# Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition
## 5 Conclusions  
This study developed an ensemble-learning-based steel bridge deck defect condition prediction model that can help bridge managers make more rational and informed decisions regarding steel bridge deck maintenance. This study explored steel bridge deck defect condition predictions as a dichotomous problem, using the latest data from the NBI database for 2021. The data were first pre-processed, and imbalances in the data were solved using ADASYN. Then, six ensemble learning models (RandomForest, ExtraTree, AdaBoost, GBDT, XGBoost, and LightGBM) were built, the hyperparameters of the models were optimized, and the performance of the six ensemble learning models was compared with determine the optimal prediction model. Next, to demonstrate the superiority of the optimal model performance, the optimal model was compared with traditional machine learning models and previous related studies, and the optimal model was applied to a new dataset. Finally, an explanatory analysis of the optimal model was performed using the SHAP framework. The following conclusions can be drawn from the study:  
Figure 8: Bridge construction year characteristics dependency chart. Figure 7: Bridge substructure condition characteristics dependence diagram. 1. The optimal hyperparameters of the models were determined using the grid search method, and the optimal hyperparameter combinations of six ensemble learning models were obtained. 2. The optimal prediction model is XGBoost, with a model accuracy of 0.9495, an AUC of 0.9026, and an F1-Score of 0.9740. The performance of the model is improved compared with traditional machine learning models, and the comparison with previous studies also demonstrated the superiority of the model's performance. The XGBoost model also performed well on the 2019 and 2020 NBI datasets, showing that the model has good generalization performance, indicating that the model has good generalization performance. Therefore, the XGBoost model developed in this study can be used as an accurate method to predict the condition of steel bridge deck defects. 3. According to the interpretable machine learning framework SHAP, the most important factor affecting the condition of steel bridge deck defects is the condition of the bridge superstructure. Relatively minor factors are the condition of the bridge substructure and the year of bridge construction. The better the condition of the superstructure and substructure of a steel bridge, the less likely the bridge deck is to be defective. The older the steel bridge deck is, the more likely it is that it will have defects. The explanatory analysis of the model is consistent with the actual situation in engineering applications and can justify the developed model. The ensemble learning model developed using this study can help bridge managers to accurately predict the condition of steel bridge deck defects. Additionally, based on the important factors affecting the condition of steel bridge decks obtained in this study, bridge managers should prioritize the maintenance of each steel bridge component when making maintenance decisions and ensure the rational use of funds. Steel bridges have a lot of room for development in China, and as the number of steel bridges in China rises, it is important to establish a database of the steel bridges in China. The future use of the Chinese steel bridge database to build a bridge deck condition prediction model will be more beneficial for Chinese bridge managers in making steel bridge deck maintenance decisions. Contemporary assessments of bridge defects are usually carried out by manual inspection methods, which are highly subjective [41]. To solve this problem, sensor-based detection methods and computer-vision-based detection methods can also be used to obtain bridge assessment data [42]. The models developed in this study can also be used in conjunction with these detection techniques. Conceptualization, Q.L. ; methodology, Z.S. ; software, Z.S. ; validation, Q.L. ; formal analysis, Q.L. ; investigation, Z.S. ; resources, Z.S.