NBI

Adaptive Bridge Condition Forecasting Through a Cluster-based Exploration This study introduces a novel forecasting method for bridge deterioration conditions, augmented with an exploration mechanism to identify a subset of bridges crucial for maintaining the predictive power of the model in subsequent years. The proposed method comprises two main components. Firstly, a forecasting model for bridge deterioration conditions was designed, formulated, and developed based on a novel framework for ordinal extreme gradient boosting (XGBoost). Secondly, an exploration mechanism is employed using a new multidimensional clustering method that integrates K-means clustering and Dynamic Time Wrapping (DTW). The proposed method is applied using historical condition assessment data from bridges in the three neighboring states of New York, New Jersey, and Connecticut. The outcomes validate the performance of the proposed method, demonstrating that the forecasting model accurately predicts future bridge conditions. Additionally, the clustering component successfully identifies a small subset of bridges essential for maintaining the prediction power of the forecasting model. The findings of this study will assist transportation agencies in utilizing their bridge inspection resources more efficiently and in customizing their condition assessment operations based on bridge characteristics and expected deterioration levels.