Research Group of Xinzheng Lu

AI-structure Copilot Reaches 5,000+ Shear Wall Design Cases
AI-structure Copilot (https://ai-structure.com/) has now reached 5,000+ shear wall design cases.

For us, this milestone is more than just a number. It means that AI-structure Copilot has been continuously tested and improved through real engineering use, with feedback from structural engineers gradually shaping the product into something more practical, reliable, and aligned with everyday design workflows.

In recent iterations, we focused on making intelligent shear wall design not only capable of generating results, but also easier to review, adjust, and trust in real projects. Key improvements include:
(1) Added settings for bottom strengthening zones
(2) Improved input logic for design conditions
(3) Introduced virtual beams to make slab regions more regular
(4) Optimized batch editing of component dimensions
(5) Improved load modification and component editing after intelligent generation
(6) Enhanced modeling and analysis feedback, so users can better understand problems and respond faster

For structural engineers, efficiency is not only about producing a result. It is also about whether that result fits engineering practice, can be adjusted smoothly, connects with the modeling and analysis workflow, and helps save time in real design work.

That is the direction we will continue to pursue: making AI-structure Copilot more stable, more practical, more transparent, and more useful for structural engineers.

Thanks to everyone who has used the product, shared feedback, and helped us improve it through real engineering cases.

Our paper, “Intelligent design of shear wall layout based on diffusion models,” has been recognized by Wiley as a Top Cited Article 2025 in Computer-Aided Civil and Infrastructure Engineering.

What makes this recognition particularly meaningful is that the work has created an impact in both academia and engineering practice.
- Academically, the paper has received strong attention from the research community.
- Practically, it has also become one of the core models behind AI-structure Copilot (ai-structure.com), supporting the design of thousands of shear wall structures.

It is always encouraging to see research move beyond publication, not only contributing to scientific progress but also helping shape real-world engineering workflows.

Grateful to my co-authors, collaborators, and everyone involved in bringing this work from an academic idea to practical application.

AI-structure Copilot (https://ai-structure.com) has just reached 4,500 shear wall design cases.
It’s what we improved along the way. In recent iterations, we focused on making the product more practical for real engineering work:
(1) Added bottom strengthening zone settings for shear wall design
(2) Improved the input logic for design conditions
(3) Enabled automatic identification of structural seismic grade
(4) Refined linkage between related parameters to reduce manual back-and-forth
(5) Optimized the tool panel and interaction flow, making component edits, load adjustments, local revisions, and parameter checks smoother after intelligent generation
(6) Enhanced feedback during modeling and calculation, so users can understand issues more clearly and respond faster

For structural engineers, efficiency is not just about generating a result. It is also about how easily that result can be reviewed, adjusted, and trusted in actual projects.
That is the direction we have been working on: making AI-structure Copilot more aligned with engineering practice, more convenient to use, and more reliable in day-to-day design work.
Thanks to everyone who has been using the product and sharing feedback.
We will keep iterating — and keep making AI for structural design more practical, more stable, and more useful.