Post-disaster recoveryOptimizing choice of post-disaster recovery options by analyzing entire cities
Civic leaders and engineers are typically faced with a very large number of possible recovery options in the aftermath of a major catastrophic event, such as a hurricane or an earthquake. “If you are trying to solve a problem that has, say, ten possible outcomes — you can probably find a way to figure out which one is optimal; [b]ut what if the possible solutions number as high as 10 to the 120th power?” ask researchers. They have developed a versatile and novel technique which is the first to factor in so many elements, demonstrating its effectiveness on transportation network recovery in imagined post-earthquake San Diego.
Some problems, says Paolo Bocchini, cannot be solved through intuition.
“If you are trying to solve a problem that has, say, ten possible outcomes — you can probably find a way to figure out which one is optimal,” says Bocchini, assistant professor of civil and environmental engineering at Lehigh University. “But what if the possible solutions number as high as 10 to the 120th power?”
To illustrate the size of that figure, 10 to the 120th power, in long form, is written as a “1” followed by 120 zeroes.
That is the massive number of possible recovery options with which civic leaders and engineers would be faced in the aftermath of a major catastrophic event, such as a hurricane or an earthquake.
“In a post-disaster recovery period, there may be one, large, very important bridge to repair that would take as long as a year to restore to full functionality,” says Bocchini. “During that year, you could restore four smaller bridges which might have an even greater impact on getting the city back up and running. So, how do you figure out which choice is optimal?”
He adds: “Computational models that predict what might work for one bridge or five bridges, simply don’t work when you try to scale up to 100 bridges.”
Lehigh University says that to address this, Bocchini and his colleague Aman Karamlou, a doctoral assistant and structural engineering Ph.D. candidate, created a novel method that represents a major improvement in existing computational models and optimization methodologies. Their technique, Algorithm with Multiple-Input Genetic Operators — or AMIGO, for short — is described in a paper that was recently published in Engineering Structures.
Designed to consider very complex objectives while keeping computational costs down, AMIGO makes the search process more efficient and expedites the convergence rate (the speed at which the sequence approaches its limit). It does this by taking advantage of the additional data in the genetic operators which are used to guide the algorithm toward a solution.
In addition to being the first model to factor in so many elements, AMIGO is unique for its versatility.
“AMIGO takes the topology or characteristics of a network — as well as the damag — and then develops optimal recovery strategies. It can be used to solve a variety of scheduling optimization problems common in different fields including construction management, the manufacturing industry and emergency planning,” says Bocchini.