Arnab Ghosh, global sales engineering director at Accuris, a company that provides technology solutions that optimize workflows, explores how artificial intelligence (AI) and machine learning (ML) technologies can accelerate the design and construction of large-scale infrastructure projects.
The architecture, engineering, and construction (AEC) industry has long been characterized by formal processes and rigorous methodologies honed through decades of informed engineering practice.
When it comes to designing and planning large, critical infrastructure projects like airports, bridges, and ports, the amount of factors to consider is astronomical. From identifying and adhering to strict regulatory standards to dealing with variables like climate change, traffic impacts, and sourcing highly specialized materials, the complexity is staggering. It's no wonder that decade-long construction times for major transportation hubs have become the norm.
However, an earthquake earlier this year shocked the AEC industry, prompting a re-evaluation of traditional approaches. In March 2024, Baltimore's Francis Scott Key Bridge, once a widely used and important transportation link, suffered a catastrophic collapse, leaving the city with significant economic losses and facing a daunting rebuilding challenge.
Engineering and construction experts predict that the rebuilding effort could take up to 15 years, given the complex web of regulations and other macro considerations. While there is a strong sense of urgency to restore this critical infrastructure, this long timeline underscores the urgent need for innovative solutions that can speed up the design and construction process.
The collapse of Key Bridge has highlighted the inefficiencies inherent in existing methodologies and sparked discussion that emerging technologies, particularly AI and ML, could be the key to driving unprecedented efficiencies in the AEC field.
AI: Opening up new possibilities
The recent proliferation of generative AI tools such as ChatGPT and Jasper.ai has attracted organizations across the globe who see the potential for these cutting-edge technologies to transform their business operations and save time, capital, and other valuable resources. The construction and engineering sector is often described as a “slow mover” when it comes to digital modernization, but it is poised to become one of the biggest beneficiaries of AI and ML capabilities.
These advanced technologies are powered by large data sets that can evaluate existing information and condense it to provide actionable recommendations. AEC teams are ideally suited to leverage AI and ML because they have extensive repositories of historical data covering project execution documents, blueprints, best practices, and more. The possibilities for streamlining processes across the entire project lifecycle, from design and construction to maintenance, are endless.
According to Tech Clarity, engineers spend one-third of their time on non-value-added tasks and 20% of their time dealing with outdated information. Critical infrastructure projects add an extra layer of complexity by having to navigate a labyrinth of frequently updated regulatory standards.
AI and ML algorithms can significantly reduce the time spent by engineering teams. AI-based knowledge search and discovery platforms comb through vast amounts of standards, codes, and regulations to provide critical engineering insights and a consolidated list of relevant standards tailored to the specific requirements of each project.
Additionally, generative AI design algorithms can be leveraged to generate higher quality design concepts faster. By injecting previous designs and plans into generative AI models and embedding project-specific parameters such as soil properties, wind load values, weight capacity, and seismic resistance, the models can rapidly identify potential development approaches based on the analysis of historical data.
Additionally, training ML models on historical data can help predict long-term inspection and maintenance costs for structures and other assets, enabling local and municipal governments to proactively allocate resources.
The benefits of AI also extend to planning and scheduling construction activities. For highly specialized projects like the Key Bridge reconstruction, it can take engineers months to find the right vendors and unique materials. AI algorithms streamline this process by quickly cataloging the right suppliers and goods, delivering the necessary materials in a fraction of the traditional procurement timeline.
AI-powered project planning and scheduling algorithms can also leverage historical data on past project plans, budgets, and schedules to optimize construction sequences, resource allocation, and logistics. This data-driven approach results in more efficient project timelines, reducing costs and minimizing waste. Additionally, AI models trained on historical project data can identify potential risks, bottlenecks, and areas of improvement, enabling proactive risk mitigation strategies.
Ensuring successful AI/ML adoption in AEC
The potential benefits of AI and ML in the AEC space are undeniable, but several key factors are critical to realizing these benefits. First and foremost, the effectiveness of AI and ML algorithms is directly proportional to the quality, accuracy, and timeliness of the data used to train these models. Strong data governance, including policies and guardrails that ensure proper data quality throughout the process, is paramount to avoid design errors, regulatory inaccuracies, and potential data misinterpretations.
Education and stakeholder engagement are essential in any project involving emerging technologies. Fostering understanding, trust, and acceptance of these advanced tools among engineering teams and other stakeholders will enable organizations to maximize the benefits the tools offer. The use of explainable AI techniques and interpretable ML models that provide insight into the decision-making process and improve human understanding is essential to ensure project deliverables are trustworthy.
Security is also paramount. In an era of ever-present cybersecurity threats, data privacy and security must be a top priority when leveraging AI and ML solutions, especially in the area of critical infrastructure. Nation-state actors are increasingly launching sophisticated cyberattacks, heightening the risk that malicious actors could attempt to infiltrate AI software to gather information about foreign infrastructure projects.
Therefore, organizations must carefully evaluate the data they upload to AI tools and implement strong cybersecurity measures to prevent breaches that could threaten public safety and national security. Because AI is largely unregulated at the federal level, proactive organizations must establish their own strict guardrails to mitigate these risks.
The Future of AEC Transformation
AI and ML technologies can reshape and transform the underlying design and construction mechanisms of AEC projects. Leveraging the power of these advanced tools, engineers can build higher quality designs faster, avoid the wasted effort of manually triaging regulatory standards, and accurately identify the right materials for complex designs. However, it is important to recognize that AI and ML do not fully replace human expertise, but rather act as powerful enablers that augment and enhance the capabilities of engineering teams.
A holistic approach is required to maximize the success of AI and ML adoption in the AEC space. Ensuring the validity and quality of source data through robust data governance practices, combined with effective cybersecurity measures and stakeholder engagement, will be crucial in mitigating risk and realizing the full transformative potential of these cutting-edge technologies.
As the AEC industry grapples with the urgent need to accelerate critical infrastructure projects like the Key Bridge reconstruction, now is the time to embrace AI and ML innovations. Leveraging these advanced capabilities wisely and with clearly defined strategies, engineering and construction teams can usher in a new era of efficiency, innovation, and resiliency to shape the infrastructure of tomorrow while addressing today's pressing challenges.
