Sustainable performance of critical infrastructure like bridges under both service loads and extreme events is of growing importance to the society. This performance of highway networks and their constituent bridges can be mapped to sustainability indicators like cost, embodied energy, carbon dioxide (CO2) emissions or resource utilization. With heightened load demands due to overweight trucks and natural hazards on the aging bridge infrastructure, an integrated multi-threat sustainability framework for bridges and bridge networks is essential. The existing sustainability quanti cation methods and approaches lack a joint sustainability assessment considering bridge vulnerabilities to hazards as well as truck loads, especially using a probabilistic approach. The primary goal of this study is the development of probabilistic frameworks to assist and evaluate sustainability on two levels: bridge and bridge network. Bridge sustainability is dependent on contributions from different life-cycle phases like construction, operation, maintenance, failure and demolition. Unlike past life-cycle studies, the probabilistic life-cycle sustainability analysis (LCS-A) framework proposed in this study considers life-cycle phase interactions as well as integrates post-repair performance of bridge components while exploring the distribution of sustainability costs to provide a more holistic view of an bridges sustainability. Interactions in the life-cycle emerge due to interventions such as maintenance activities which primarily benefit service load performance, but also enhance hazard performance, a previously unexplored secondary interaction effect from life-cycle studies. For bridges subjected to multiple hazard events in their lifetime, the impact of post-repair modification of bridge component behavior in LCS-A is significant and cannot be neglected. This study gathers the insights from bridge level LCS-A and integrates it into developing a probabilistic sustainability evaluation framework for highway networks as well. The inclusion of bridge failures due to both spatial variation of hazard as well as truck presence is a key advancement proposed through this research. The traffic simulation of overweight trucks on a bridge in the network is developed using the average daily truck traffic on the bridge and using extreme value theory to predict percentage of overweight trucks. The variation in hazard occurrence and intensity is captured by using a probabilistic suite of scenarios for the network. Advances are also made in the methodology to evaluate traffic emissions by incorporation of traffic flow modeling and fuel congestion into the network assessment. The LCS-A and network frameworks developed in this study are capable of handling various sources of uncertainties, with propagation of uncertainties facilitated by use of surrogate models when predicting bridge failures. In addition to developing probabilistic distributions of sustainability metrics, this study also recommends using probabilistic sensitivity analysis to understand how the uncertainty in the sustainability indicator is influenced by uncertainties in the input parameters. Such a detailed sensitivity analysis highlights opportunities for reducing uncertainties in sustainability outcomes by focusing on reducing the uncertainties in the most important input parameters. The sustainability framework developed as part of this thesis also can be used to de-aggregate bridge or network sustainability into contributions from their constituent components. A new sustainability informed component importance measure (SCIM) is proposed in this study that leverages the probabilistic nature of the sustainability contributions from individual components and maps it to system level sustainability consequences. The SCIMs proposed in this study is developed by adopting a flexible system failure based on a user-defined threshold on the system level sustainability indicator. The temporal evolution of component importance due to aging related deterioration or potential change in user-de fined thresholds are also incorporated into the SCIMs. The probabilistic frameworks developed in this thesis can support owners in their efforts to improve bridge or network sustainability,such as evaluating the impact of interventions or repairs on performance of bridge for future hazards or service loads and making upgrade decisions to minimize impacts of bridge failures to the surrounding natural and built environment. Moreover, stakeholders may pose sustainability objectives or intervention schedules for preferred risk threshold given new insight on the full probability distribution of sustainability outcomes.