30. Asset Hierarchy (updated)

Updated August 9, 2019 (Original post October 23, 2014): To enable the definition of physical deliverables (Physical Assets) in a flexible manner, the concept of Asset Unit is needed (to be covered in a separate post). Asset Units combines: (i) a variable Asset Scale (e.g. a component, an assembly, a building, or a whole city) derived from the Asset Hierarchy (see below), with (ii) an Asset List defined by the Demand Entity (Employer, Client, Appointing Party) within an Information Cycle (a list of assets to be designed delivered, and/or utilised), and (iii) an Asset Attributes derived from the Conceptual (BIM) Ontology (e.g. Asset Function, Asset Location, and Asset Cost).

image from www.bimframework.info
Asset Hierarchy – v2.0, 2018 (full size image) - Link to older version (v1, 2011) 

 

The Asset Hierarchy organises Physical Deliverables/Assets by relative scale by combining taxonomies developed by OSCRE[1] and Transport for North South Wales[2]. The diagram does not differentiate between assets – Buildings as a sample scale - by their size, monetary value, location, or designated function[3] - e.g. Cultural, Transportation, or Recreation[4] but by their granularity relative to each other. The diagram includes a numbering system (scale 10-70) to establish an Asset’s relative position on the scale. The diagram also highlights how:

  • The Assets Scale bridges the semantic and informational divide between three complementary industries and their respective information domains. At the upper-end of the scale, the Geospatial Industry and its Geographic Information System (GIS) domain overlaps with the Construction Industry and its BIM domain; while at the lower-end of the scale, it overlaps with the Manufacturing Industry and its Product Lifecycle Management (PLM) domain[5].
  • An Asset can be a member of three Asset Clusters:
  • An Asset Portfolio (scale 300) is a clustering of assets for the purposes of strategic investment, operation, and management. Asset Portfolios may also be referred to as ‘asset groupings’ or ‘asset systems’[6];
  • A Modular Assembly (scale 500) is a functional clustering of assets for the purposes of Design for Manufacturing and Assembly (DfMA) and similar offsite methods. Examples of Modular Assemblies and Sub-Assemblies include prefabricated houses, pre-assembled mechanical risers, and pre-constructed wall sections[7]. Treated as a single Asset Unit, a Modular Assembly can be (a) first developed and tested in digital space; (b) prototyped, manufactured, constructed, and/or pre-assembled offsite; (c) packaged and readied for transportation; (d) transported for storage on site or delivered Just-In-Time (JIT); and (e) unpacked, erected, and/or assembled; and
  • A Temporary Package (scale 700) is a clustering of assets for the purposes of transportation and storage logistics. Examples of Temporary Packages include standardised shipping containers and logistical package[8]. Temporary Packages also include bundled materials (e.g. a sandbag, load, or palette).
  • Systems are treated as dynamic sub-scales that – depending on the Physical Asset being measured – may be more/less granular than other assets. Systems span the entire hierarchy (from scale 15 to 65) and can flexibly connect information deliverables and requirements across the BIM, GIS, and PLM domains.

 

Using a variable Asset Scale allows the dynamic assignment of less granular assets at the start of an information cycle. More information about how Asset Scales and Asset Units are used in the future.

 


 

[1] Fuhrman, A. (2007). The Hybrid Taxonomy Real Estate Focus, The Open Standards Consortium for Real Estate (OSCRE).

[2] TfNSW. (2015). Systems Engineering, Transport for NSW, New South Wales Government (T MU AM 06006 GU). Retrieved from https://www.transport.nsw.gov.au/system/files/media/asa_standards/2017/t-mu-am-06006-st-v2.0.pdf Last accessed Aug 9, 2019

[3] Assets can be classified under many concepts which should not be combined in a single taxonomy. Information Actors will need to identify the number of classifications needed to manage their asset information. For example, an Information Actor may use three primary classifications to identify a Physical Asset: Asset Scale (e,g. Lighting System), Asset Function (Lighting), and Asset Location (e.g. In Room 391 or along Highway A5, Exit Ramp 17).

