine learning as a
preliminary design aid that provides guidance to engineers with regards to the
statistically optimal bridge type to choose, ultimately improving the
likelihood of optimized design, design standardization, and reduced maintenance
costs. In order to devise this supervised learning system, data for over
600,000 bridges from the National Bridge Inventory database were analyzed. Key
attributes for determining the bridge structure type were identified through
three feature selection techniques. Potentially useful attributes like seismic
intensity and historic data on the cost of materials (steel and concrete) were
then added from the US Geological Survey (USGS) database and Engineering News
Record. Decision tree, Bayes network and Support Vector Machines were used for
predicting the bridge design type. Due to state-to-state variations in material
availability, material costs, and design codes, supervised learning models
based on the complete data set did not yield favorable results. Supervised
learning models were then trained and tested using 10-fold cross validation on
data for each state. Inclusion of seismic data improved the model performance
noticeably. The data was then resampled to reduce the bias of the models
towards more common design types, and the supervised learning models thus
constructed showed further improvements in performance. The average recall and
precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0%
and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.