With the recent news of the Chancellor doubling funding for the UK’s AI sector, many businesses will be pausing to consider how they can harness the tool to improve processes within the structural engineering sector.
According to the latest statistics, the UK AI market is worth more than £16.9 billion in the UK economy alone and is expected to grow to £803.7 billion by 2035.
For many engineers, technology holds the promise of ushering in a new era of innovation and efficiency.
This includes expediting production processes, structural health monitoring and generative design.
With many forward-thinking businesses looking for a silver bullet, here’s how to best incorporate AI technology into structural engineering:
Design Optimisation
One of the primary areas where AI is making significant inroads is in design optimisation and structural engineering software.
Traditional design processes often involve manual calculations and iterative adjustments, which can be time-consuming and labour-intensive for engineering practitioners.
AI-driven algorithms, however, have the capability to analyse vast datasets and generate optimised designs based on these in a fraction of the time.
Reportedly, 74% of UK CEOs believe generative AI can bring a strategic advantage. Engineering firms are harnessing AI-driven design optimisation tools to streamline their workflows and deliver superior outcomes.
These tools leverage machine learning algorithms to explore a multitude of design options, considering factors such as material properties, load conditions, environmental constraints and the client’s aesthetic considerations.
By automating this process, engineers can focus their expertise on refining and fine-tuning designs, ultimately accelerating production and enhancing overall efficiency.
Structural Health Monitoring
AI-powered Structural Health Monitoring (SHM) systems have also emerged as invaluable tools for ensuring the safety and reliability of infrastructure assets.
By continuously analysing sensor data and identifying patterns indicative of structural degradation or anomalies, these systems enable early detection of potential issues, minimising the risk of structural failures.
For instance, SHM systems are often used on critical infrastructure assets such as bridges and tunnels in the UK. Sensors, acoustic emissions and vibration technologies are used to detect the overall conditions of the structure.
By providing real-time insights into structural health, these systems empower asset managers to prioritise maintenance activities, optimise resource allocation, and, ultimately, ensure the safety and resilience of vital buildings and transportation networks.
Construction Management and Scheduling
AI algorithms can also be used to optimise project scheduling, resource allocation, and risk management.
A recent survey found that 31.4% of respondents said that poor initial planning and unrealistic expectations were the number one reason for construction delays.
By analysing historical project data, supply chain dynamics and even weather forecasts, this risk can be mitigated.
AI systems can generate realistic timeframes that enable proactive decision-making and dampen the impact of unforeseen disruptions.
These platforms leverage machine learning algorithms to optimise resource utilisation, identify potential bottlenecks, and dynamically adjust project schedules in real-time in response to changing conditions.
As a result, projects can be completed faster, with fewer delays and cost overruns, ultimately enhancing client satisfaction and profitability.
Building Information Modeling (BIM)
One of AI’s most thrilling uses is building information modelling (BIM), which uses data sets to create a virtual mock-up of an entire project.
The tool enables collaboration and coordination among stakeholders throughout the full project lifecycle.
For instance, AI-driven BIM platforms leverage algorithms to automate tedious tasks such as clash detection, quantity takeoffs, and 4D scheduling. This virtual ‘heavy lifting’ frees up valuable time for engineers to then focus on the more strategic aspects of project delivery.
In the UK, construction companies can use the platform to facilitate data-driven decision-making, enhance communication and coordination among project teams, and minimise errors and rework.
Conclusion
Shiny new AI technology can be daunting at first, but when employed effectively, it can bring clear benefits for the structural engineering sector—enhancing buildings and your bottom line.