[4] OmniClass. (2013). OmniClass | Construction Classification System, Retrieved from http://www.omniclass.org/; and NBS. (2015). UniClass 2015 (1.7 ed.). Retrieved from https://buildig.com/uniclass-2015/ Last accessed Aug 9, 2019.

[5] Overlaps between the BIM and GIS/PLM domains are well-covered in: Song, Y., Wang, X., Tan, Y., Wu, P., Sutrisna, M., Cheng, J. C., & Hampson, K. (2017). Trends and Opportunities of BIM-GIS Integration in the Architecture, Engineering and Construction Industry: A Review from a Spatio-Temporal Statistical Perspective. ISPRS International Journal of Geo-Information, 6(12), 397; and in Jupp, J. R., & Singh, V. (2014). Similar concepts, distinct solutions, common problems: learning from PLM and BIM deployment. Paper presented at the IFIP International Conference on Product Lifecycle Management.

[6] ISO. (2014). ISO 55000:2014 Asset management - Overview, principles and terminology. Retrieved from https://www.iso.org/standard/55088.html Last accessed Aug 9, 2019

[7] BWTL. (2018). Platforms Bridging the gap between construction + manufacturing, Brydon Wood Technology Limited (BWTL) for the Centre for Digital Built Britain (CDBB), University of Cambridge. Retrieved from https://www.cdbb.cam.ac.uk/system/files/documents/2018Platforms_Bridgingthegapbetweenconstructionandmanufacturing.pdf Last accessed Aug 9, 2019

[8] Saghir, M. (2004). The concept of packaging logistics. Paper presented at the Proceedings of the Fifteenth Annual POMS Conference, Cancun, April.


44. Information Management Cycle

Information-Management-Cycle
 

Information Management Cycle v0.5 (Full-size Image - Older version)

Updated Jan 21, 2018: This high-level conceptual model describes the Information Management Cycles that Structured Project Information pass through [1]. Each Cycle includes three Information Management States, separated by varied Information Management Activities conducted by specialised Information Management Actors:

Information Management States

The three states describe how Structured Project Information can be experienced as either:

[1] Information Requirements: project specifications, protocols or similar that identify what needs to be generated by project stakeholders. Information Requirements can be represented as a set of Document Uses, Model Uses and Data Uses.

[2] Digital Deliverables: digital simulations of physical objects and how/when they’ll be constructed or fabricated. Digital Deliverables can either be documents (in digital format -e.g. CAD drawings or a PDF furniture lists), models and/or data sets.

[3] Physical Assets: information embodied within real world objects similar to whole facilities, a building, mechanical system, heating unit, or a single pump.

Information Management Activities

The transitions between these Information Management States are represented as either forward or reverse activities. Forward Cycle Activities refer to the actions executed to cross from one state to the next; while Reverse Cycle Activities refer to the measurements made to examine one state against its preceding one. Sample activities [2] are provided below:

  • Forward Execution Activities from [1] to [2]: the activities typically conducted during a project’s Design Phase which includes the planning and specifications sub-phases (e.g. drafting, drawing, detailing, and modelling); and
  • Reverse Measurement Activities from [2] to [1]: the activities necessary to verify or validate digital deliverables against information requirements (e.g. checking floor areas in a BIModel against a client’s spatial requirements).
  • Forward Execution Activities from [2] to [3]: all the activities typically conducted during the Construction Phase which includes construction planning and commissioning (e.g. laying floors, mounting ceilings, and painting walls); and
  • Reverse Measurement Activities from [3] to [2]: the activities necessary to test and confirm physical outputs against digital deliverables (e.g. checking the placement of duct hangers on site against relevant models or mechanical shop drawings).
  • Forward Execution Activities from [3] to [1]: all the activities typically conducted during the Operation Phase which includes management, maintenance and decommissioning (e.g. cleaning rooms, repairing down-pipes, replacing roof tiles); and
  • Reverse Measurement Activities from [1] to [3]: the activities necessary to capture data pertaining to a physical asset or to monitor the performance of a physical system (e.g. data capture through laser scanning and data monitoring through sensors).

