Innovation
Key Bridge Reconstruction: Engineers Look to AI, Machine Learning to Accelerate Rebuild Timeline
By Arnab Ghosh, Global Sales Engineering Director at Accuris
The collapse of the historic Francis Scott Key Bridge in Baltimore in March has left the city and nation grappling with the daunting task of reconstruction. More than a symbolic loss, the bridge's destruction has disrupted a critical link in the eastern corridor's supply chain, guaranteed to cause havoc on both business operations and daily life within the region. As recovery efforts continue amidst heaps of debris, engineering experts estimate that a complete rebuild adhering to stringent safety standards could take a staggering 10-15 years using existing methodologies and design approaches. Traditionally, architectural engineering and construction (AEC) projects, specifically for large infrastructure such as the building of bridges, have extensive and rigid design and planning phases. For over 200 years, the engineering discipline has formalized their approach to consider the hundreds of factors at play during such a process. This includes impacts on traffic flow, determining how precast concrete segments are manufactured and delivered, site-specific factors such as soil conditions, outlining necessary land purchases, and considering environmental elements, such as climate change and effects on wildlife. All while ensuring that projects adhere to both local and national standards and regulations. Therefore, it comes as no surprise that it could be more than a decade before Key Bridge is back in operation. Daily, engineers face a painstaking process in trying to locate the right tools, materials, and suppliers that fit their complex designs while ensuring compliance with ever-evolving regulations—a needle-in-a-haystack endeavor for even the brightest minds. The longevity of critical infrastructure construction is often the topic of public conversations but the rebuilding of a widely utilized transport channel adds serious urgency and complexity. So how can engineers eliminate some of this strain and complete projects on significantly reduced timelines?
AI Enters the Chat Artificial intelligence and generative AI tools such as ChatGPT and Jasper.ai have captivated the attention of organizations across all industries, especially over the past year. And they have earned the right to do so, boasting the potential to improve efficiencies and processes to save valuable time and resources. While construction and engineering sectors have been somewhat slower to modernize their digital infrastructure, leveraging AI and machine learning (ML) can significantly streamline design and construction processes, particularly for high-priority, large-scale, and intricate projects, such as the reconstruction of the Key Bridge. AI and ML are strongest in the presence of large data sets, where the technology can evaluate existing information and work to synthesize such data and provide recommendations. Construction, manufacturing and engineering are sectors rich with troves of regulations, historical data, project execution documents, best practices, lessons learnt, and design blueprints—making them ideal candidates for the effective application of AI and ML. Engineers spend more than 40 percent of their time searching for and processing information when they don't have credible sources rightly available, according to recent market research by Accuris. Obtaining accurate information in a timely, concise manner is an age-old pain point for many engineering professionals. With the Key Bridge, and most other critical infrastructure projects, dissecting endless local, state and national regulations and standards is perhaps the most challenging aspect of the design and build process. Deploying AI and ML algorithms and technology can help engineers
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csengineermag.com
Summer 2024
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