## 4 Conclusions  
As bridges in most urban areas worldwide are aging, conducting preventive maintenance is essential to ensure their safety and reliability. However, conventional national bridge maintenance standards based on the HI are not suitable for accurately predicting how quickly bridges are aging because the HI is determined by the current condition. Therefore, this study developed a novel HI to quantitatively evaluate bridges by deriving deterioration models from inspection records using the LSTM algorithm. Inspection data from 865 bridge inspection reports were used to train the LSTM model, resulting in 324 deterioration models that reflected 9 regions, 3 bridge components, and 12 deterioration types. These models were used to derive the DHI for each deterioration type of, and their CHI and BHI were derived through weighting, enabling quantitative evaluations. In the case study, the HI curves of the deterioration models were compared with the HIs derived from actual inspection data of six bridges. Accuracies of >93% were confirmed for all bridges, thereby validating the effectiveness of the proposed HI- and LSTM-based bridge evaluation methodology. Additionally, the case study results indicated that HI contributes to bridge-maintenance decisions by providing quantitative data and visual information regarding the level and rate of bridge deterioration. From a bridge maintenance perspective, the main contribution of this study is its development of a deterioration model utilizing data and LSTM, which enables a quantitative approach to bridge assessment that provides a foundation for future performance prediction. Traditional bridge assessment systems typically only examine the current condition and assess the urgency of maintenance needs through a simple grade comparison. In contrast, the methodology presented in this paper offers a means to infer not only the current but also the future state of a bridge, facilitating the creation of more rational and long-term maintenance plans. Although this study specifically focused on RC slab bridges, the proposed methodology can be applied to other types of bridges if sufficient data are available. For prestressed  
Figure 7: HI plots for the case study bridges. concrete girder bridges, additional data are specifically gathered on the girders; however, the format of these data in inspection reports remains consistent with that of other structural components. Consequently, while the distribution of these collected data may vary somewhat from that shown in Figure 2, the methodology proposed in this study can still be directly applied to classify the data and develop deterioration models. Although some defects unique to prestressed concrete girders, such as corrosion of prestressing tendons, could be used to generate deterioration models, such data are generally absent for most bridges. Owing to its considerable reliance on data, the reliability of the data is crucial, and preprocessing steps are required to eliminate human errors. The inspection data collected for this study exhibited significant variance, and data preprocessing was time-consuming. Additionally, as the regional characteristics were incorporated to derive the deterioration models, the application of more detailed environmental factors is expected to further enhance the reliability of these models, which is an important direction for future research. Even though regional characteristics were considered in the data classification categories for deriving the degradation model, it is clear that there can be varying environmental characteristics even within the same region. Due to challenges in data collection, further regional subdivision is currently difficult. Nevertheless, for areas with sufficient data accumulation, it would be possible to derive a more accurate degradation model through finer regional distinctions. Conceptualization, J.K.; methodology, T.H.K. and J.K.; validation, C.-H.J., T.H.K., and J.K.; formal analysis, C.-H.J. ; investigation, K.-S.J., C.-H.J., T.H.K., and J.K.; data curation, T.H.K.