Note: the three Key Information Activities (Prepare [P], Manage [M], and Utilise [U]) will be explained in a future model.

Information Management Actors

The Information Management Activities separating Information Management States are conducted by actors which are either humans and/or computers. There are three main actors who operate throughout the Information Management Cycle:

  • Design Information Management Actors: executing the transition from Information Requirements to Digital Deliverables and measuring (e.g. verifying or validating) how well Digital Deliverables match with Information Requirements;
  • Construction Information Management Actors: executing the transition from Digital Deliverables to Physical Assets and measuring (e.g. testing or confirming) how well Physical Assets match with Digital Deliverables; and
  • Operation Information Management Actors: executing actions applied to Physical Assets (e.g. operating, maintaining and decommissioning). Also these actors can either (a) measure - e.g. capture or monitor - how well a Physical Asset matches with the Information Requirements covering the asset (within the same Information Management Cycle), or (b) measure one or more Physical Assets in order to generate new Information Requirements within a new Information Management Cycle.

Actors may overlap and replace each other. Depending on the current state of technologies, processes and policies within a market, two or even one Information Management Actor may be able to complete all execution and measurement activities across an Information Management Cycle [3].

 


Acknowledgements

The following colleagues have provided improvement suggestions to version 0.1 of this model: AProf. Sheryl Staub-French, AProf. Julie Jupp, Ms. Marzia Bolpagni, and Mr. Victor Roig Segura. Thank you to all.

 


Endnotes

[1] Each Information Management Cycle has a nominal start (e.g. information covering the design of a new physical asset) and a nominal end (e.g. information decimated through the demolition of an asset). However, it is possible and even probable that the same information would persist over a number of Cycles (e.g. through iterative renovation of the same physical asset).

[2] Activities are a subset of ‘Relations’ within the Conceptual BIM Ontology.

[3] This model is part of the BIMe Initiative Integrated Information Platform project


43. Product Development Diagram

BIMe-Initiative-Product-Development-Diagram

BIMe Initiative Product Development Diagram (Updated Jan 21, 2018full size image)

This diagram illustrates the process of delivering a BIMe Initiative Product (a Published Guide or a Software Application): a Top-Level Project must be first launched. Each Top-Level Project typically includes both existing components (e.g. a taxonomy or a classification) and new components. Existing components are (a) selected from the Knowledge Object Library, a public resource available through BIMexcellence.org. New components (e.g. a framework or a software module) are (b) generated by BIMe Members through Micro Projects. Once completed and validated, all components are (c) packaged into a new BIMe Product. Once tested and verified, the new product is (d) released through the Product Library (a webpage on BIMexcellence.org) and all newly generated components are (e) added to the Knowledge Object Library for future reuse. As opposed to the Knowledge Object Library and the Product Library, which are both publicly available resources, the generation of new components and end products are conducted within the Project Space (wiki pages, chat rooms and physical meetings) which are accessible to BIMe Members and invited international collaborators.

To understand Conceptual Components (also referred to as Conceptual Constructs), please review the Conceptual Hierarchy and - as an example - how components populate the Research Continuum. Also, for more information about BIMe Initiative products, projects, and how they are managed, please refer to 103in BIMe Initiative Projects.


