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This is a preliminary proof-of-concept, not a validated design tool. An LLM agent drives a COMPAS structural modelling API through an MCP server to build and edit graph-based building models from natural-language prompts. The useful signal for AI readers is the architecture: constraining an agent to explicit, domain-specific operations rather than generating unstructured mesh geometry. Whether it holds up on real projects, with engineering constraints and professional review, remains untested.
Can AI Co-Design Structures? An AI Agent for Early-Stage Structural Design
arXiv:2610.31113v1 Announce Type: new Abstract: Recent advances in large language models and agentic workflows offer a new paradigm to early-stage structural design translating design intent into structural models. Although text-to-3D approaches have demonstrated that natural language can be used to produce visual, mesh-based geometric models, structural design requires domain-specific geometric arrangements and semantically rich data types. This paper introduces a proof-of-concept workflow for prompt-to-structure generation using an AI agent connected to a COMPAS-based structural modelling API through a Model Context Protocol (MCP) server. Through controlled modelling operations, the agent creates and modifies a graph-based structural data model composed of explicit building elements. The resulting artifacts can be inspected, modified, serialised, and further connected to downstream AEC workflows. The approach is demonstrated through selected prompt-to-structure examples and by progressive and editable design sequences that resemble typical iterative structural design workflows. The results are preliminary, but demonstrate the possibility of combining natural language, agentic workflows, and domain-specific structural data structures as a first step toward AI-assisted structural co-design.
Title:Can AI Co-Design Structures? An AI Agent for Early-Stage Structural Design
Abstract:Recent advances in large language models and agentic workflows offer a new paradigm to early-stage structural design translating design intent into structural models. Although text-to-3D approaches have demonstrated that natural language can be used to produce visual, mesh-based geometric models, structural design requires domain-specific geometric arrangements and semantically rich data types. This paper introduces a proof-of-concept workflow for prompt-to-structure generation using an AI agent connected to a COMPAS-based structural modelling API through a Model Context Protocol (MCP) server. Through controlled modelling operations, the agent creates and modifies a graph-based structural data model composed of explicit building elements. The resulting artifacts can be inspected, modified, serialised, and further connected to downstream AEC workflows. The approach is demonstrated through selected prompt-to-structure examples and by progressive and editable design sequences that resemble typical iterative structural design workflows. The results are preliminary, but demonstrate the possibility of combining natural language, agentic workflows, and domain-specific structural data structures as a first step toward AI-assisted structural co-design.
Artificial Intelligence (cs.AI)
arXiv:2610.31113 [cs.AI]
(or arXiv:2610.31113v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.31113 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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