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How an Australian Government Department Uses Plannerly for Model Checking

Queensland's Department of Transport and Main Roads uses Plannerly Verify to check supply-chain model data against clearly defined information requirements, giving the team more certainty that model deliverables contain the information they expect.

Brisbane, Queensland Department of Transport and Main Roads Model Checking + Information Requirements Explore Verify Read the Case Story TMR

Bryan McSweeney AFIEAust

Manager - Special Projects, Digital Systems · Queensland Department of Transport and Main Roads

Queensland government information requirements model checking

"It highlights the need for due diligence when pulling all of the pieces of the puzzle together to make sure there are no errors in the definition of our information requirements."

Bryan McSweeney, Department of Transport and Main Roads DefineInformation requirements VerifyModel data InvestigateExceptions Model checking in practice

Checking supply-chain models against owner information requirements

For a government transport team, model checking is not just about whether a model opens or whether geometry looks right. The model also needs the right information in the right places, connected to owner-defined requirements that can be checked consistently.

Plannerly gives TMR a workflow for turning information requirements into checks against the models produced by the supply chain. That creates a clearer path from requirement definition to verified deliverables, with exceptions that can be investigated before handover.

QA Model data checked against
information requirements. Owner information requirementsDefined Supply-chain model dataChecked Model deliverablesTraceable Without structure Manual review and assumptions With Plannerly Defined, checked, traceable

The goal is certainty that delivered model data matches the requirements that were agreed.

The Challenge: Making Model Checking Reliable and Repeatable

Government infrastructure teams depend on model information that can be trusted beyond the design authoring environment. It is not enough for a model to contain objects; the right properties need to be present, named consistently, and aligned with the owner's information requirements.

Plannerly gives TMR a structured way to define those information requirements and then use Verify to check model data against them. That means model checking can move from manual inspection and scattered assumptions to a clearer process: define the requirement, run the check, investigate the result, and improve the model data.

This workflow gives the team more certainty about deliverables created by the supply chain. When something does not validate, the result gives reviewers a focused place to investigate instead of treating the whole model check as a vague pass-or-fail exercise.

A Detailed Example: A Difficult-to-Spot Model Data Mismatch

One useful example came from a Noise Fence Design review. TMR used Plannerly Verify to check requirements including "Unique Object Code" and "Road Section ID." One requirement passed and one failed, giving the team a precise signal about where the model information needed closer investigation.

50% Example check result:
one pass, one fail. Unique Object CodeValidated Road Section IDNot validated Expected Road Section ID In the model Road Section lD

In many fonts, capital I and lowercase l are almost indistinguishable.

During the support session, the team removed the "Road Section ID" requirement and Verify showed 100% compliance for the remaining requirement. Bryan McSweeney then recreated the requirement and simplified the verification rule to "any value (excluding empty)" to check whether the property existed with a value.

The result still came back at 50%. Plannerly's development team investigated the failed check and found the cause. It was not a bug. The model property contained a lowercase "l" where an "I" was expected in "ID." Visually, the two characters are almost identical in many interfaces, which made the issue extremely difficult to spot manually.

"What an interesting issue and well spotted by the team."

Bryan McSweeney, Department of Transport and Main Roads

That small mismatch mattered because model checking is exact by design. If the information requirement asks for Road Section ID but the model property is named Road Section lD, the value may exist somewhere in the model, but it is not attached to the requirement that has been defined.

Why Exact Checking Builds Trust

Information requirements only create accountability when the naming, rule, object, responsibility, and evidence all line up. If a verification system silently accepts near-matches, teams can get a green result while their underlying data remains inconsistent. Plannerly made the mismatch visible so the team could fix the actual source of the failed check.

Why This Matters for Government Model Checking

This detailed example shows the larger value of Plannerly for public owners. Government teams need a repeatable way to check whether delivered model information matches the requirements they have defined, especially when many people, systems, and organizations contribute to the same asset data.

For owners and public agencies, this is especially important. Information requirements often pass through many hands: client teams, designers, BIM managers, model authors, reviewers, and delivery partners. Plannerly helps make those requirements visible, testable, and traceable through model checking.

• Model checking needs structured requirements. Property names, object categories, rule logic, and required values need to be defined before they can be checked.
• Verify makes model data testable. Teams can move from visual inspection to evidence-based checks against the information requirements.
• Failed checks become diagnostic signals. Exceptions show the team where to investigate instead of hiding problems in a broad pass/fail summary.
• Subtle data issues are hard to find manually. Automated checking helps reviewers focus on the model data that needs attention.
• Traceability builds confidence. Teams can explain why a check failed, fix the source, and improve future requirement definitions.

The Outcome: More Confidence in the Model Checking Workflow

The outcome is a stronger model checking workflow: TMR can use Plannerly to connect owner-defined requirements to supply-chain model data, run checks consistently, and investigate exceptions with a clear trail back to the requirement.

That matters because model information is only useful when teams can trust it. Plannerly helps make information requirements more than static text. They become testable commitments that can be checked before small data issues become downstream handover problems.

The practical lesson for model checking

Use Plannerly to define model information requirements clearly, then use Verify to test whether the delivered model data actually meets them. The more precise the requirement, the more useful the check.

Learn About Verify

What the Workflow Provides

• Define model information requirements in a way that can be checked, not just read.
• Use Verify results to focus investigation on the exact requirement, object, or property that needs attention.
• Give reviewers a repeatable way to check model data created by the supply chain.
• Improve confidence that model deliverables contain the information expected by the owner.
• Capture corrected requirement mappings so future model checks become clearer and more reliable.

About the Department of Transport and Main Roads

The Queensland Department of Transport and Main Roads manages transport infrastructure and delivery across Queensland, Australia. In this case study, the team uses Plannerly Verify to check model information created by the supply chain against defined owner requirements.

This case was shared with permission from Bryan McSweeney, Manager - Special Projects, Digital Systems, Engineering & Technology, Infrastructure Management & Delivery.

Ready to Make Information Requirements Easier to Verify?

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