42. BIMe Initiative Knowledge Structure

image from www.bimframework.infoBIMe Initiative Knowledge Structure (full size image)

This model represents the Knowledge Structure which the BIMe Initiative (BIMexcellence.org) is reliant upon to deliver interconnected software applications, guides, conceptual structures and learning materials. The Knowledge Structure is composed of five complementary Knowledge Sets:

  • KS1 Knowledge Foundations represents all the research supporting the BIMe Initiative;
  • KS2 Knowledge Blocks represents the modular language developed/used by the BIMe Initiative to define inputs, processes and outputs;
  • KS3 Knowledge Tools represents all the digital and analogue tools/templates used to conduct knowledge acquisition, engineering and sharing;
  • KS4 Knowledge Workflows represents all repeatable procedures for knowledge acquisition and service delivery; and
  • KS5 Knowledge Views identifies the varied ways the BIMe Initiative activities and deliverables can be represented and communicated.

The Knowledge Sets and their subsets form the bases for all BIMe Initiative Projects (refer to 103in); organise the activities of the BIMe Initiative Network (refer to 104in); and allow the development of an expansive Knowledge Object Library.


41. Macro Diffusion Responsibilities

This conceptual model (Figure 1) identifies nine BIM player groups (industry stakeholders) distributed across three BIM Fields (technology, process and policy) as defined within the BIM Framework. The nine player groups are: policy makers, educational institutions, construction organisations, individual practitioners, technology developers, technology service providers, industry associations, communities of practice, and technology advocates.


image from www.bimframework.infoFigure 1. Macro Diffusion Responsibilities v1.0 (full size, current version)

 

The nine player groups belong to either BIM Field or their overlaps. Pending further research, the tenth player group at the intersection of the three fields is intentionally excluded from this model. Table 1 below provides a succinct description of each player group followed by how this subdivision can be used in evaluating BIM diffusion within and across different markets.

 

Macro-Diffusion-Responsibilities-TableTable 1. Macro Diffusion Responsibilities matrix (player groups with sample players – market scale)

 

Each of the nine player groups identified in Figure 1 includes a number of player types. For example, player group 3 (construction organisations) is composed of varied player types including: asset owners, architects, engineers and project managers. Also, player group 4 (individual practitioners) is composed of professionals, associated professionals and tradespeople. These distinctions between player groups, player types and unique players (e.g. a specific person, group, association, company or university) allow the targeted assessment and comparison of stakeholders’ involvement.

 

Below is a short video explaining the above, as available on the Framework's YouTube channel:

 

 

Please note that the above model and table are part of five macro adoption models collated within "Succar, B., & Kassem, M. (2015). Macro-BIM adoption: Conceptual structures. Automation in Construction57, 64-79". Download full paper from here: http://bit.ly/BIMPaperA8


40. Project Information Taxonomy

Updated June 30, 2019 (original post was June 20, 2016, updated November 29, 2019)

The Information Taxonomy (previously Project Information Taxonomy) extends the Modular Requirements Clarification Language first introduced in Paper A10 . The taxonomy currently includes concepts/terms to be used in defining information requirements within mission-critical documentation (e.g. within an Employer’s Information Requirements (EIR), a BIM Management Plan (BMP) or similar).

The term ‘information’ – here used to describe Digital Assets - can be subdivided into three Information Representation Types or ‘digital artefacts’- documents, models, and data:

