AI IN CEE MS STORY

Professor Greg Lowry is using robots to increase both safety and speed in the remediation process. He is working to develop terrestrial robots that can potentially autonomously explore natural environments, select sample locations, extract samples, and analyze the data online without exposing humans to hazardous conditions.

“The scope of our job is to build robots that drive around autonomously and take measures of contaminants from the oil and gas industries,” he says.

The robots—which can use X-ray fluorescence detectors to identify contaminants, such as salt concentration, drive themselves into areas that are difficult for humans to access. They are also designed for difficult terrain—much like the Mars rovers.

This accessibility allows the robots to gather hundreds of samples from each pit, providing data points that create a much clearer picture of onsite contaminants and hotspots. “The robots collect a lot more data than people can, are more representative of the whole pit, and keep humans out of potentially risky situations,” says Lowry. “They also provide reliable data that leads to better management decisions.”

Lowry’s research is based on collaboration between teams working in sensing, sampling, mobility, and autonomy. His team works to build contaminant detectors that take and analyze samples. Another team of engineers create the technology that allows the robot to drive through tough terrain. “This group works on what the mobility platform looks like – everything from wheels to ground clearance,” he adds.

The goal is for the robot to chart its own path, based on the information it’s received about the site. Lowry mentions that AI algorithms allow the robot to decide where to go next, given all of the samples it has already taken. “This minimizes uncertainty and allows the robot to collect a large number of samples in the least amount of time.”

He adds that robotic sampling has potential positive impacts outside of the oil and gas industries.

For example, if robots were able to sample vast areas of land after Hurricane Katrina, contaminated areas could have been identified and remediated much more quickly, allowing people back into towns and homes that remained closed for years afterward.

A prototype robot has already been tested in collaboration with industry sponsor Chevron and the PA Infrastructure Technology Alliance. Lowry adds that the team is continuing to test enhancements, such as LIDAR, to improve the robot’s function. While a roll-out of the robot is still up to five years away, it signals an important step in successfully remediating contaminants, leading to positive environmental impacts.

“If you own a contaminated site, you have to know what is there. But, the cost of sending people out to take samples is expensive and time consuming. This project creates a system that makes it much easier to identify contaminant distribution.”

New Master's Program: AI Engineering in CEE

As AI gets better, engineers need to take the next step. Carnegie Mellon University’s new Master of Science in Artificial Intelligence Engineering (MS AIE) combines the fundamentals of artificial intelligence and machine learning with engineering domain knowledge and culminates in an integrated capstone project — in other words, the rare know-how to develop and apply AI-based solutions within an engineering context.

“I’m excited to see the transformations that will happen to our profession once these students have graduated and taken on the workforce,” says Professor Mario Bergés. “This new generation of engineers will not only be able to master AI tools, but also to recognize how they can leverage engineering domain knowledge to extend them to be more practical and powerful.”

The program — first of its kind in the nation — trains students to truly integrate AI into practical solutions to Civil and Environmental Engineering problems — capabilities/talents that are currently not addressed by either computer science or traditional engineering disciplines alone.

“This program is unique in that it acknowledges the fact that engineers in the future will need to make use of and develop innovations in AI as part of their toolbox when formulating solutions to the problems they face,” says Bergés.

MS AIE core courses cover the fundamentals of machine learning along with the systems and toolchains needed to work with AI systems. “Aside from those fundamentals, students can choose from a broad set of elective courses and a capstone project course that allow them to concentrate on specific sub-disciplines of civil and environmental engineering and learn to leverage AI solutions in these contexts,” says Bergés. The program also teaches students to embrace the ethics or policy considerations which are crucial in

solutions-driven fields like engineering. As MS AIE graduates, they are set to have the This program is unique upper hand when they apply for high-demand in that it acknowledges careers within the Civil and Environmental the fact that engineers Engineering field. “With these skills, in the future will need to graduates would be able to pursue innovative make use of and develop career paths in areas such as intelligent innovations in AI as part transportation systems, structural health monitoring, facilities of their toolbox. management and environmental data analysis, to name a few,” says Bergés. More specific examples of industry positions in these fields include advanced analytics research scientist, deep learning research engineer, senior 3D process engineer, and senior engineer.