## 1 Introduction  
According to bridge maintenance standards of several countries, including South Korea, the United States, and the United Kingdom [1; 2; 3; 4], the maintenance procedure typically involves the following: (1) performing on-site inspections; (2) analyzing factors that impact bridge performance based on ultimate, service, and durability limit states; and (3) assessing the current performance level based on the analysis results. However, the amount of data required for the evaluation varies depending on the assessment level and significance, with insufficient data (e.g., material properties) substituted with values derived from statistical data and conservative assumptions. Although this method offers the advantages of prioritizing bridge maintenance and efficient allocation of personnel and costs, it faces limitations under a rapid increase in the number of aging bridges. With the advent of the Fourth Industrial Revolution, various technologies have been explored to assess the current conditions of bridges, such as data acquisition through Internet-of-Things sensors, artificial intelligence (AI)-based analysis of the acquired data, virtual scenario analysis via simulation models, and the creation of digital twins by integrating virtual models with actual structures [5; 6; 7; 8; 9]. However, these methods assume that a sufficient budget and human resources are available for the target bridges, even though such levels of resources can be provided only for some bridges. Consequently, a more rational and quantitative bridge assessment method that does not deviate significantly from existing inspection frameworks is required for most bridges. To address this issue, numerous machine-learning techniques have been proposed to track the current conditions of bridges based on past inspection and diagnostic data. Particularly in the United States, several studies have analyzed historical inspection data from the National Bridge Inventory to identify features that influence bridge condition ratings (ranging from 0-9) and develop machine learning models to predict them [10; 11; 12]. Among them, [PERSON] et al. [12] employed an artificial neural network (ANN) along with ten features to predict the conditions of bridge decks and achieved a prediction accuracy of 91.44%. To incorporate more features that influence bridge condition ratings, convolutional neural networks (CNNs), which are better suited than ANNs for learning high-dimensional data, have been considered [13; 14; 15]. ANNs process all features equally, whereas CNNs consider the relationships between features, resulting in highly accurate bridge condition predictions when many features are employed. However, CNNs alone do not adequately consider the temporal characteristics of features, and research has been conducted to address this issue. [PERSON] et al. [16] developed a degradation model by combining a recurrent neural network (RNN) and a CNN, and achieved prediction and conservative accuracies of over 80 and 85%, respectively, for the next three years. Because RNNs process longer time histories, they encounter difficulties in converting information from earlier data to the current state. The long short-term memory (LSTM) algorithm was developed to address this issue, and some researchers have employed it to predict bridge condition ratings [17; 18; 19]. [PERSON] et al. [19] employed it to determine the cumulative effects of damage over time. Their model was trained on inspection data collected from 3368 bridges in Japan, considering the potential factors influencing bridge deterioration (e.g., geometric characteristics, environmental factors, traffic conditions, and years of bridge use), which were categorized into deterioration grades (1-4). Subsequently, they built a deterioration-grade prediction model based on the deterioration factors and years of service and achieved accuracies exceeding 80%. Additionally, this result demonstrates that the LSTM model is superior to the multilayer perceptron model, which has an accuracy of 65%.