AI for Structural Engineering: Documentation and Review

AI is useful in structural engineering work for tasks that involve documentation, specification review, code research, and report drafting. These tasks are time-consuming and detail-intensive, and AI can handle the initial passes faster than a person can. What AI cannot do is replace the engineering judgment that determines whether a structural design is safe and adequate. That judgment requires a licensed engineer who understands the project, the loads, the materials, and the applicable standards. Tools like fenbs help structural engineering teams keep approved decisions and attributed changes visible to every connected assistant, so the record behind design choices is always available.

Structural engineering carries direct public safety implications. A structural failure can injure or kill people. The liability framework exists precisely because professionals have to stand behind their designs. That liability cannot be transferred to an AI tool, and engineering output produced by AI without engineer review is not a design — it is a draft that requires professional evaluation.

Documentation tasks AI handles well

Structural engineers spend significant time on documentation: calculation documentation, specification writing, drawing notes, and design basis reports. AI can help with each of these once the engineering decisions behind them have been made.

• Calculation documentation: once the engineer has performed the calculations, AI can help organize and narrate the calculation package for clarity and completeness.
• Specification drafting: given a project type and material requirements, AI can draft a starting specification section for the engineer to review and revise.
• Drawing notes: AI can help draft standard structural notes from a list of requirements, saving time on repetitive text.
• Design basis reports: AI can help draft sections describing the structural system, code references, and design criteria once the engineer has defined them.
• Review letters: AI can help organize review comments into a systematic letter format.

In each case, the engineer provides the engineering content — the calculations, the criteria, the code citations — and AI helps with the organization and drafting. The engineer reviews the output before it leaves the office.

Code research and standard navigation

Structural engineering requires working across multiple codes and standards: the International Building Code, ASCE 7 for loads, ACI 318 for concrete, AISC 360 for steel, NDS for wood, and state and local amendments. Finding the right provision in the right edition of the right code is time-consuming.

AI can help with initial code research: finding relevant sections, summarizing what a provision requires, and identifying related sections to check. This is useful for getting oriented on a code provision or for preparing a list of requirements to verify.

The critical discipline is confirming every code reference against the actual published standard before acting on it. AI can misquote section numbers, summarize provisions inaccurately, or reference an older edition of a code. Any code provision that will appear in a calculation or specification must be verified against the actual document. The American Institute of Steel Construction publishes current AISC standards at aisc.org↗ (opens in a new tab); the American Concrete Institute publishes ACI standards at concrete.org↗ (opens in a new tab).

Specification and submittal review

Reviewing contractor submittals and shop drawings is a significant part of structural engineering practice during construction. AI can help organize a list of review comments, check that a submittal includes all required information, and identify whether claimed code compliance matches the specified requirements.

Submittal review still requires an engineer. The engineer must determine whether the submitted design meets the intent of the contract documents — a judgment that requires understanding the structural system and the project. AI can help prepare the review checklist and draft standard comments; it cannot determine whether a proposed modification is structurally equivalent to what was specified.

Peer review and interdisciplinary coordination

Structural engineering projects involve coordination with architects, mechanical engineers, civil engineers, and contractors. This coordination generates significant documentation. AI can help organize interdisciplinary review comments, track open items, and draft coordination responses — work that is administrative in character but critical to project execution.

Engineering peer review — where one engineer checks another's design — is a different matter. Peer review requires an engineer who understands the design well enough to identify errors in judgment, not just formatting. AI can help a reviewer organize their comments or prepare a peer review checklist, but the technical review is performed by a person.

Keeping the change record

Structural engineering projects generate design changes throughout their lifecycle. A change to a column size, a revision to a connection design, an update to the loading criteria — each of these needs to be documented with what changed, why it changed, and who approved it. This record matters both for the current project and for any future work on the structure.

When an AI assistant helps draft documentation for a design change, the documentation needs to be tied to the engineer's decision — who made the judgment, on what basis, and at what point in the project. Documentation that does not name the responsible engineer is documentation that does not support accountability.

How to do this with fenbs

1. Create a free account. Add a project for the structural engineering engagement.
2. Record the design basis decisions as Decisions and rules: the structural system, the applicable code editions, the design criteria, and any agreed interpretations.
3. Use tasks to track open coordination items, outstanding submittals, and review comments that need resolution.
4. When a design change is made, record what changed, the engineering basis for the change, and who approved it.
5. Connect your AI tools at /integrations. Each session reads the current project context — approved decisions, open tasks, and the change record — before starting documentation work.