## 1 Introduction
Most of the aforementioned data-driven deterioration-prediction models employ discrete data ranges because bridge condition assessments worldwide are recorded in the form of categorical results, as listed in Table 1[1; 19; 20; 21; 22; 23; 24; 25]. This condition-rating system is widely used because it simplifies the reporting and determination of management priorities based on the bridge conditions. Several studies have employed this system as a foundation to predict future states of bridges. However, this approach can result in information loss and the disappearance of features by grouping continuous data based on the time history of damage. For instance, [PERSON] et al. [17] compared the condition-rating predictions of various algorithms, including LSTM, linear regression, and deep neural networks, and demonstrated that their accuracies did not noticeably differ. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Country** & **Condition Rating** & **Description** \\ \hline U.S. & 0–9 & 9: excellent, 0: fail \\ Italy & 0–5 & 0: census/geolocalization evaluation, 5: network resilience evaluation \\ Denmark & 0–5 & 0: insignificant deterioration, 5: no longer functional \\ Sweden & 0–3 & 0: repair after 10 years, 3: repairs required now \\ Germany & 0–4 & 0: defect has no effect, 4: structural strength is lost \\ Norway & 1–4 & 1: minor damage, 4: critical damage \\ Japan & 1–4 & 1: emergency action required, 4: healthy \\ England & 1–5 (Severity) & 1: no significant defects, 5: non-functional/fail \\ China & A–E (Extent) & A: no significant defects, E: \textgreater{}50\% surface area affected \\ China & 1–5 & 1: excellent, 5: fail \\ South Korea & A–E & A: excellent, E: fail \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bridge condition rating systems employed in various countries. Additionally, an analysis of the health index (HI) employed by different countries [26] reveals that most indices, including the condition index based on condition ratings, depend primarily on the current state of the structural components, without accounting for performance indicators related to operation, safety, and life-cycle costs. Most recently, Italy introduced a new multilevel approach for prioritizing bridge management, which includes a detailed classification system for bridge condition analysis to enable broader applicability [27]. However, the methods prove insufficient for proactive measures, such as assessing the rate of bridge deterioration. A more effective HI should incorporate metrics addressing various limit states, including utility, operational efficiency, and serviceability. Therefore, in this study, a novel HI as a quantitative evaluation metric for reinforced concrete slab bridges based on LSTM-based deterioration models was developed. The goal of this study is to develop a new BHI that expresses bridge evaluation results as quantitative data, enabling more precise assessments of bridge conditions. Raw defect data obtained during bridge inspections were collected for training the LSTM, and a preprocessing procedure to reduce the uncertainty of bridge maintenance data was introduced in detail. The developed HI was applied to six in-service RCS bridges to verify its effectiveness.