  • Document: A digital medium (e.g. an email, web page, or a PDF document) carrying a variety of information including text, metadata, or embedded 3D models. A document refers to “information and its supporting medium” [i.e. when the information is placed within a medium, it becomes a document] ….“the medium can be paper, magnetic, electronic or optical computer disc, photograph or master sample, or a combination thereof” (ISO, 2009, Item 4.5) Item 4.5, adapted from ISO (2015, Item 3.7.2) - Items 3.7.2. In this taxonomy, the term Document refers to a digital medium or an analogue medium that need to be digitised in preparation for management and utilisation; non-digital documents will be qualified (e.g. paper document). Examples of Documents include a Master Plan Drawing, a Performance Certificate, or an Audit Report;
  • Model: A digital three-dimensional medium carrying a variety of information and may embed or reference both documents and data sets. A model is a “representation of a system that allows for investigation of the properties of the system” (ISO, 2016). As a term, it may refer to digital models (e.g. shell/boundary or solid geometry graphical models), physical (e.g. a sand castle, Lego model, or 3D printed shapes), financial, mathematical, or conceptual models. In this taxonomy, unless qualified, the term Model refers to 3D computer-generated graphical models representing a discipline/ specialty (e.g. architectural model), a state (e.g. record model), or base technology (e.g. object-based model); and
  • Data: A digital sequence of symbols – typically letters and numbers - that can be collected and parsed/ interpreted by an actor. Data may be statically embedded within Documents and Models or drive their dynamic generation/ modification - adapted from ISO/IEC (2015). Data can be captured through sensors, actuators, and scanners; derived from connected data sources; or generated through machine learning. The term Data may be coupled with other terms preceding it (e.g. structured, unstructured, or meta Data) or following it (e.g. Data Set, Cloud, Table, Script, or Routine) to indicate functional groupings, relationship structures, internal cohesion, semantic inference, or coded computer instructions. Data can also be represented as a variety of charts and diagrams. In this taxonomy, the term Data refers to digital computable data; data sourced from analogue devices (e.g. heat sensors and legacy imaging equipment) need to be converted into digital data before it can be managed and utilised.

This representational subdivision is intended for practical purposes: to allow more accurate identification of targeted digital deliverables and – as illustrated in the Information Taxonomy  below – to provide flexibility in specifying information delivery uses, views, view definitions, viewers, and environments.

Information Taxonomy - Table

Information Use: The intended uses/applications of information

Document Use

The intended or expected types of deliverables from developing and exchanging information through Documents

Examples: document-based reporting, certifying, or warranting

Model Use

The intended or expected types of deliverables from generating, collaborating-on and linking Models to external databases

Examples: model-based representing, simulating, or quantifying

Data Use

The intended or expected types of deliverables from generating, exchanging, and manipulating Data

Example: data mining or scripting (e.g. using code to drive cutting, milling or sintering equipment

Information View: How information is represented to enable its use

Document View

A view enabling one or more Document Uses. A Document View can be a drawing, schedule, report, an instruction memo, or a set of specifications. A Document View may be drafted (computer-assisted) by a human-actor or derived automatically from a Model or Data source

Example: Product Data Sheet or Room Data Sheet (a drawing detailing an operator’s requirements for each room type including room layout, furniture, fittings, equipment, and surface finishes)

Model View

A view enabling one or more Model Uses. A Model View can be a static/ dynamic 3D view, an animation, or a holograph. A Model View follows the specifications within its corresponding Model View Definition or reflects custom project/asset requirements

Examples: Model View showing only Architectural elements, or another showing both Mechanical and Structural elements within a Model Viewer (see further below)

Data View

A view enabling one or more Data Uses. A Data View can be a code snippet, a Computer Numerical Control (CNC) file, an XML/JSON file, or similar

Examples: a data chart, a node-link diagram, an If This Then That (IFTTT) recipe, an FME translator, or a visual script in Grasshopper or Dynamo

Information View Definition: How Information Views are defined for consistent use of information

Document View Definition

A specification (or template) which identifies the contents, attributes, and formats of Document Views (see above). Document View Definitions are typically generated by authorities, technology advocates, and associations promoting standardisation

Examples: Product Data Template (a document identifying the data that need to be collected for each product - e.g. model, manufacturer, performance attributes, and maintenance requirements)

Model View Definition (MVD)

A specification which identifies the properties and specifies the exchange requirements of Model Views. A 'standardised' Model view Definition (MVD) can be a subset of an established schema (e.g.   Industry Foundation Classes) and is typically intended for software developers (not end users) to implement into their Software Tools (ISO, 2016)

Example: the IFC4 Design Transfer View by buildingSMART International

Data View Definition

A specification which identifies the properties and specifies the exchange requirements of Data Views. Data View Definitions are generated by a variety of information actors to formalise data exchange scenarios and harmonise data analysis methods

Examples: a data representation template, data translation script, or data analysis formula

