Introduction
In the realm of Building Information Modelling (BIM), maintaining the integrity and compliance of models is not merely a best practice — it is a fundamental requirement for project success. As projects grow in scale and complexity, involving hundreds of model files contributed by architects, structural engineers, MEP consultants, and subcontractors, the margin for undetected errors shrinks considerably. A single misclassified element, an incorrectly assigned fire rating, or a missing parameter can trigger a chain reaction of coordination failures that only become visible — and expensive — during construction.
Automating model health checks provides a systematic answer to this challenge. By configuring Solibri Model Checker to run predefined rule sets against every new model upload, organisations can shift quality assurance from a periodic manual activity to a continuous, embedded process. This article explores how to implement that approach, what it means in practice on large-scale projects, and why the investment in automation pays dividends far beyond simple error reduction.
The Importance of Model Health Checking
In the construction and architecture industries, the health of a BIM model directly determines the reliability of every downstream process that depends on it — quantity take-offs, clash detection, scheduling, cost estimation, and facilities management handover. A model in poor health introduces noise at every stage: inaccurate quantities flow into cost plans, misaligned geometry defeats clash detection, and incomplete property data corrupts COBie exports.
Traditional manual checking methods carry two significant weaknesses. First, they are time-consuming. A thorough review of a large architectural model can take a skilled BIM coordinator several hours, and that effort must be repeated every time the model is updated. Second, and perhaps more critically, they are inconsistent. Human reviewers, even experienced ones, apply rules differently on different days, under different time pressures. A rule that is diligently enforced at the start of a project may receive less attention during a busy design development phase.
Automation addresses both weaknesses simultaneously. It runs the same rule sets with exactly the same rigour every single time, and it does so in the background, without consuming coordinator time. The result is a consistent baseline of model quality that the entire project team can rely upon throughout the project lifecycle.
Why Solibri?
Solibri Model Checker has earned its position as one of the leading tools for BIM quality assurance through a combination of technical depth and practical flexibility. At its core, Solibri operates on IFC (Industry Foundation Classes) data, making it interoperable across all major authoring platforms — Revit, ArchiCAD, Vectorworks, Tekla, and others. This platform-neutral approach is particularly valuable on projects where multiple disciplines work in different authoring environments.
The rule framework within Solibri is genuinely versatile. Out-of-the-box rule sets cover a wide range of checks, from straightforward data validation (verifying that every room has a name and area assigned) through to complex compliance verification against standards such as COBie, UK BIM Level 2 requirements, or local building codes. Beyond the built-in library, Solibri allows organisations to author entirely bespoke rules using its rule-creation interface, enabling the enforcement of project-specific or client-specific requirements that no off-the-shelf tool would anticipate.
Solibri also produces structured, traceable reports. Each rule violation is logged with the affected component's GUID, its location in the model, and a description of the issue. This traceability is essential when reports need to feed back into issue-tracking workflows or when audit trails are required for contractual compliance.
Understanding Rule Sets and Their Scope
Before implementing any automation, it is worth understanding the taxonomy of rule sets and choosing them deliberately. In Solibri, rules are grouped into rule sets, and rule sets are assembled into checking profiles. The most effective automated health-check systems use a tiered approach:
Structural integrity rules verify that the model's geometry is valid — no overlapping solids, no inverted normals, no unbounded spaces. These rules catch authoring errors that would corrupt downstream processes.
Data completeness rules confirm that required parameters are populated. For a typical architectural model, this might include checks that every door has a fire rating, every space has an occupancy classification, and every structural element has a material assignment.
Standards compliance rules validate adherence to a project's BIM Execution Plan (BEP), national standards, or client requirements. On UK public-sector projects, for instance, this typically includes COBie validation and verification against the employer's information requirements.
Coordination rules look for spatial conflicts between elements — not only hard clashes (two solid objects occupying the same space) but also clearance violations, accessibility corridor widths, and minimum structural clearances. These rules are most powerful when run across federated models that combine contributions from multiple disciplines.
Selecting the right combination of rule sets for automated upload-triggered checks requires dialogue with the project team at the outset. Including every available rule creates noise; a report with thousands of low-priority issues trains reviewers to ignore it. The goal is a focused set of rules that catches genuinely important problems while producing a report short enough to act upon.
Automating Solibri Rule Checks
Automating Solibri rule checks involves configuring a workflow where every new model upload triggers an automatic review against the project's approved rule sets. The following steps outline how this is achieved in practice.
Step 1: Define Rule Sets
Before any automation is configured, the project team must agree on the rule sets that will govern automated checks. This is not a technical task — it is a project management task. The BIM manager, in consultation with the employer's information manager and key discipline leads, should review the project's BEP and employer's information requirements to identify the non-negotiable quality gates. These become the foundation of the automated checking profile.
Solibri allows rule sets to be saved as named profiles and shared across the project environment, ensuring that every automated check uses exactly the same configuration. Version-controlling these profiles alongside the BEP documentation is good practice, providing an audit trail of how checking criteria evolved over the course of the project.
Step 2: Integrate Automation Tools
The trigger mechanism — the component that detects a new model upload and initiates the Solibri check — typically sits within a Common Data Environment (CDE) or an automation middleware layer. Platforms such as Autodesk Construction Cloud (formerly BIM 360), ProjectWise, and BIMcollab can be configured with webhook-based or API-based triggers. When a new IFC file is uploaded to a designated folder or achieves a certain workflow status, the trigger fires and passes the file to the checking process.
For organisations using custom automation pipelines, tools such as Dynamo scripts, Python-based automation libraries, or dedicated BIM automation platforms can orchestrate the same workflow. The key is that the trigger is event-driven rather than scheduled — the check fires because a new model arrived, not because a clock reached a certain time.
