# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 7 Conclusions  
Deterioration models should provide reasonable deterioration estimates to support the maintenance decision-making process and to enable governments to allocate an appropriate budget  
Figure 14: Bridge distribution comparison between inspection and predicted condition ratings without considering classification for (**a**) two-year and (**b**) four-year prediction.  
Figure 15: Bridge distribution comparison between inspection and predicted condition ratings using second level of classification tree for (**a**) two-year and (**b**) four-year prediction.  
for SHM, including repair, rehabilitation, and reconstruction. Numerous local governments have developed deterioration models for the effective monitoring of their bridge assets. To enable easy access to deterioration models, this study presented a toolbox, known as the DMDP, and compared the performance of its embedded algorithms.  
The DMDP was successfully used to develop deterioration models, and its convenient usability as a potential tool for BMS was demonstrated. The weighted LASSO was newly implemented for the consistent selection of explanatory variables with improved performance. Additional investigation was conducted to suggest an appropriate number of explanatory variables, which were used to classify the bridge inventory into groups with common factors. On the basis of the comparison of normalized prediction errors according to the classification tree level, we were able to suggest the optimum number of explanatory variables in terms of the number of bridges in the target state's inventory.  
Deterioration models were then developed for Wyoming bridges using various methods embedded in the DMDP, and the normalized prediction errors were analyzed to compare their performance. Although the deterministic deterioration models were unsuitable for predicting future conditions, they were still effective for observing the distribution of current bridge assets and construction trends. The performance of the stochastic methods varied on the basis of the purpose of the developed deterioration models. The optimization-based and Weibull distribution-based approaches exhibited superior performance for near-future prediction without considering classification. Although the logistic regression and percentage prediction algorithms were both preferable for long-term monitoring and high classification tree levels, the logistic regression algorithm generally exhibited better performance.  
The error comparison also demonstrated the importance of considering the bridge performance improvement to develop deterioration models. The current guide did not request to record the maintenance action such as repair and rehabilitation, and thus the deterioration curve only decreased. Indeed, the historical inspection record included an increase in condition ratings without proper explanation. To improve future predictive models, the condition rating changes followed by such maintenance should be modeled as well.  
Conceptualization and methodology: M.C. and M.M.; software, validation, formal analysis, investigation, data curation, writing--original draft preparation, visualization, supervision, project administration, and funding acquisition: M.C.; resources, writing--review and editing: M.M. All authors have read and agreed to the published version of the manuscript. This research is supported by a grant from R&D Program of Myongji University (2019 year) and the Korea Agency for Infrastructure Technology Advancement (KAIA) funded by the Ministry of Land, Infrastructure, and Transport (no. 20 SCIP-B128492-04). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.