Revitalizing Infrastructure:

The Potential of AI and Machine Learning in the Key Bridge Rebuild 

By Arnab Ghosh, Director, Global Sales Engineering, Accuris

The devastating collapse of Baltimore’s Francis Scott Key Bridge in March shocked the world, deeply impacting the city's economic balance and presenting an arduous task of rebuilding this critical transportation artery. Industry specialists have estimated a possibility of up to 15 years for its reconstruction. 

Photo of the Francis Scott Bridge from wikipedia

Why such a long timeline?

Often, traditional practices for such large-scale architecture, engineering and construction (AEC) projects are complex and time-consuming. The process involves strict adherence to regulatory standards while also considering many other variables such as environmental changes, traffic implications, and sourcing of highly specialized materials. This rigid methodology results in very long project durations. However, the advent of emerging technology, specifically artificial intelligence (AI) and machine learning (ML), could drastically change this landscape. 

AI could be key to faster timelines 

AI is being touted as a game-changer, set to revolutionize business processes to achieve increased efficiency and reduce resource strain. And, indeed, in the AEC sphere, the capabilities of AI and ML could be transformative. One of AI's key strengths lies in managing, analyzing, and simplifying large volumes of data and offering instant recommendations for action. 

Market research by Accuris has revealed engineers spend more than 40 percent of their time locating and compiling data due to the sheer volume of information they need to sort through. This results in a slow, cumbersome design process, often hindering innovation. AI based Knowledge Search & Discovery platforms built with an Engineering focus can drastically improve this by sifting through a vast amount of data, including regulatory standards, codes, and regulations, offering critical engineering insights at your fingertips along with a synthesized list of relevant information. 

Innovative design concepts can also be created swiftly thanks to generative AI design algorithms. By injesting previous design blueprints into generative AI models, along with specific project parameters such as soil characteristics, wind load values, seismic protection or weight capacities, the model can provide potential development strategies based on past data analysis. ML can predict future performance and maintenance costs for infrastructures, thus helping in better resource allocation. 

Moreover, AI can significantly enhance planning and scheduling construction activities. For unique projects like the Key Bridge rebuild, selecting vendors and sourcing appropriate materials could be a lengthy process. AI algorithms can fast-track this by cataloguing suitable suppliers and ensuring material delivery in a fraction of the time, leading to more efficient project timelines, reduced costs, and minimal wastage. 

Despite the incredible capabilities of AI and ML, their success is largely dependent on the quality and accuracy of the data used to train them. Data security is another critical factor to consider, considering the rise in cyberattacks, ensuring data privacy is of paramount importance. Hence, robust data governance policies and stringent cybersecurity measures are vital to maximize output and success. 

Despite these warnings, AI and ML offer an innovative approach to AEC projects like the Key Bridge reconstruction. While they will not replace human ingenuity, these technologies offer invaluable tools for engineering teams, promoting efficiency, accuracy, and timely project completion. By combining the power of AI with data integrity and sound cybersecurity, we can successfully expedite the resurrection of our vital infrastructures. 

Learn more about using AI to locate, understand, and implement standards, codes, and regulations using Engineering Workbench Professional.