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Bridging AI and Civil Engineering
DesignSafe is increasingly integrating AI and machine learning to enhance its capabilities and impact. This includes using AI for tasks such as building recognition, assessing damage from images, and predicting wind pressure coefficients. Additionally, DesignSafe provides resources for researchers to use AI and machine learning in their own projects, such as through Jupyter notebooks, interactive data analytics, and access to high–performance computing.
Beyond enabling scientists to mine publicly available data for AI-driven scientific discovery, DesignSafe is also pioneering the development of AI-powered chatbots to enhance its interface, making the platform more intuitive and accessible for researchers.
“The AI chatbot will come back with a clear, textual, description of exactly what you are asking of DesignSafe, whether it is ‘What is all the lidar data that’s available at a location?’ or ‘How do I run 1 million simulations with the data on a high-performance computer?’” Rathje said.
Chishiki-AI is a project in collaboration with DesignSafe and TACC that fosters innovation in the field of Civil and Environmental Engineering through the integration of artificial intelligence. Image credit: Chishiki-AI
A notable example of AI innovation at DesignSafe is the NSF-funded Chishiki-AI project, which AI and civil engineering experts at UT launched in 2023 in collaboration with Cornell University. Named after the Japanese word for “knowledge,” Chishiki-AI aims to accelerate the integration of AI into civil engineering, advancing research, education and practical applications in hazard resilience.
“As AI continues to evolve, we see an urgent need to educate civil engineers and equip cyberinfrastructure professionals to handle the new challenges which AI and civil engineering brings together,” said Krishna Kumar, principal investigator of Chishiki-AI, an assistant civil engineering and affiliate faculty member professor at UT’s Oden Institute for Computational Engineering and Sciences.
For example, engineers must understand AI limitations, such as convolutional neural networks trained to recognize structures in environments with bright lighting or minimal graffiti, which may underperform in more variable, real-world situations. Conversely, cyberinfrastructure professionals need deeper insight into the unique demands of civil engineering, including how to deploy large-scale AI models in post-disaster scenarios where power and connectivity are limited.
“Civil engineering presents unique challenges — not only in the context of natural hazards addressed by DesignSafe, but also in emerging areas like autonomous construction,” Kumar said. “As we push the field forward, we are embracing an AI-accelerated engineering paradigm that redefines how we meet society’s evolving infrastructure needs.”
Looking ahead, Rathje envisions a deeper, more transformative integration of AI with DesignSafe data, unlocking new possibilities for research and innovation.
“We want to make the AI and machine learning training more seamless,” she said. “One way we’re working to make it easier is developing models that can automatically apply tags to images upon upload with damage information.”
In addition, AI-enabled pre-curation of data shows strong potential to streamline the initial stages of data organization, helping DesignSafe users jump-start the upload process with greater efficiency and confidence.
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