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The peculiar nature of bridge infrastructure condition data persistently poses challenges in predicting bridge component deterioration that necessitate the continued investigation of probabilistic modeling techniques. These challenges include the uncertainty that characterizes bridge condition data due to the inherent random nature of deterioration factors and the existence of other variables that are not typically measured (unobserved factors responsible for deterioration), the panel nature of the data and its consequent observation-specific correlation and heterogeneity bias, and the lack of knowledge of the type and nature of past interventions. To these ends, this paper introduces a novel probabilistic modeling methodology intended to enhance the reliability of condition prediction by defining and quantifying the types of interventions and incorporating newly introduced explanatory variables to capture the effect of …