Quality Control of “As Built” BIM Datasets Using the ISO 19157 Framework and a Multiple Hypothesis Testing Method Based on Proportions
Metadatos
Afficher la notice complèteAuteur
Ariza-López, Francisco J.; Rodríguez Avi, José; Reinoso Gordo, Juan Francisco; Ariza López, Íñigo AntonioEditorial
MDPI
Materia
BIM datasets Quality control Hypothesis tests
Date
2019-12-10Referencia bibliográfica
Ariza-López, F. J., Rodríguez-Avi, J., Reinoso-Gordo, J. F., & Ariza-López, Í. A. (2019). Quality Control of “As Built” BIM Datasets Using the ISO 19157 Framework and a Multiple Hypothesis Testing Method Based on Proportions. ISPRS International Journal of Geo-Information, 8(12), 569.
Patrocinador
This work has been supported by grant CMT2015-68276-R from the Spanish Ministry of Economy and Competitiveness.Résumé
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.