ABSTRACT
The role of transportation networks is critical to the disaster resilience of communities, as these lifelines provide the necessary means of communication, transit, and basic services in the aftermath of extreme events. However, the experience of several past events has shown that the extensive vulnerability of bridges located in disaster prone regions might put the postdisaster performance of transportation networks in jeopardy. To this respect, proactive approaches should be considered as a solution to this issue. This includes strengthening, retrofitting, or rebuilding the vulnerable bridges in the network. However, typically due to the financial and logistic constraints, only a limited subset of these actions can be performed in a limited time span. Therefore, it is necessary to prioritize such pre-event disaster management activities.
To this respect, this paper presents a comprehensive framework for optimal retrofit planning of highway bridges. A two-level simulation-based optimization analysis is performed to obtain different tradeoffs for both pre-event retrofit and post-event restoration of bridges, considering the post-event resilience of transportation network and pre-event cost of retrofitting as the main objectives of the optimization problem. A Genetic Algorithm with a bi-objective mixed-integer programming formulation is used to compute the optimal retrofit strategies. The genetic operators, namely initial population generator, mutation, and crossover have been customized to operate the integer-valued design variables of the problem.
The first objective of the optimization problem is the minimization of the retrofit cost of the bridges in the network. The second objective is the minimization of the probability of the post-event optimal resilience being less than a specified critical value. Each optimal restoration strategy is computed from the simulated damage scenarios, through a lower-level optimization, using an efficient evolutionary algorithm with a combinatorial problem formulation (Karamlou and Bocchini 2015).
In order to take into account the uncertainties in the seismicity and the spatial correlation of the ground motion intensity measure throughout the studied region, a set of ground motion intensity maps are efficiently generated using a method called FQ-IDCVT and proposed by Christou et al. (2015). A Monte Carlo simulation is performed to generate bridge damage scenarios resulting from the intensity maps.
Moreover, a new network performance metric is proposed for more computationally efficient calculation of the post-event resilience.
Finally, the application of the technique is illustrated through an example of a transportation network (Figure 1) and the optimal retrofit strategies are discussed. The analyzed transportation network and a few representative samples of the peak ground acceleration in the region. https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781315207681/cd556cd4-4dcf-4efe-8e29-56fc67b8bfbd/content/fig150_1.tif"/>
