IN Brief:
- New software automatically checks structural connections before components enter manufacture.
- A four-hour checking task was reduced to approximately ten minutes.
- Testing used a five-storey modular residential model containing more than 18,000 connections.
Birmingham City University and HadleyFRAME have developed automation tools that reduce the time required to complete a structural-model check from around four hours to ten minutes.
The software examines thousands of connections before components enter manufacture, identifying missing or unexpected details and reducing the volume of repetitive manual checking undertaken by engineers and draughting teams.
Development and validation used a five-storey modular residential building in Derbyshire containing more than 18,000 connections. By testing the tools against a live HadleyFRAME workflow rather than a simplified demonstration model, the project team was able to measure performance across a realistic volume of structural data.
One application automates the placement and checking of custom parameters across structural components. A batch of five model copies that previously took approximately 15 minutes to prepare could be processed in around 12 minutes, producing a 20% saving on that stage.
The second tool scans the model more extensively to identify missing connections and details that do not conform to expected rules. A process previously requiring about four hours of manual work was reduced to approximately ten minutes, representing a 96% reduction in checking time.
Funding came through a Business and Innovation Support Sprint involving the West Midlands Advanced Construction Cluster and the West Midlands Combined Authority. The programme was established to transfer academic and digital capability into practical construction-manufacturing processes.
HadleyFRAME’s light-gauge steel systems depend on repeatable connections and accurate digital information because manufacturing decisions flow directly from coordinated models. Errors that pass into production can affect component fit, assembly sequence, transport, site installation, and the structural performance of completed modules or panels.
Applying automation before fabrication moves quality control to a point where discrepancies remain relatively inexpensive to correct. Amending a parameter or missing connection in the model is considerably faster than stopping a manufacturing line, reworking completed components, or resolving clashes during assembly.
The development sits alongside a broader increase in construction robotics and automated production. On a Frankfurt office project, for example, robots have been drilling coordinated service openings from digital building information, extending automated control from model verification into physical execution.
Structural checking provides a more controlled starting point because it deals with defined rules and repeatable data. Software can compare model content against expected conditions at a speed that would be impractical for a person, while engineers retain responsibility for setting the rules, reviewing exceptions, and approving the design.
That division of responsibility remains essential. Automated checking cannot replace engineering judgement where unusual geometry, load paths, temporary conditions, fire performance, tolerances, or interfaces fall outside standardised arrangements.
A tool will apply the criteria it has been given with consistency, including criteria that are incomplete or incorrectly configured. Governance must therefore cover validation, permissions, rule changes, version control, software updates, and evidence showing who reviewed the output.
Time released from repetitive checks can allow technical staff to concentrate on exceptions and higher-risk decisions. Used carelessly, however, the same efficiency could encourage organisations to reduce scrutiny or assume that a clean automated report represents complete technical assurance.
The reliability of source data creates a parallel constraint. If model objects are incorrectly classified, duplicated, disconnected, or created outside the expected naming structure, the checking process may generate false warnings or fail to detect a genuine issue.
Consistent modelling standards are consequently as important as the checking algorithm. Libraries, naming conventions, parameter structures, templates, and approval routes must remain controlled across different designers and project stages.
Manufacturers can gain further value when verified information connects to production planning and quality control. A coordinated model can support material scheduling, machine instructions, inspection records, packaging, delivery sequence, and site assembly, creating a continuous information chain from design to installation.
That chain becomes increasingly valuable as off-site and platform-based construction expand. Greater repetition increases the benefit of automation, but it also increases the number of components affected when a standard detail is wrong.
Early detection therefore supports productivity and risk control simultaneously. A single incorrect connection rule repeated across hundreds of panels can create a much larger failure than an isolated drafting error on a traditionally delivered building.
The Derbyshire test shows that significant time can be removed from a real checking process without waiting for fully autonomous design. Further assessment will need to establish whether faster checks reduce errors during manufacture and installation, rather than simply shortening the time required to review a model.
