###### Abstract  
Bridge decks deteriorate faster compared to other bridge components, primarily influenced by traffic volume, while previous studies have examined the effect of bridge-wearing surfaces on deterioration, further understanding of the relationship between bridge performance and maintenance is needed for policy-making and planning purposes. In this study, we focus on nine influential variables to unravel the intricate connections among performance, deterioration, and maintenance of six distinct bridge-wearing surfaces: Monolithic Concrete, Gravel, Wood or Timber, Bituminous, Low Surface Concrete, and Other. Statistical analyses were employed to determine associations between variables and concepts, exploring similarities and differences across various wearing surface types. In particular, machine learning algorithms were utilized to model the maintenance considering the performance and deterioration of the six diverse wearing surfaces. This approach allowed for an examination of interactions between those variables and concepts. We further applied a well-performing prediction model (which achieved an accuracy of 0.86 and an AUC score of approximately 0.83) to obtain interpretable insights regarding bridge deck surfaces. Analysis with interpretable methods such as SHAP (Shapley additive explanation) and PDP (partial dependency plot) revealed that deterioration, deck age, deck area, and overall performance were the most influential variables among average daily traffic, average daily truck traffic, and the number of spans significantly influenced the maintenance of bridge deck condition with different wearing surfaces. Notably, a strong relationship between performance and maintenance was observed in specific wearing surface types, such as Monolithic Concrete and Wood or Timber, while Other surface types exhibited different patterns. These findings highlight the need for tailored approaches when assessing bridge health, considering the distinct characteristics of different bridge deck types. bridge deck performance; deterioration and intervention; machine learning; SHAP feature importance; data and data science; artificial intelligence; pattern recognition; infrastructure management and system preservation +
Footnote †: journal:future interventions for preserving a bridge's overall condition. However, these methods generally adhere to strategies that either focus on comprehending the repercussions of bridge failure, relying on the existing rate of bridge condition deterioration [5], or consider the known time elapsed between two consecutive condition assessments of the bridge [6, 7]. Overall, a comprehensive understanding of the determinants for selecting the appropriate maintenance type and evaluating the performance of bridge components across time intervals still needs to be addressed. This research analyzes the influential variables affecting the overall health of the bridge deck, focusing on three key metrics: performance, deterioration, and maintenance. We define the three concepts of bridge health as the following:  
* **Performance:** Performance of a bridge component is measured by comparing its condition rating to that of other bridges relative to age. This metric serves as an indicator of the bridge's service life. * **Deterioration:** Deterioration refers to the degradation of a bridge component over time. * **Maintenance:** Maintenance encompasses interventions or actions aimed at significantly improving the condition rating of a bridge component. It is important to note that we assume each of these bridge health metrics is independent, and focusing on one metric does not necessarily provide information about the others. For instance, while maintenance may improve condition ratings, it may not necessarily enhance performance compared to other bridges of the same age. Similarly, high levels of deterioration may lead to frequent maintenance, but maintenance is not always mandated with respect to deterioration.