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Data validation

Data validation tools surface inconsistencies in a project before they cause analysis errors or noisy output. The most-used tool in this group is the Explosive Scrubber, which removes explosive records whose weights are all zero or empty.

Learning objectives

By the end of this lesson you should be able to:

  • Explain why an emptied explosive still affects an analysis
  • Run the Explosive Scrubber and review what it would remove
  • Recognize when a scan result points to a data-entry mistake rather than harmless leftover data

Explosive Scrubber​

When a trainee sets an explosive's weights to zero or clears them, the explosive record is not removed from the feature — only its values are emptied. The analysis engine still sees an explosive on that feature, runs the calculation against the empty record, and returns an unnecessary 0 ft required result. These empty records build up after edits, imports, and bulk operations and clutter analysis output.

The Explosive Scrubber finds every explosive whose weight attributes are all zero or empty and deletes the record from its feature, so the engine stops evaluating it.

For the click-by-click UI flow, see the how-to guide: Data validation.

Scan, then review​

Open the Data Validation panel from the Workspace menu and click Scan Features. The Scrubber lists each empty explosive with its Feature, hazard division (Explosive), Criteria, and Attributes count, with every row selected by default.

Teach trainees to read the list before removing. Because the Scrubber only lists explosives whose weights are all zero or empty, the entries are safe to delete. But an entry is also the only visible evidence that a value was cleared by mistake: if a record appears here for a facility that should carry a weight, that points to a data-entry error. Have the trainee open that feature and check its explosives before scrubbing.

When to scrub​

Run the Scrubber:

  • After bulk edits that may have zeroed weights
  • After importing a project from an external source
  • Before major deliverables, to keep analysis output clean
  • When stray 0 ft required results are cluttering an analysis

Do not blindly remove entries you did not expect — investigate the feature first.

Try it​

In a sandbox project:

  1. Add a feature and enter an explosive with a weight
  2. Edit that explosive and set its weight to zero (or clear it) — note the record is still on the feature
  3. Open Data Validation from the Workspace menu and click Scan Features
  4. Confirm the now-empty explosive appears in the results
  5. Leave it selected, click Remove Selected, and re-scan to confirm the list is clear