PERFORMANCE COMPARISON OF VARIOUS TIME-SERIES FORECASTING MODELS FOR BRIDGE SUFFICIENCY RATING PREDICTION

The rapid increase in the number of bridges worldwide has intensified the need for effective maintenance strat-egies to ensure structural safety and economic efficiency. Accurate predictions of future bridge performance are essen-tial for preventing unexpected failures and optimizing road network maintenance planning. However, existing prediction models frequently overlook the time-series characteristics inherent in bridge inspection data, thereby limiting their ac-curacy. This study aims to develop improved prediction models by integrating sequential data patterns using advanced deep-learning techniques. Data from the National Bridge Inventory were utilized. As most NBI data lacked explicit sequential structures, preprocessing techniques were applied to generate meaningful time-series patterns. Deep-learning models, including deep neural networks (DNNs), convolutional neural networks, long short-term memory (LSTM), and Transformers, were developed and evaluated using cross-validation to optimize their performance. Results showed that the LSTM model improved prediction accuracy by approximately 46% compared to the baseline DNN model. The Transformer model further improved accuracy by approximately 7% over the LSTM, highlighting its superior ability to capture long-term dependencies. These findings highlight the potential of the Transformer model as a powerful tool for predicting bridge performance, thereby supporting effective maintenance planning and reducing the risk of structural failures.