Ensuring the correct mapping of model elements to Industry Foundation Classes (IFC) classes is fundamental for the seamless exchange of information between Building Information Modeling (BIM) applications, and thus achieve true interoperability. This research explored the possibility of employing novelty detection, a machine learning approach, as a way to detect potential misclassifications that occur during current ad hoc and manual mapping practices. By training the algorithm to learn the geometry of BIM elements for a given IFC class, outliers are detected automatically. A framework for leveraging multiple BIM models and training individual one-class SVM's was formulated and tested on four IFC classes. Performance results demonstrate the classification models to be robust and unbiased. The algorithms developed thus can be leveraged to check the integrity of IFC data, a prerequisite for BIM-based quality control and code compliance. Highlights The correct mapping of BIM elements to IFC classes is critical for IFC based interoperability. A framework is formalized for applying novelty detection to automate the checking of misclassifications. One-class SVM's are trained and tested on two architectural and two infrastructure IFC classes. Performance metrics indicate robust and unbiased models with high accuracy and true negative rates. Novelty detection is a superior approach to outlier detection in identifying misclassifications of BIM to IFC associations.

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-Although Industry Foundation Classes (IFC) provide standards for exchanging BuildingInformation Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and compromise the interoperability of BIM models. This research explored the use of two machine learning techniques for identifying anomalies, namely outlier and novelty detection to determine the integrity of IFC classes to BIM entity mappings. Both approaches were tested on three BIM models, to test their accuracy in identifying misclassifications. Results showed that outlier detection, which uses Mahalanobis distances, had difficulties when several types of dissimilar elements existed in a single IFC class and conversely was not applicable for IFC classes with insufficient number of elements. Novelty detection, using one-class SVM, was trained a priori on elements with dissimilar geometry. By creating multiple inlier boundaries, novelty detection resolved the limitations encountered in the former approach, and consequently performed better in identifying outliers correctly.

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

:The Hedonic Pricing model is the predominant approach used today to model the effect of relevant factors on real estate prices. These factors include intrinsic elements of a property such as floor areas, number of rooms, and parking spaces. Also, The model also accounts for the impact of amenities or undesirable facilities of a property's value. In the latter case, euclidean distances are typically used as the parameter to represent the proximity and its impact on prices. However, in situations where multiple facilities exist, multi-colinearity may exist between these parameters, which can result in multi-regression models with erroneous coefficients. This research uses Variance Inflation Factors(VIF) and Ridge Regression to identify these errors and thus create more accurate and stable models. The techniques were applied to apartments in Guro-gu of Seoul, whose prices are impacted by subway stations as well as a public prison, a railway terminal and a digital complex. The VIF identified colinearity between variables representing the terminal and the digital complex as well as the latitudinal coordinates. The ridge regression showed the need to remove two of these variables. The case study demonstrated that the application of these techniques were critical in developing accurate and robust Hedonic Pricing models.