sort through millions of pages of standards, codes, and regulations, and compare and share versions of standards as they change. Information- gathering tasks that could have taken days in the past can now be completed in a matter of minutes. New natural language processing tools can help engineers turn their questions (for example, “Are there alternative materials I can use for this pipe that would still meet the pressure requirements of the current standard?”) into quick and accurate answers without having to write a complicated prompt. AI and ML can also be used to optimize the design process to create new, higher quality concepts faster. By uploading previous blueprints and plans to an AI algorithm and embedding the specific parameters of new projects (weight capacities, temperature changes or earthquake protection, etc.), engineers 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 machine learning data models on historic data can support long-term inspection and maintenance costs for structures and other assets so that regions can allocate specific materials and resources to these causes. In terms of planning and scheduling construction activities, AI technology also presents significant advantages to bolster material selection practices. For highly specialized, regulated projects, engineers spend months in search of appropriate suppliers and materials, oftentimes causing expensive delays to construction. AI can assist greatly in this realm to quickly catalog where to obtain necessary parts and develop best approaches to deliver such goods. AI enabled project planning and scheduling algorithms can leverage past project plans, budgets, and schedules to optimize construction sequences, resource allocation, and logistics, leading to more efficient project timelines and reduced costs. AI models trained on historical project data can also be used to identify potential risks, bottlenecks, and areas for improvement, enabling proactive risk mitigation strategies. Beyond design planning and construction, AI can improve the safety of critical infrastructure. In terms of Key Bridge, having cameras on the bridge, where data is ingested by a computer vision AI algorithm that can automatically calculate deviations in normal surrounding activity and alert necessary services or close the approaching traffic to the bridge, could prevent similar instances and save lives. Airports have already deployed such technology to identify suspicious packages for removal. Best Practices and Limitations The success of AI and ML algorithms in AEC projects largely depends on the information that is provided to it. To avoid design errors and regulatory inaccuracies, data input must be of high quality as well as up-to-date and accurate. Data governance is an essential component of this process, and implementing policies and guardrails to ensure proper data quality throughout this process is essential for avoiding misconceptions. Education amongst engineers and project managers is also pivotal to the concept's success and teams must be aware of its strengths and limitations.
Cybersecurity is another important factor, especially when leveraging AI technology for designing national critical infrastructure like bridges and airports. Cyber criminals may penetrate AI software to gather intel about foreign nation states to execute attacks. Organizations must proceed with caution about the data that is provided to such tools as AI remains a largely unregulated industry. Creating strict federal regulations is critical to the AEC industry, especially as more organizations start to leverage AI technology. Explainability and interpretability of generative AI models is a topic of great discussion and must be prioritized especially in the AEC industry. The use of explainable AI techniques and interpretable machine learning models, which can provide insights into engineer decision-making processes and enable better human understanding is critical to ensure project deliverables are trustworthy. Stakeholder engagement and continuous education of engineers, contractors, regulators, and the public, to promote understanding, trust, and acceptance of AI technologies in infrastructure projects is also a must for the AEC industry to successfully benefit from this AI boom. AI will not replace humans—rather, it will act as a companion to streamline workflows and spark creative recommendations. It certainly boasts the potential to completely transform AEC projects like the Key Bridge rebuild, helping engineers to design and construct critical infrastructure quicker and more efficiently than ever before. The technology will ultimately help engineers accelerate material selection and keep pace with evolving safety standards, to deliver smarter, compliant designs in a fraction of the time.
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Summer 2024 csengineermag.com
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