Key Bridge rebuild: leveraging artificial intelligence and machine learning for AEC projects

The collapse of Baltimore's Francis Scott Key Bridge sent shockwaves both nationally and internationally earlier this year. The city is now faced with the significant economic loss of a critical transport channel – and the challenging task of its reconstruction. Engineering and construction specialists anticipate that the rebuild effort could take up to 15 years. As a once-widely utilized structure, there is a deep sense of urgency to expedite the resurrection of the Key Bridge.

But current engineering and construction processes could wreak havoc on the possibility of an expeditious rebuild. The design and planning processes for large architecture, engineering and construction (AEC) projects, specifically critical infrastructure such as airports, bridges and ports, are historically rigid. Operating with a highly formalized approach derived from years of informed engineering practices, the volume of factors to contemplate around the build of a crucial transport structure is astronomical. Identifying and adhering to rigorous regulatory standards is only the first hurdle. Combine this with navigating other variables such as climate change, traffic implications and sourcing highly specialized materials, and suddenly a decade doesn’t seem like an unreasonable timeline for the construction of a major transport hub.

The bridge’s collapse has called into question current approaches to critical infrastructure design and construction and the lengthy timelines that result from such practices. The key to changes in efficiencies may lay within the depths of emerging technology – artificial intelligence (AI) and machine learning (ML).

AI: The New Window of Opportunity

Generative AI tools like ChatGPT and Jasper.ai have enthralled organizations worldwide, as many contemplate how the technology is set to transform business efficiencies to save time, capital and other resources. The construction and engineering sectors are often branded as 'slow movers' when it comes to digital modernization. However, these industries are perhaps set to gain the most from AI and ML capabilities.

AI and ML are most powerful with large data sets, where the technology can evaluate existing information and then condense such data and provide recommended actions. Notorious for maintaining historical data, from project execution documents to previous design blueprints and best practices, engineering and construction teams are ideal contenders for reaping the benefits of AI and ML. There are several key instances where the technology can be applied to streamline AEC projects, from design, right through to construction and even maintenance.

Benefits of AI and ML in AEC

Engineers spend a third of their time on non-value-added work, and 20 percent of their time is spent working with outdated information, according to Tech Clarity. With critical infrastructure projects comes a host of frequently changing regulatory standards to wade through. It has become merely impossible to keep pace and decipher which are most applicable to particular projects. This is where AI and ML algorithms can reap significant time saving for engineering teams. AI based knowledge search and discovery platforms built with an engineering focus can quickly sort through troves of standards, codes, and regulations, and provide critical engineering insights and synthetized list of relevant standards.

AI and ML can also be leveraged to create new, higher quality design concepts faster. Injecting previous blueprints and plans to generative AI models, and embedding the specific parameters of each project such as soil characteristics, wind load values, weight capacities, and earthquake protection, the model can quickly pinpoint possible development approaches based on the analysis of past data. In addition, it can enable engineers to predict the future performance of such infrastructure. Training ML data models on historic data can help anticipate long-term inspection and maintenance costs for structures and other assets so that regions can allocate specific materials and resources ahead of time.

AI technology also boasts significant advantages when planning and scheduling construction activities. For highly specialized projects such as the Key Bridge, engineers can spend months in search of appropriate vendors and unique materials. AI algorithms can be leveraged to enhance material selection and procurement practices by swiftly cataloging suitable suppliers and goods to deliver materials in a faction of the time.

AI-enabled project planning and scheduling algorithms can also leverage past project plans, budgets, and schedules to optimize construction sequences, resource allocation, and logistics, leading to more efficient project timelines and reduced costs and wastage. AI models trained on historical project data can be used to identify potential risks, bottlenecks, and areas for improvement, enabling proactive risk mitigation strategies.

Best Approaches for AI Success

The effectiveness of AI and ML algorithms hinges on the information that organizations leverage within these models. The data input must be high quality, up-to-date and accurate to avoid design errors and regulatory inaccuracies. Strong data governance is a core element of this process, and applying policies and guardrails which ensure proper data quality throughout this process is key to avoiding possible data misinterpretations.

Security is also an important issue. As cybercriminals continue to launch attacks, ensuring data privacy within the AI and ML algorithms leveraged for critical infrastructure projects is another key consideration. There are a host of cyber risks to AI software, and bad actors may look towards penetrating the technology to gather intel about foreign nation states to execute attacks. The project data which organizations upload to such tools should be carefully considered and evaluated to ensure that attackers cannot gain access to information that could breach the security and public safety of airports and government buildings. AI remains a largely unregulated industry federally, and therefore it is essential that organizations establish their own guardrails to prevent failing victim to a malicious attack.

Without a doubt, AI has the potential to reshape and transform design and construction mechanisms for AEC projects like the Key Bridge rebuild. By leveraging AI, engineers can build higher quality designs on an expedited timeline, avoid wasted efforts on trying to manually triage regulatory standards, and accurately pinpoint appropriate materials for their complex designs.

AI and ML will never fully replace humans within AEC projects. Instead they will be used as helpful tools for engineering teams.

To ensure that these new technologies help more than they hinder, robust education among design teams about the strengths and limitations of such technology is also pivotal. Engaging all stakeholders and promoting understanding, trust, and acceptance of AI technologies in infrastructure projects will ensure that organizations reap the benefits. Ensuring the validity of source data and strong data governance, combined with effective cybersecurity guardrails when utilizing AI software, will maximize output and success for engineering teams.

Image credit: cafphoto.aol.com/depositphotos.com

Arnab Ghosh is global sales engineering director at Accuris