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Using data for Better disaster recovery

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. “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 that would face civic leaders and engineers 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?

“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.”

To address this, Bocchini and Aman Karamlou, a P.C. Rossin Doctoral Assistant and structural engineering Ph.D. candidate, have 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 damage—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.

Read the full story at the Lehigh University News Center.

-Lori Friedman is Director of Media Relations in the Office of Communications and Public Affairs at Lehigh University.

August 29, 2016

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