Automation is playing an increasing role in infrastructure engineering projects. Whether it is through the use of automation to assist the design process itself or the deployment of smart equipment as part of a project’s engineered solution, automation technologies — including artificial intelligence (AI) — are changing and disrupting every industry.
Broadly speaking, automation can be split into two categories: procedural automation and intelligent automation.
Procedural automation breaks down a given process into a series of manageable and predictable stages. Each stage receives knowable input parameters and then the system selects a predefined output option, based on criteria set for evaluating the inputs. Examples of this in the engineering process include parametric 3D modelling software and the use of genetic algorithms in generative design optimisation. With advancing computer technology, complex and extensive procedural automation is now possible. The technology is more accessible and cost-effective than ever for engineers to deploy, but it can only be used in narrow and unchanging applications, where all possible inputs can be specifically predicted and paired with predefined output actions.
Intelligent automation refers to the application of AI technology to determine the appropriate system outputs from given system inputs. Present AI systems are generally developed using one of three machine learning (ML) approaches: supervised learning, unsupervised learning and reinforcement learning.
Regardless of which ML approach is used, all rely heavily on high-quality datasets and/or prior experience, from which the AI can learn patterns that will appropriately influence its output decisions for various future inputs. In the case of reinforcement learning, it is important to specify explicit and well-considered reward functions to enable desired AI performance. Because intelligent automation makes use of AI, it has far more general and extensive application potential in infrastructure engineering.
The need for AI technology is rising in tandem with the demand for smart facilities and networks in all infrastructure sectors. As such demand grows, engineering firms will need to continue to expand their knowledge and use of AI in facility and process design.
The U.K. is the third-largest investor in AI technology development, after the U.S. and China. According to the U.S. International Trade Administration, the U.K.’s AI market is estimated to grow significantly over the next decade, adding $800 billion (circa £650 billion) to its economy by 2035. There is a clear opportunity for engineering innovators and disruptors to be at the forefront of this growth, delivering improvements in efficiency, safety, resiliency, performance and sustainability.
AI applications have the catalytic power to help individuals in engineering and construction reimagine the way societies operate. Here are three manageable actions to take into account when considering automation in design.
1. Take Little Steps Now
Start small, but start today. Begin by building an understanding of the basics of AI and ML, focusing particularly on neural networks (the underlying technology behind deep learning AI). Several neural network frameworks already exist, and most are open-source and free to access. This means there is no need to reinvent the wheel; merely apply the technology. Also, look for where there are semirecurring procedures and routine tasks in design projects and consider if these can be fully standardised.
It is key to remember that more advanced AI often relies on reinforcement learning from input data. If an engineering firm does nothing else in the short term, think about how high-quality design data (electronic drawings, models, studies, analysis, etc.) can be captured in a consistent, structured, metadata-tagged manner across all projects, so that the data will be readily available for AI training when it’s time to take the next step.
2. Think Big
Long-term seismic shifts will occur in a variety of industries, as a result of AI-driven robotics and automation. AI is seen as key to the next leap forward in quality and efficiency improvements in manufacturing, with the potential to unlock 20% more production capacity in existing semi-automated facilities. By leveraging intelligent automation systems, those who create infrastructure have the power to develop substantially more optimised solutions, as well as produce broadly deployable standardised solutions that could increase production capacity by an order of magnitude.
Automation-driven standardisation can help move more of the construction process off-site and earlier in the programme. This can lead to safety and sustainability improvements, larger cost savings, and reduced project durations and risk. It could even help the U.K. bridge the construction industry skills gap as the country looks ahead to the critical infrastructure needed to hit net zero carbon emission goals.
3. Embrace the Change
The reality is that AI is with us to stay. Companies should consider where the opportunities are, and plan accordingly. One of the most important changes that can be made early is training current and future workforces to be AI technology-savvy, so firms can take full advantage of engineering technologies coming to market. This will enable businesses to play their part in keeping employment strong and helping the U.K. continue to compete economically on a global scale, post-Brexit.
The West Midlands Digital Roadmap, embraced by West Midlands Mayor Andy Street, gives guidance on — and makes provisions for — a technologically advanced, forward-looking digital U.K. economy. Similar support schemes are being implemented around the U.K., so resources are available for engineering businesses to help with the transition.
Regarding training, everyone in society should take a broad interest in, and try to understand, AI fundamentals. Elements of this should be included in mandatory school IT curriculums, starting in primary school. AI will impact the way we all live and work in the not-too-distant future. Understanding the technology will help us govern its proliferation in safe and fair ways, allowing all in society to prosper from it.
Engineering firms need to be familiar with the many ways AI and automation can improve performance across industries. The use of AI and other advanced technologies will become more urgent as industries consider the complexities of next generation infrastructure. New approaches to design will be required because of ongoing technology advances. As society navigates the transition, we must remember that AI itself is not the destination, but it is the tool that gets us there.
As companies tackle future challenges in infrastructure and other sectors, the use of innovative technologies, like AI, will become increasingly critical. Learn more about how to unlock the value of digital engineering solutions.