Engineering Frontiers Fall 2018

Resilience Engineering

Other researchers are developing data-driven techniques to assess, monitor and enhance the resilience of power grids, better protect communities from tsunamis, and more accurately predict urban reservoir levels for more effective water management strategies

More than half of the nation’s major power failures from 2000–2016 were caused by severe weather, affecting millions of people and costing billions of dollars, says Roshanak Nateghi, assistant professor of industrial engineering and environmental and ecological engineering.

“The number of billion-dollar climate disasters is expanding rapidly and so is the cost associated with them,” she says.

At the same time, vast quantities of data are available from numerous sources, and advances in machine learning are providing new modeling tools to improve resilience.

Monitoring Structural Health

In Disaster Response

Pruning Neural Networks to Inspect Infrastructure Degradation

Neural networks promise future robots, drones and other “edge devices” the capability to automatically inspect elements of the infrastructure for cracks and corrosion and providing life-saving emergency-management services.

“Civil infrastructures constantly face aging issues, natural hazards, poor usage and extreme weather conditions,” says Mohammad R. Jahanshahi, assistant professor of civil engineering. “This causes degradation in the service life of the infrastructure, and the damage or defects may propagate over time and potentially be harmful or dangerous. For instance, potholes, corroded underground sewer pipelines and cracks in building or bridge columns could lead to serious deterioration and even catastrophic events if not detected early.”

Deep Learning Key to Future Data-Driven Monitoring

In Disaster Response

Artificial intelligence is allowing researchers to improve the assessment of disaster-related damage to buildings, structures and the power grid in efforts to mitigate and avert catastrophic failure.

In the aftermath of major earthquakes, engineers descend on the scene and must quickly document damage to structures before crucial data are destroyed.

“These teams of engineers take a lot of photos, perhaps 10,000 images per day, and these images are critical to learning how the disaster affected structures,” says Shirley Dyke, professor of mechanical engineering and civil engineering. “Every image has to be manually analyzed by people, and it takes a tremendous amount of time for them to go through each image and put a description on it so that others can use it.”

Engineering teams routinely spend several hours after a day of collecting data to review their images and determine how to proceed the next day.

“Unfortunately, there is no way to quickly organize these thousands of images, which are essential to understanding the damage from an event, and the potential for human error is a key drawback,” says Dyke. “When people look at images for more than one hour, they get tired, whereas a computer can keep going.”

Read more: Deep Learning Key to Future Data-Driven Monitoring

Mapping New Terrains

In Disaster Response

Remote, mobile-sensing systems can make quick work of gathering and processing tedious on-the-ground data, and the potential applications are immense.

Road construction. It’s the bane of every driver but, if the work is executed efficiently, construction crews can minimize traffic headaches and frayed nerves. Ayman Habib, the Thomas A. Page Professor of Civil Engineering, is a critical ally in this delicate dance. Habib’s research on mobile mapping systems is intended to provide high-definition, high-fidelity 3D maps and data of the road network, which can be mined for insights. Case in point: the mobile mapping system can accurately provide lane widths while driving at normal speed and guide construction crews to maintain appropriate lane width so as to disrupt traffic only minimally.