Building information model (BIM) data are digital and geometric-based data that are enriched thematically, semantically, and relationally, and are conceptually very similar to geographic information. In this paper, we propose both the use of the international standard ISO 19157 for the adequate formulation of the quality control for BIM datasets and a statistical approach based on a binomial/multinomial or hypergeometric (univariate/multivariate) model and a multiple hypothesis testing method. The use of ISO 19157 means that the definition of data quality units conforms to data quality elements and well-defined scopes, but also that the evaluation method and conformity levels use standardized measures. To achieve an accept/reject decision for quality control, a statistical model is needed. Statistical methods allow one to limit the risks of the parties (producer and user risks). In this way, several statistical models, based on proportions, are proposed and we illustrate how to apply several quality controls together (multiple hypothesis testing). All use cases, where the comparison of a BIM dataset versus reality is needed, are appropriate situations in which to apply this method in order to supply a general digital model of reality. An example of its application is developed to control an “as-built” BIM dataset where sampling is needed. This example refers to a simple residential building with four floors, composed of a basement garage, two commercial premises, four apartments, and an attic. The example is composed of six quality controls that are considered simultaneously. The controls are defined in a rigorous manner using ISO 19157, by means of categories, scopes, data quality elements, quality measures, compliance levels, etc. The example results in the rejection of the BIM dataset. The presented method is, therefore, adequate for controlling BIM datasets.
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