Optimizing choice of post-disaster recovery options by analyzing entire cities

A San Diego simulation
To demonstrate the effectiveness of their algorithm, Bocchini and Karamlou conducted a large-scale numerical analysis using an imagined earthquake scenario in the City of San Diego, California.

They chose San Diego for the size of its transportation network — it contains 238 highway bridges — as well as its importance and value as a U.S. strategic port. The total value of the port’s imports and exports in 2013 has been estimated to be more than $7 billion.

The researchers identified the 80 bridges that would sustain the most serious damage based on the seismicity of the region, and used AMIGO to calculate the best restoration strategy.

In a post-disaster situation, after the initial emergency response, those responsible for the recovery of a city or region must plan a repair schedule that balances mid-term and long-term recovery goals. Because every action will have an impact on the recovery, the trade-offs of each possible action must be considered.

AMIGO is of the class of optimization solvers that uses what are called heuristic techniques and evolutionary algorithms that are inspired by the process of natural selection. These techniques are particularly useful for solving multi-objective optimization problems using a Pareto-based approach. The approach, which describes a method of assessing a set of choices, is named after Vilfredo Pareto (1848-1923), an Italian engineer and economist who used the concept in his studies of economic efficiency and income distribution.

While the total number of feasible solutions in the imagined San Diego bridge network restoration scenario is considerably large, the results show that AMIGO managed to find a set of near optimal Pareto solutions in a small number of trials (about twenty-five generations).

From the study: “Moreover, a new bridge recovery model is proposed. Compared to the previous studies, this recovery model is more realistic, as it takes advantage of the available restoration functions obtained by experts’ surveys and scaling factors that account for the bridge cost.”

The researchers compared the performance of their optimization formulation with their previous optimization techniques. The results show significant improvement both in terms of optimality of the solution and convergence rate.

“This is of great importance, since for large realistic networks, the traffic analysis procedure can be computationally very expensive,” they write. “Therefore, reducing the number of required generations for convergence can considerably affect the computational cost of the problem and make this approach finally applicable to real-size networks. Compared to previous formulations, the use of operational resource constraints and the new recovery model yield the generation of more realistic schedules.”

Restore power or fix roads? Addressing interdependencies
This paper was the first to be published under a project called Probabilistic Resilience Assessment of Interdependent Systems (PRAISys), a collaboration between Lehigh, Florida Atlantic University, and Georgia State University. The team was awarded a grant of $2.2 million by the National Science Foundation (NSF) last year, as part of NSF’s $20 million investment in new fundamental research to transform infrastructure.” It is part of the Obama administration’s “Smart Cities” initiative to help communities tackle local challenges and improve city services.

Lehigh University notes that the interdisciplinary Lehigh team — led by Bocchini and made of up of faculty members with specialties in civil engineering, systems engineering, computer science and economics — is looking at how interdependent systems work together during and after a disaster. The goal is to establish and demonstrate a comprehensive framework that combines models of individual infrastructure systems with models of their interdependencies for the assessment of interdependent infrastructure system resilience for extreme events under uncertainty using a probabilistic approach.

“In the post-disaster phase, leaders are faced with tough choices. The impact of each decision will affect so many other areas so it’s important to go beyond looking at one system — such as transportation — and look at how they all work together,” said Bocchini.

— Read more in Aman Karamlou and Paolo Bocchini, “Sequencing algorithm with multiple-input genetic operators: Application to disaster resilience,” Engineering Structures 117 (15 June 2016): 591-602 (DOI: 10.1016/j.engstruct.2016.03.038)