# Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition
## 3 Prediction of Steel Bridge Deck Defect Condition Based on Ensemble Learning
### Data Preprocessing  
In this study, the latest NBI database data from 2021 were first used to obtain data related to bridges with a steel superstructure material via screening. Then, the data were preprocessed, and the data for the bridges with category N bridge deck conditions (N indicates not applicable) were removed from the dataset followed by eliminating data outliers and missing values to obtain a final dataset containing 139,063 bridges. The condition ratings for the bridge decks in the NBI database are shown in Table 1[36]. ### Data Preprocessing  
In this study, the latest NBI database data from 2021 were first used to obtain data related to bridges with a steel superstructure material via screening. Then, the data were preprocessed, and the data for the bridges with category N bridge deck conditions (N indicates not applicable) were removed from the dataset followed by eliminating data outliers and missing values to obtain a final dataset containing 139,063 bridges. The condition ratings for the bridge decks in the NBI database are shown in Table 1[36]. \begin{table}
\begin{tabular}{c l c} \hline \hline
**Rating** & **Description** & **Target** \\ \hline N & Not applicable. & \\ \hline
9 & Excellent condition. & \\
8 & Very good condition—no problems noted. & \\
7 & Good condition—some minor problems. & Not Defect \\
6 & Satisfactory condition—structural elements show some minor deterioration. & \\
5 & Fair condition—all primary structural elements are sound but may have minor section loss, & \\  & cracking, spalling, or scour. & \\ \hline
4 & Poor condition—advanced section loss, deterioration, spalling, or scour. & \\  & Serious condition—section loss, deterioration, spalling, or scour have seriously affected & \\
3 & primary structural components. Local failures are possible. & \\  & Fatigue cracks in steel or shear cracks in concrete may be present. & \\  & Critical condition—advanced deterioration of primary structural elements. Fatigue cracks in & \\
2 & steel or shear cracks in concrete may be present, or scour may have removed & \\  & substructure support. Unless closely monitored, it may be necessary to close the bridge until & \\  & corrective action is taken. & \\  & “Immunient” failure condition—major deterioration or section loss present in critical structural & \\
1 & components or obvious vertical or horizontal movement affecting structure stability. The bridge & \\  & is closed to traffic. & \\
0 & Failed condition—out of service—beyond corrective action. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Condition ratings of the bridge decks. Figure 1: Predictive model flow chart. Based on the deck condition ratings, the steel bridge deck defect condition prediction problem can be viewed as a dichotomous problem (defective and non-defective), with the presence or absence of defects on the steel deck as the target variable. Based on the Federal Highway Administration's definition of structural defects [37], ratings less than or equal to four were classified as defective conditions, and ratings above four were classified as non-defective conditions. Subsequently, statistical analysis of the target variables in the dataset revealed that there was a sample imbalance in the target variables, in which the proportion of defective samples accounted for 6%. Therefore, this study used ADASYN to process the variable target samples, and the ratio of defective and non-defective samples after processing was 1:1. The categorical feature variables in the dataset are usually not continuous numbers, and most machine learning algorithms cannot deal with categorical feature variables directly; thus, this paper used a unique thermal coding approach to deal with them. For the numerical feature variables in the dataset, because they possess different magnitudes and magnitudes, data normalization is used to deal with them. The numerical feature variables are transformed into dimensionless, order-of-magnitude difference-normalized values through normalization in order to process the data more efficiently [38].