International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

Automated dimensional compliance assessment with incomplete point cloud Czerniawski, Thomas; Nahangi, Mohammad; Walbridge, Scott; Haas, Carl T.


Dimensional compliance assessment of prefabricated assemblies is a critical part of mitigating rework on heavy industrial construction projects. As assemblies become more complex, manual direct contact metrology becomes ineffective at detecting fabrication error and so automated alternatives that offer objective, fast, and continuous data collection must be explored. Nahangi and Haas (2014) developed an automated method for assessing pipe spools through an algorithm that compares as-built laser scans to 3D design files. The tool is capable of automatic and continual monitoring of prefabricated assemblies throughout their lifecycle and enables timely detection and quantification of dimensional non-compliance. In the original publication, the tool was validated using ideal input data. In this paper, the tool is tested for robustness when processing incomplete point cloud input data. Non-ideal input data is a risk associated with unfavorable conditions in the fabrication environment such as random assembly occlusions causing blind spots in sensing setup, budgetary constraints limiting the purchase of sensing equipment/viewpoints, and random hardware or software failures resulting in corrupt data. The tool was found to reliably detect dimensional non-compliance so long as the non-compliance indicator (pipe spool feature distinguishing the non-compliant state from the design state) was not fully occluded. Accuracy of non-compliance quantification was predominantly high, however, loosely proportional to the input point cloud’s coverage of the assembly’s surface area.

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