# Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition
## 1 Introduction  
With the advantages of a large span, a short construction period, and easy transportation, steel bridges have been widely used in bridge engineering. Among the various components of steel bridges, bridge decks are more prone to defects than other components during service because they are directly subjected to traffic loads [1]. Under the influence of various factors such as vehicle load and climatic environment, steel bridge decks will experience different degrees of defects. Cracking, rutting, and other types of fatigue damage, such as fatigue cracks, are the most typical steel bridge deck defects [2, 3]. If fatigue cracks are not treated in time, they will lead to water and other chemical reagents and steel bridge panel contact [4]. The steel bridge panel will produce corrosion phenomena, thus affecting the performance of the bridge deck. Severe cracking will also increase the roughness of the bridge deck, affecting driving comfort and safety [5]. Compared with the rapid progress in construction in China, the management and maintenance technology for such projects are relatively underdeveloped. After defects appear on the bridge deck, domestic bridge management is prone to incorrectly predicting the condition of defects and the future development trends, making it difficult to carry out effective maintenance measures. Therefore, it is essential to carry out research related to identifying defects in steel bridge decks. In recent years, large-span steel bridges have been widely used in China, but the proportion of the overall number of bridges is still small, and most of them are in the early stages of service. Usually, large amounts of data are needed to analyze the defective condition of in-service steel bridge decks. It is difficult to collect data related to steel bridge decks in China. The United States has more developed transportation infrastructure. National Bridge Information Base (NBI) data show that the number of steel bridges in the United States will account for more than 30% of the bridges in the country by 2021 [6]. The NBI is a database compiled by the Federal Highway Administration that contains data related tothe structure type, material type, and condition rating of bridges in each state of the United States [7]. All of the data are recorded in a uniform standard. Therefore, using the NBI database can provide good data support for predicting and analyzing steel bridge deck defect conditions. Developing a data-driven prediction model can help Chinese bridge managers make better deck maintenance decisions. To build a bridge deck defect condition prediction model, it is necessary to establish the interrelationships between deck conditions and various parameters (such as superstructure condition, substructure condition, average daily traffic, structure length, bridge roadway width, and bridge age) of the bridge, but there are significant challenges to making such a model due to the highly nonlinear relationship between the parameters [8; 9; 10]. However, with the rapid development of artificial intelligence in the last decade, many researchers have started to use machine learning models to solve related problems in the field of civil engineering [11]. Regarding the issue of bridge deck condition prediction, [PERSON] et al. used stochastic parametric binary logistic regression to predict the effects of environmental and structural parameters on the bridge deck condition [12]. They showed that the developed stochastic parametric model outperformed the traditional binary logistic model. [PERSON] et al. used the multivariate three-stage least squares model to predict the condition of the bridge deck, superstructure, and substructure conditions [13]. They compared it with the ordinary least squares model, and the results showed that the multivariate three-stage least squares model had higher accuracy. [PERSON] et al. used a stochastic Markov chain model to predict the condition of wooden bridges [14]. The results showed that the developed model had high accuracy. [PERSON] et al.