The use of Building Information Modelling (BIM) has gained considerable interest in new build projects. However, its use in existing assets has been limited to geometric models utilising Point Cloud Data (PCD) as the primary source of data. The inclusion of non-geometrical data from distributed sources in the geometric model to make it semantically rich has been fraught with considerable challenges. In this paper, an approach is proposed to provide a framework for generating semantically-rich parametric models for existing assets. While the geometric information like length, width, area, and volume can be extracted from a PCD, non-geometric data may need to be appended to this for generating genuinely semantically rich models. The Comma Separated Values (CSV) format is utilised to represent the data that can be extracted from PCDs. In addition, the non-geometric information derived from other sources is appended to the CSV file. Subsequently, the Resource Description Framework (RDF) data is generated from the data presented in the CSV files. RDF is a commonly used Semantic Web technology for storing, sharing, and reusing information on the Web. The RDF data is then used to create the IFC data model by translating RDF into IFC. The IFC file is used to generate 3D BIM by importing it into any IFC-compliant application. The proposed approach was validated on one part of the Edinburgh castle, a relatively complex historical building. The choice of building for validating the approach was driven by technical as well as pragmatic reasons. Technically, the approach will have proven its robustness if it could be shown to work for a complex rather than a relatively simple building. Pragmatically, the authors had access to data on Edinburgh Castle due to an ongoing partnership with the Historic Environment Scotland (HES). However, as a result of the validation process, it is suggested that the proposed approach should be applicable to any existing building.

Semantic-rich 3D parametric models, like Building Information Models (BIMs) are becoming the main information source during the entire lifespan of an asset. The use of BIM in existing buildings has been hampered by the challenges surrounding the limitations of existing technologies for developing retrofit models. Some progress has been recently made in generating non-parametric models from the Point Cloud Data (PCD). However, a proper fully developed parametric model is still some way away. In this paper, challenges are addressed by reviewing the stateof-the-art before presenting our approach. The aim of our approach is to apply the Semantic Web Technologies for generating parametric models using PCD as primary data. The Semantic Web as a set of standards and technologies is used for providing an appropriate framework for storing, sharing, and reusing the semantics of information on the web. Building elements are recognized in PCD, and the concept of Resource Description Framework (RDF) as a Semantic Web technology and a standard model for interchanging the data on the web is then used to markup detected elements. The RDF data is then standardized to Industry Foundation Classes (IFC) as an open standard building data model to generate the parametric model of the asset utilizing BIM software that supports IFC. Some parts of this ongoing research are performed manually, and the future work is to implement the process automatically. Primary results are quite promising and should be of interest to the modeling of all kinds of assets, in particular, Historical Building Information Modelling (HBIM).

Building Information Modelling is a well-known acronym in the construction industry. BIM process is more than modelling buildings, and it provides the opportunity to drive efficiency and effectiveness to the information management of build projects. Accordingly, Building Information Models (BIMs), typically known as semantic three-dimensional parametric models, are fast becoming the comprehensive information source in Architecture, Engineering & Construction (AEC), and Facility Management (FM). The use of BIM in existing buildings has been hampered by the challenges and limitations surrounding the available technologies. The most popular and commonly used approach for generating models is to manually generate 3D artefacts utilizing point measurements extracted from range-based technologies (typically 3D laser scanning). In the recent past, several studies have been carried out to make the retrofit BIM development process as effective and efficient as possible by developing different methods for mapping 3D models using Point Cloud Data (PCD) as the main source of information. However, an appropriate fully generated parametric model is still some way away. In this paper, we review the-state-of-the-art to address the research gap and challenges involved in generating parametric models before outlining the proposal of our approach. In this research, we employ Semantic Web technologies to capture parametric models. Elements are first recognized in PCD, and corresponding geometric information extracted from PCD are then tagged with Universally Unique Identifiers (UUIDs). Tags are then linked with the generated Resource Description Framework (RDF) data for each element. The core and challenging part of this research is the standardization process where RDF as a serialization is translated to Industry Foundation Classes (IFC) as a data model. The generated IFC format is then utilized to capture corresponding models. The primary results are very promising and should be of interest to the modelling of all kinds of building components, particularly historical building information modelling (HBIM).

Purpose Facilitating the information exchange and interoperability between stakeholders during the life-cycle of an asset can be one of the fundamental necessities for developing an enhanced information exchange framework. Such a framework can also improve the successful accomplishment of building projects. This paper aims to use Semantic Web technologies for facilitating information exchange within existing building projects. Design/methodology/approach In real-world building projects, the construction industry’s information supply chain may initiate from near scratch when new building projects are started resulting in diverse data structures represented in unstructured data sources, like Excel spreadsheets and documents. Large-scale data generated throughout a building's life-cycle requires exchanging and processing during an asset's Operation and Maintenance (O&M) phase. Building information modelling (BIM) processes and related technologies can address some of the challenges and limitations of information exchange and interoperability within new building projects. However, the use of BIM in existing and retrofit assets has been hampered by the challenges surrounding the limitations of existing technologies. Findings The aim of this paper is twofold. Firstly, it briefly outlines the framework previously developed for generating semantically enriched 3D retrofit models. Secondly, a framework is proposed focussing on facilitating the information exchange and interoperability for existing buildings. Semantic Web technologies and standards, such as Web Ontology Language and existing AEC domain ontologies are used to enhance and improve the proposed framework. Originality/value The proposed framework is evaluated by implementing an example application and the Resource Description Framework data produced by the previously developed framework. The proposed approach makes a valuable contribution to the asset/facilities management (AM/FM) domain. It should be of interest to various FM practices for existing assets, such as the building information/knowledge management for design, construction and O&M stages of an asset’s life-cycle.

Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules. However, semantic parsing is known to be hard and requires large amounts of training data. The complexity of creating such training data has led to research that focuses on small sub-tasks, such as shallow parsing or the extraction of a limited subset of rules. This study introduces a shallow parsing task for which training data is relatively cheap to create, with the aim of learning a lexicon for ACC. We annotate a small domain-specific dataset of 200 sentences, SPAR.txt 1 , and train a sequence tagger that achieves 79,93 F1-score on the test set. We then show through manual evaluation that the model identifies most (89,84%) defined terms in a set of building regulation documents, and that both contiguous and discontiguous Multi-Word Expressions (MWE) are discovered with reasonable accuracy (70,3%).