Multi-Scale Methodologies for Probabilistic Resilience Assessment and Enhancement of Bridges and Transportation Systems

When an extreme event occurs, such as an earthquake or a tsunami, the amount of socioeconomic losses due to reduced functionality of infrastructure systems over time is comparable to or even higher than the immediate loss due to the extreme event itself. Therefore, one of the highest priorities of owners, disaster management officials, and decision makers in general is to have a prediction of the disaster performance of lifelines and infrastructures a priory considering different scenarios, and be able to restore the functionality in an efficient manner to the normal condition, or at least to an acceptable level during the emergency, in the aftermath of a catastrophe. Along the line of this need, academic research has been focused on the concept of infrastructure resilience, which reflects the ability of structures, infrastructure systems, and communities to both withstand against and quickly recover functionality after an extreme event. Among infrastructure systems, transportation networks are of utmost importance as they allow people to move from damaged to safe areas and rescue/recovery teams to effectively accomplish their mission. Moreover, the functionality and restoration of several other infrastructure systems and socio-economic units of the community is highly interdependent with transportation network performance. Among different components of transportation networks, bridges are among of the most vulnerable and need a particular attention. To this respect, this research is mostly focused on quantification, and optimization of the functionality and resilience of bridges and transportation networks in the aftermath of extreme events, and in particular earthquakes, considering the underlying uncertainties. The scope of the study includes: (i) accurate\\efficient assessment of the seismic fragility of individual bridges; (ii) development of a technique for assessment of bridge functionality and its probabilistic characteristics following an earthquake and during the restoration process; (iii) development of efficient optimization techniques for post-event restoration and pre-event retrofit prioritization of bridges; (iv) development of metrics and formulations for realistic quantification of the functionality and resilience of bridges and transportation networks.The evaluation of the damage and its probabilistic characteristics is the first step towards the assessment of the functionality of a bridge. In this regard, a simulation-based methodology was introduced for probabilistic seismic demand and fragility analyses, aimed at improving the accuracy of the resilience and life-cycle loss assessment of highway bridges. The impact of different assumptions made on the demand was assessed to determine if they are acceptable. The results show that among different assumptions, the power model and constant dispersion assumption introduce a considerable amount of error to the estimated probabilistic characteristics of demand and fragility. The error can be prevented using the introduced simulation-based technique, which takes advantage of the computational resources widely available nowadays.A new framework was presented to estimate probabilistic restoration functions of damaged bridges. This was accomplished by simulating different restoration project scenarios, considering the construction methods common in practice and the amount of resource availability. Moreover, two scheduling schemes were proposed to handle the uncertainties in the project scheduling and planning. The application of the proposed methodology was presented for the case of a bridge under a seismic scenario. The results show the critical impact of temporary repair solutions (e.g., temporary shoring) on the probabilistic characteristics of the functionality of the bridge during the restoration. Thus, the consideration of such solutions in probabilistic functionality and resilience analyses of bridges is necessary. Also, a considerable amount of nonlinearity was recognized among the restoration resource availability, duration of the restoration, and the bridge functionality level during the restoration process.A new tool called "Functionality-Fragility Surface" (FFS) was introduced for pre-event probabilistic recovery and resilience prediction of damaged structure, infrastructure systems, and communities. FFS combines fragility and restoration functions and presents the probability of suffering a certain functionality loss after a certain time elapsed from the occurrence of the extreme event, and given the intensity of the event. FFSs were developed for an archetype bridge to showcase the application of the proposed tool and formulation. Regarding network level analysis, a novel evolutionary optimization methodology for scheduling independent tasks considering resource and time constraints was proposed. The application of the proposed methodology to multi-phase optimal resilience restoration of highway bridges was presented and discussed. The results show the superior performance of the presented technique compared to other formulations both in terms of convergence rate and optimality of the solution. Also, the computed resilience-optimal restoration schedules are more practical and easier to interpret. Moreover, new connectivity-based metrics were introduced to measure the functionality and resilience of transportation networks, to take into account the priorities typically considered during the medium term of the disaster management.A two-level simulation-based optimization framework for bridge retrofit prioritization is presented. The objectives of the upper-level optimization are the minimization of the cost of bridge retrofit strategy, and probabilistic resilience failure defined as the probability of post-event optimal resilience being less than a critical value. The combined effect of the uncertainties in the seismic event characteristics and resulting damage state of bridges are taken into account by using an advanced efficient sampling technique, and fragility analysis. The proposed methodology was applied to a transportation network and different optimal bridge retrofit strategies were computed. The technique showed to be effective and efficient in computing the optimal bridge retrofit solutions of the example transportation network.