Step 3: Running the Checks
Once the trigger fires, the automation workflow opens Solibri in headless mode (via the Solibri API or command-line interface), loads the uploaded model, applies the predefined checking profile, and runs the full suite of rules. Depending on model size and rule complexity, this process typically completes within minutes for individual discipline models and within fifteen to thirty minutes for large federated models.
The resulting report is generated in Solibri's native format or exported as a structured BCF (BIM Collaboration Format) file, which can be consumed directly by issue-tracking tools. The BCF format carries camera viewpoints and component references, allowing reviewers to navigate immediately to the location of each issue without manual searching.
Step 4: Review and Rectify
Post-validation, the generated report is distributed automatically to the relevant model author and their BIM coordinator. The most effective implementations integrate report delivery with an issue-management platform such as BIMcollab or Autodesk Issues, so that each rule violation becomes a tracked issue with an assigned owner and a due date.
A well-designed automated health-check workflow includes a status gate: models that fail critical rules are held in a "checking" or "rejected" status within the CDE until the issues are resolved and the model re-uploaded. This prevents non-compliant models from progressing to shared or published status, which would allow the errors to propagate to other disciplines.
Real-World Implementation
Consider a construction firm managing multiple large-scale mixed-use developments simultaneously. Prior to implementing automated Solibri checks, the BIM team ran manual model reviews on a fortnightly basis — which meant that errors introduced on day one of a fortnight could remain undetected for up to two weeks, during which other disciplines may have federated against the faulty model. After implementing upload-triggered automation, the firm reported a 30 per cent reduction in design errors at the pre-construction phase. More significantly, the average time between error introduction and detection fell from nine days to under four hours.
On a large infrastructure project in the UK, automated Solibri checks were configured to validate IFC exports from both civil and structural authoring tools against a COBie-aligned data schema. The automation caught 847 non-compliant elements across six model uploads in the first month alone — issues that would otherwise have been discovered during a costly COBie handover exercise at project completion.
These outcomes are not exceptional; they are representative of what is achievable when automation replaces periodic manual checking with continuous quality assurance.
Structuring Notifications and Reporting Dashboards
An often-overlooked aspect of automated model health checking is the communication layer — how findings reach the people who need to act on them. A comprehensive notification strategy considers three audiences.
Model authors need immediate, actionable notifications when their upload fails a check. These notifications should include a direct link to the BCF report and, wherever possible, a camera view of the first critical issue so the author can orient themselves quickly without opening the full report.
BIM managers and project coordinators benefit from a dashboard view that shows the health trend of each discipline model over time — not just the current status, but whether quality is improving or deteriorating across successive uploads. Solibri's reporting capabilities, combined with a lightweight data visualisation layer, can provide this at-a-glance picture.
Employers and client-side information managers require periodic summary reports that demonstrate compliance with the BIM execution plan and employer's information requirements. These can be generated automatically at defined project milestones, drawing on the accumulated log of automated check results.
Building this communication layer from the outset, rather than retrofitting it after problems have already occurred, is what separates a mature automation programme from a tool that runs but nobody acts on.
Common Pitfalls and How to Avoid Them
Automated model checking workflows fail for predictable reasons. Understanding these pitfalls reduces the time to a stable, trusted implementation.
Rule set overload is the most common issue. When too many rules are active, reports become overwhelming, and teams learn to dismiss them. Start with a focused set of high-priority rules and expand incrementally as the team builds confidence in the process.
IFC export configuration is frequently misaligned with checking expectations. Solibri checks IFC data, not native authoring tool data directly. If the IFC export settings in Revit or ArchiCAD are not correctly configured, required parameters may not be exported, causing false positives in the automated checks. Investing time in IFC export templates at the start of a project pays considerable dividends.
Lack of ownership undermines even well-designed workflows. Every rule violation needs a named individual responsible for resolution. Without clear ownership embedded in the workflow, reports accumulate without action.
Benefits of Automation
The cumulative effect of well-implemented automated model health checking is measurable across several dimensions.
Efficiency is the most visible gain. Automating checks frees BIM coordinators from repetitive review work, allowing them to focus on higher-value activities such as design coordination, clash resolution, and client communication.
Accuracy improves because the same rules are applied identically on every check run. There is no variation based on reviewer fatigue, distraction, or interpretation.
Compliance becomes a continuous state rather than a periodic achievement. With every upload checked against the project's standards, there is never a moment when the model's compliance status is unknown.
Cost-effectiveness is perhaps the most compelling argument for investment. Errors caught at model upload cost a fraction of the same errors caught at construction. The earlier in the project lifecycle a problem is identified, the less it costs to correct — and automated checking pushes detection to the earliest possible moment.
Conclusion
Automating model health checks by implementing Solibri rule runs on every new model upload represents a genuine shift in how BIM quality assurance operates — from reactive and periodic to proactive and continuous. The combination of a well-chosen rule set, a reliable trigger mechanism, structured reporting, and clear issue ownership creates a quality gateway that operates around the clock without consuming coordinator capacity.
As BIM mandates become more demanding and employer information requirements more prescriptive, the ability to demonstrate continuous, documented compliance will increasingly differentiate capable project teams from those who are simply producing models. The firms that invest in this infrastructure now will be better positioned to meet those demands confidently.
At Adyantrix, automated model validation sits at the heart of our BIM service delivery. Our team designs and implements Solibri-based checking workflows tailored to each project's BEP and information requirements, integrating seamlessly with existing CDE environments and issue-management platforms. Whether you are establishing a quality framework for a new project or seeking to strengthen controls on an existing one, we bring the technical depth and practical experience to build a process that your team will trust and use.
Speak with our BIM Consulting team at Adyantrix to find out how we can support your next project.
