This dissertation presents methodologies to develop bridge condition deterioration models which accounts for non-stationarity in the deterioration process with applications to Florida concrete and timber bridges. A critical and comprehensive review of bridge deterioration modeling approaches is presented with illustrative examples based on regression, stochastic Markov-chain, mechanistic and Artificial Neural Network (ANN) models. This study also develops a framework for relating the qualitative National Bridge Inventory (NBI) condition ratings with normalized resistance of the concrete bridge component with application to concrete bridge T-beams to reduce the subjectivity of the NBI condition rating. A systematic approach for the prioritization of bridges for inspection is developed using the multivariate regression modeling technique, and forecasting models are developed based on multiple relevant variables for both concrete bridge superstructure and substructure components. This dissertation also develops an approach for risk and reliability assessments of concrete and timber bridges based on non-parametric deterioration modeling techniques such as average time-in condition rating (ATICR) and Kaplan-Meier (K-M) survival (reliability) models, for probabilistic prediction of bridge safety while accounting for the partial information from the incomplete bridge condition observations. This study develops relative deterioration rates based on the ATICR and illustrates the time-dependent probability of deterioration of the concrete and timber bridge components based on K-M estimates. Further, the relationship of explanatory variables to the survival time is discussed and estimates are made for the median survival years for reinforced concrete solid slab decks. This dissertation presents the code developed in R for multivariate regression analysis and data-driven reliability analysis. Future research studies in bridge deterioration modeling are also presented. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection