Engineering Tech: What’s Real and What’s to Come?
With all of the technology buzzwords in the engineering industry today, it can be difficult to differentiate between what’s hype and what’s real. To clear up some of the confusion, the ACEC Research Institute brought together four experts for a roundtable in mid-June to discuss which cutting-edge technologies will likely have the biggest impact on the industry.
In “The Impact of Technology on Engineering” roundtable, McKinsey Partner Jose Luis Blanco, Autodesk VP of Research Mike Haley, ETH Zurich Director of Strategic Foresight Chris Luebkeman, and Jacobs SVP Technology and Innovation Heather Wishart-Smith recommended three technologies to track.
“I’m a firm believer that to be a strong professional, you must keep up with what’s happening in your profession,” said Luebkeman. “If you don’t, you’ll lose relevance and have a dwindling market share.”
A Digital Twin is a virtual model of a physical process, product or service. By monitoring systems and analyzing data digitally, engineers can head off problems before they occur, develop new opportunities, and even use simulations to plan for the future.
The technology isn’t quite ready for prime time because digital twins cannot yet understand the real-world interaction of systems that is required for proper modeling. Until they can accurately reflect a problem, engineers can’t optimize a solution.
Digital twins in the future will use sampling to correctly represent a situation. The technology will measure the data, learn from that data, generalize patterns, and then place them within the digital twin. Engineers can then leverage that digital twin to optimize their designs and investigate alternatives.
There are a few tools that make use of digital twin technology. Engineers at water treatment and industrial water plants use the Replica tool to run scenarios to optimize the systems to prevent overflows in the event of an emergency response.
Artificial intelligence (AI) is the simulation of human intelligence processes by computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition, and machine vision.
In the engineering sector, AI combines software and hardware systems. Engineers build AI models using machine learning algorithms and deep learning neural networks to reach insights that can be used to make real-world decisions.
Although the profession is making great strides in the machine learning needed to create AI, we are not there yet. We can automate various tasks, especially the most repetitive ones, and that is a step in the right direction, but the technology remains in its infancy.
Machine learning is a subset of AI in which systems automatically learn and improve from experience using computer algorithms without being explicitly programmed. Data analytics — the science of analyzing raw data to reach conclusions — is at the heart of machine learning. Everyday examples are recommendations you receive from Amazon or Netflix.
The engineering profession is increasingly using machine learning and data analytics. One panelist described a project at a NASA site in Hampton, Virginia. More than 120,000 sensors around the campus measure factors such as vibration, temperature, and humidity. Using predictive analytics and machine learning, the system can create equipment maintenance schedules and can even anticipate when equipment will wear out.
“You don’t have to dive deep into these technologies,” says Luebkeman, “but at least understand the opportunity that each of these new technologies is bringing to you and your firm.”
Click here to view the roundtable.