AI and Roof Truss Analysis: A Junior Engineer’s Use Case
Articles
AI and Roof Truss Analysis: A Junior Engineer’s Use Case
August 4, 2026
Tim Murray, EIT, Engineer III, Weston & Sampson
As artificial intelligence becomes increasingly accessible within the engineering profession, identifying meaningful applications remains critical. This article presents a practical use case from a junior structural engineer who applied AI-driven automation to support the design of a prefabricated roof truss system. The project involved multiple finite element analysis models that generated large volumes of repetitive data requiring aggregation and review after each design iteration.
By using an AI tool to develop custom macros for data processing and reporting, the engineer reduced a task that traditionally required several hours of manual effort to just a few minutes. The resulting efficiency gains improved visibility into system-wide structural behavior and accelerated the design process. However, not all AI applications proved successful, and attempts to apply AI to more interpretive engineering tasks yielded limited value.
The experience highlights both the opportunities and limitations of AI in structural engineering. While automation can effectively reduce repetitive administrative work and free engineers to focus on higher-value analysis, it cannot replace engineering judgment, technical understanding, or professional development. As AI capabilities continue to expand, engineers must carefully balance productivity gains with the need to develop core competencies and maintain a deep understanding of the work they perform.
Grunt work is a fair focal point for artificial intelligence (AI) within structural engineering. However, junior engineers must rightly identify the grunt work in question before rushing in with their large language model (LLM) of choice. One young structural engineer found a worthwhile case for the use of AI in the design of a prefabricated roof truss system for a large municipal building.
The roof system itself consisted of 14 unique steel trusses, each truss with a dedicated finite elements (FEA) model that required significant post-analysis data processing after each adjustment and design iteration.
To design a prefabricated roof truss system, one young structural engineer found a worthwhile case for the use of AI that showed both the strengths and limitations of self-developed tools.
If undertaken manually from start to finish, the data aggregation and summarization steps would have required about three hours of direct labor per round of roof system analysis. The project’s complexity made delegating the task to a junior colleague impractical, but as the task’s true scale, simplicity, and repetitive nature grew clearer, the task shone ripe for automation.
The engineer subsequently used an available AI engine to develop three macros for embedment within the master workbook. Once validated and implemented – an eight to ten-hour endeavor – the virtual tool bundle was ready for operation. A few clicks triggered each script in turn, looping through the individual FEA exports, importing the data into the centralized Excel file, and organizing the information as programmed.
On the next analysis cycle though, what would have taken nearly three hours took less than two minutes, allowing the engineer to visualize truss-to-truss interactions and make subsequent structural adjustments. After several more cycles, the roof design was complete, saving between one and two dozen hours.
While the stab at automation described above was a net time-saver, additional hours of prompts and attempts to interpret data with AI however proved ineffective and were abandoned. The experience proved instructive to the junior engineer: in the face of day-long copy/paste agony, watchfulness is needed to ensure that AI tool-building doesn’t become the same monotonous grunting but in a new octave.
This particular engineer chose to finish the remaining portions of work on the project manually using conventional building information modeling (BIM) software. He detailed truss connections by hand and used no AI agents in email correspondence between junior and senior structural engineers – all to the junior engineer’s benefit.
As formal integrations between common BIM programs and popular AI products grow in availability, lower-level civil/structural engineers should stay on the lookout for AI use cases that improve their skill set and add value to their team without short-changing their competence or atrophying their professional growth. Machine learning is no shortcut to engineering judgement or comprehension.
Still, now more than ever, it is insufficient to simply be the guy who draws the lines on the page. Grunt work ought not be synonymous with unenjoyable work, as it’s often through early toil that entry level engineers begin gaining their bearings within their field.
Tim Murray, EIT, is an Engineer III with Weston & Sampson in their Portsmouth, New Hampshire office.
