By Emily Newton
While machine learning is an incredibly powerful tool, in some industries it’s still in a developmental stage. In other words, we are still exploring applications for its use and refining how that’s done. Machine learning for engineers and civil engineering is one such field. Even now, it’s clear that it’s going to have a huge impact.
Machine learning (ML) will change the way we plan, the way we build, and even the way we maintain everything. The specifics of how and where depend on how we deploy the actual technologies behind it. Every form of artificial intelligence, including machine learning, needs data to ingest. It will take some time to pinpoint and understand what data is most useful for these platforms, which is where we are now.
Today’s machine learning tools leverage algorithms to parse and understand ingested data, allowing them to find trends, patterns, and other insights of value. These algorithms act as guidelines, essentially telling the system where to go, what to look for, and what to do with that information.
This is relevant because those algorithms, or guidelines, need to be established and optimized to empower AI and ML technologies. In civil engineering, they will be applied in various ways to complete planning, construction, and similar tasks.
For example, if you want the machine learning or AI solution to use available data and design a blueprint, you need to tell it where to look for the relevant specs, how to find it, and what to do with it. You also need to define the parameters for that design, so the system doesn’t create outside the required boundaries and tolerances.
It’s important to understand this concept because it helps reveal how the technologies will collaborate and cooperate with their human counterparts. They will work alongside human laborers rather than taking their jobs outright.
In many ways, we are still in the developmental phase for said algorithms. Every day we discover new ways to leverage the technology, but there’s still a lot of untapped potential. So, what can it do? What are some theories about how to apply machine learning for engineers?
A relatively new form of design technique, generative design involves the use of AI to solve complex problems and create blueprints, models, and beyond. The technique is used across several fields, not just civil engineering and construction.
The big picture also provides a better idea of how the technology fits into the modern world. The automotive and aerospace industries benefit from smarter designs, like complex internal lattices and aerodynamic components. Consumer goods benefit from more durable and structurally sound products, effective shapes and patterns, and more.
By using these tools, designs can be created around conflicting constraints or complex specifications. So, where a human architect or designer may not be able to find a solution, advanced intelligence can, thereby creating a workable print.
This has major implications in civil engineering, where projects are rife with complications and sophisticated problems. The system can not only create more successful plans but also remarkably unique products — including fantastical structures like the Heydar Aliyev Centre in Baku, Azerbaijan.
Civil engineering projects call for a huge selection of tools, heavy machines, and expensive materials. Managing these assets is a challenging task, both during working hours and outside of them, as well. Theft can be a rampant problem in some areas, especially in remote locations. Machine learning can assist by providing a real-time, always-on crime-deterrent in the form of smart asset tracking.
The concept is quite simple. All devices, tools, equipment, hardware, and even supplies are outfitted with smart tags, like RFID or IoT solutions. A central hub or software — powered by machine learning — constantly analyzes the location, environment, and data from conditional sensors. When something is strange, or when the assets move, that system can alert the necessary parties and even take action.
For example, imagine the system remotely disabling a piece of equipment so that thieves cannot move it or take it away. Or, that system could alert nearby authorities, allowing ample time for the thieves to be caught.
Smart asset tracking can be used for much more than theft and fraud, however. It can help organize projects by allowing workers to see what’s in use, and what’s available. It can facilitate proper project planning and management strategies. It can also be used to discern performance, progress, and other factors, remotely by team leaders.
A huge part of what makes AI and ML technologies so amazing is that they can process absolutely massive swarms of data in a relatively short period. It also allows them to discover hidden trends, patterns, and insights, and also make accurate predictions through the analysis of historic and current information. In civil engineering, this could potentially help discover and create new solutions to complex problems, or present ideas we didn’t even know existed.
Imagine leveraging machine learning to design vertical farming solutions, come up with compounds for self-healing concrete, or build modular structures. These are all things happening in the field right now, but machine learning can take them to a whole new level of efficiency and design.
Risk mitigation happens naturally and daily on a project site. It involves analyzing the site, the surrounding environment, external conditions like weather, hazards, worker safety, and much more. But because there’s so much to consider, measure, and plan for, superintendents and project managers can make mistakes and miss details rather easily.
What if it was all delegated to a machine learning system through smart technologies and contextual data?
What’s more, what if that machine learning solution could directly notify or message anyone on the job site to update them about current conditions? For example, the tool might send an alert to a worker entering a hazardous area, telling them what to watch out for or even letting them know they’re in danger. Or, maybe that tool can notify superintendents that supplies are lower than expected and more will be necessary to meet the current projections.
That same tool could even assess the surrounding area to detail potential flooding problems, pinpoint uneven or challenging site terrain, and much more.
It can also learn to prioritize select issues, sending precisely the right notifications to the right people and with the right level of urgency. In this way, the technology will revolutionize risk mitigation, especially on large and complex project sites.
The most important thing to understand about machine learning, AI, and neural network technologies is that we are still finding and exploring new ways to leverage them. What we know for sure is that civil engineering and construction are indeed changing, thanks to generative and data-oriented design techniques, real-time asset management, and smarter risk mitigation and job site safety. New discoveries and avenues are explored every day, which shows the incredible promise of machine learning for engineers and development applications.
About the Author
Emily Newton is an industrial writer who specializes in covering how technology is disrupting industrial sectors. She’s also the editor-in-chief of Revolutionized where she covers innovations in industry, construction, and more. Subscribe to her newsletter for the latest articles from Emily.