# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 1 Introduction  
The increasing demand for structural health monitoring (SHM) has demonstrated the need for effective civil infrastructure management systems [1; 2; 3]. The objective of such management systems is to inform an appropriate maintenance actions depending on the condition state of the infrastructure in question. Deterioration models have been widely adopted by infrastructure management agencies to evaluate the changes in the condition ratings during the service life of civil infrastructures, such as bridges, sewers, and asphalt pavements [4; 5; 6]. Inspection data monitored from all assets over several decades can be analyzed to extract the features of the deterioration process. Among the various types of civil infrastructure, deterioration models mainly focus on bridges, owing to the importance of public safety and the substantial maintenance expenses involved [3; 7].  
The deterioration of civil infrastructure is defined as a continuous decline in the condition ratings from the initial operational condition due to gradual changes in the performance of the structural elements [8]. The health status of an individual bridge can be expressed by the condition rating, which provides a comprehensive measure on the basis of field inspections and ranges from \"0 (failed)\" to \"9 (excellent)\" [9]. Using the Pontis bridge management system, the element levels of inspection data are converted into the National Bridge Inventory (NBI) condition ratings for the decks, superstructures,substructures, channels and their protection, and culverts [10]. Numerous studies have focused on the decks, superstructures, and substructures in the development of deterioration models [4; 11; 12; 13; 14].  
Many studies have been conducted classify of bridge assets and demonstrated the benefit of using a specific deterioration model type for effective bridge monitoring systems (BMS); however, these tasks require significant computation time to manage bridge inspection data and should interpret new inspection data for model update [15]. For further investigation, a comprehensive toolsuite for the development of deterioration model would aid this process for engineers, managers, and researchers.  
This study presents a method for determining the explanatory variables of condition ratings from inspection data. Although several variables are commonly identified as explanatory variables, the final variable sets may vary from year to year. In the proposed method, nearly stationary sets of explanatory variables, regardless of the inspection year, are identified for bridge assets. To provide a convenient platform for developing deterioration models, we propose a software package referred to as the Deterioration Model Development Package (DMDP). The essence of DMDP is to allow the user to update the inspection data if the previously archived data exist and to provide multiple options for the developing deterioration models. A deterministic model and several stochastic deterioration models were encoded for the deck, superstructure, substructure, and culvert elements. Wyoming bridges were then investigated to validate the proposed method and DMDP by predicting the condition ratings. The ability to conduct performance comparisons in the DMDP allows the optimal number of explanatory variables that minimizes the normalized prediction error for each condition state to be determined.