###### Abstract  
The cost assessment of bridge maintenance is a difficult topic to study, but it is critical for a bridge life cycle cost analysis. The maintenance costs sample database was established in this study according to actual engineering data, and a bridge maintenance cost prediction model was developed using a fully-connected artificial neural network (ANN) and convolutional neural network (CNN), respectively. First, eight main factors affecting maintenance costs were evaluated based on the random forest method, and the evaluation results were verified by an exploratory data analysis. The original data were then screened based on the isolation forest principle, and the recent gross domestic product (GDP) growth rate was used to illustrate the relationship between economic development and bridge maintenance costs. Finally, these two neural networks were used to establish maintenance cost prediction models, respectively. The results from the two models were compared and their prediction accuracies were analyzed. The prediction performance of the CNN model for bridge maintenance costs was found to be better than that of the traditional fully-connected ANN model. The results of this study will enhance the opportunity for bridge managers to balance lifecycle maintenance costs. bridge engineering; bridge maintenance cost; fully-connected ANN; CNN; Bayesian optimization; deep learning; intelligent prediction model +
Footnote †: journal: Medical  
to detection results and preset damage threshold [6]. [PERSON] et al. combined maintenance costs, failure losses, and choice preferences to optimize multiple objectives in the maintenance planning simultaneously, with the goal of finding a set of Pareto optimal solutions [7]. Condition states and reliability indicators were defined as performance indicators, and a multilinear model was used to describe the degradation process of performance indicators under different maintenance scenarios. [PERSON] et al. selected the most cost-effective one by comparing different maintenance scenarios based on a genetic algorithm engine [8]. Many domestic and foreign researchers have carried out extensive research in the cost assessment of bridge maintenance theory, making significant progress. There are cost estimation methods based on numerical analyses, for example, maintenance cost estimations based on the bridge performance degradation model and binary programming algorithm [9], bridge maintenance cost allocation based on the priority index [10], prediction of daily maintenance costs of bridges based on linear regression and time series analyses [11], and the cost assessment of bridge maintenance based on system reliability analyses and multiple regression algorithms [12]. Such evaluation models are established based on the statistical analysis and parametric analysis results of maintenance costs. As for their advantages: they do not require large amounts of samples and the evaluation of maintenance costs could be achieved with fewer computational steps/computational effort. However, these models have some drawbacks. For example, the model prediction accuracy is not high because the parametric analysis does not fully learn the characteristics of the data. At the same time, there are cost prediction studies based on data analyses and the intelligent algorithm. For example, [PERSON] et al. established a cost prediction model through the particle swarm algorithm based on big data analysis. This method can predict the short-term maintenance costs of the structure under the current performance [13]. [PERSON] and [PERSON] allocated the costs of bridge maintenance based on prioritization indices, according to the condition, strategic importance, and vulnerability of the bridge. This method involves simple/a small amount of calculations, which is helpful in short-term maintenance decision-making [14]. [PERSON] et al.