# Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition
## 1 Introduction
used a hybrid Bayesian optimization approach calibrated with the Markov model to predict bridge deck condition [15]. They concluded that the proposed model outperformed some commonly used Markov models. [PERSON] et al. used statistical methods to determine the factors that lead to defects in bridge decks and developed an ANN-based bridge condition prediction model and a model that could achieve 75% accuracy [16]. [PERSON] et al. used ANN and KNN models to predict bridge deck condition; the optimal model was ANN, which achieved an accuracy of 91.44% [8]. [PERSON] et al. used logistic regression, decision tree, artificial neural network, gradient boosting, and support vector machine models to predict the condition of steel bridge superstructures; the optimal model was a logistic regression model with an accuracy of 73.4% [17]. [PERSON] used convolutional neural networks to predict the condition of the bridge deck, superstructure, and substructure [18]. The results showed that the accuracy of the developed model was more than 85%. Ensemble learning is a machine learning method that combines multiple weak learners to improve the prediction accuracy, and the technique is gaining popularity among researchers in engineering prediction problems [19]. The following ensemble learning models are currently commonly used: RandomForest, ExtraTree, AdaBoost, GBDT, XGBoost, and LightGBM. RandomForest and ExtraTree use bagging for ensemble learning. Bagging first obtains a sample set by random sampling, then trains it to derive a weak learner, and finally uses the integration strategy to determine the final strong learner [20]. AdaBoost, GBDT, XGBoost, and LightGBM use boosting for ensemble learning. Boosting first trains a weak learner, assigns greater importance to the wrongly predicted samples in the training results of this weak learner, then uses this adjusted training set to train the next weak learner. Through continuous iterative learning, b weak learners are obtained, and finally, these b weak learners are combined in a weighted manner [20]. There have been many successful engineering studies using ensemble learning models. [PERSON] et al. used gradient boosting decision trees and random forest models to predict the bond strength of CFRP-steel interfaces and compared them with traditional machine learning models and showed that the models outperformed the traditional models [21]. [PERSON] et al. used ensemble learning models to predict the strength of high-performance concrete crafted from waste materials, and the results showed that random forest and decision tree with bagging performed better [22]. [PERSON] et al. used the XGBoost model to assess the seismic vulnerability of buildings in Kavrepalanchok, Nepal, and the results showed that the developed model had high accuracy [23]. [PERSON] et al. used the random forest model to predict the international roughness index of asphalt pavement [24]. The results showed that the developed model was significantly better than the traditional linear regression model. [PERSON] et al. used the GBDT, XGBoost, and LightGBM models to predict the stability of complex rock pillars, and the results showed that the performance of all three algorithms was better [25]. Research on bridge deck condition predictions mainly focuses on bridges with a concrete superstructure and less on bridges with a steel superstructure. Therefore, it is essential to develop a steel bridge deck defect prediction model using an ensemble learning model with good prediction accuracy. In this study, an ensemble-learning-based bridge deck defect prediction model was developed to help bridge managers make more rational and informed steel bridge deck maintenance decisions. This study utilized the latest data from the NBI database for U.S. states for the year 2021 for modeling.