Information Viewer: The software allowing access to information to human actors

Document Viewer

A software application allowing users to inspect and manipulate Information according to pre-set Document Views

Examples: PDF reader or a 2D CAD viewer

Model Viewer

A software application allowing users to inspect and navigate Models according to ad-hoc or standardised Model View Definitions

Example: BIM Vision, BIMx, Dalux, or Solibri Model Viewer

Data Viewer

A software application allowing users to inspect and manipulate data according to pre-set Data View Definitions

Examples: Tableau or Business Intelligence tools

Common Information Environment: The distributed digital ecosystem allowing the collation and utilisation of information by multiple stakeholders. A Common Information Environment may include a combination of software solutions connected to disparate data sources through middleware and plugins

Shared Document Environment

An ecosystem for managing documents, composed of several software modules – including a Document Viewer, and allowing the isolation of Information by Document View

Example: A digital ecosystem built around a Document / Project Management System

Federated Modelling Environment

An ecosystem for managing Models, composed of several software modules - including a Model Viewer, and allowing the isolation of Information by Model View

Example: A digital ecosystem built around a Model Server or BIM-enabled Software as a Service (SaaS)

Integrated Data Environment

An ecosystem for managing Data sets and structures, composed of several software modules – including a Data Viewer, allowing the isolation of Information by Data View

Example: A digital ecosystem built around Data Warehousing and Data Integration solutions

 

More info

This post is part of the BIMe Initiative Integrate Information Platform project and contributes to the ongoing effort to clarify the language used (or to be used) in defining project requirements within Noteworthy BIM Publications. Some of the concepts introduced above are still being refined, connected and reconnected to multiple models, taxonomies and classifications. For an update-to-date description of these concepts and their relations, please refer to respective terms within the BIM Dictionary.

 

Endnotes

[1] A document refers to “information and its supporting medium” [i.e. when the information is placed on a medium, it becomes a document] … “The medium can be paper, magnetic, electronic or optical computer disc, photograph or master sample, or a combination thereof” – [SOURCE: ISO 14050:2009, 4.5 - adapted from ISO 9000:2005, 3.7.2]

[2] Another definition is a “representation of a system that allows for investigation of the properties of the system” [SOURCE: ISO 29481-1:2016(en) Building information models — Information delivery manual — Part 1: Methodology and format]

[3] “a reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or communication, or processing” [SOURCE: ISO/IEC 2382-1:1993, Information technology — Vocabulary — Part 1: Fundamental terms.01.01.02]

[4] Note: Documented Project Information should not be confused with Documented Information which is defined in ISO 9001:2015 as meaningful data that is required to be controlled and maintained by the organization and the medium on which it is contained (Source: ABP Consultant)


39. Model Uses Taxonomy

Model-Uses-TaxonomyFigure 1. Model Uses Taxonomy (Full Size)

The Model Uses Taxonomy organises Model Uses into a tiered structure that allows: (a) collation of Model Uses from across different industries; (b) close alignment with the Competency Taxonomy (Succar et al., 2013 - http://bit.ly/BIMpaperA6); (c) custom use-cases for unique projects; and (d) future expansion towards whole of life cycle classifications.

The Model Uses Taxonomy includes 3 Categories and 9 Series:

Category I: General Model Uses represent all Modelling Information as applicable across various knowledge domains, industries, and information systems. General Model Uses are organised a single Series, General Modelling (1000-1990) and include the word ‘modelling’ in their name to differentiate them from other categories (e.g. Audio-visual Systems Modelling or Urban Modelling);

Category II: Domain Model Uses represent industry-specific Modelling Information. The Construction Domain Model Uses are organised in seven Series:

CATEGORY III: Custom Model Uses are a combination of General and Domain Model Uses to respond the non-typical requirements of a specific project. Custom Model Uses are organized under a single Series, Custom Modelling (9000-9990) - e.g. Modelling security systems for a correctional facility.

 Please Note:

  1. The Model Uses Taxonomy was published as part of  BIM ThinkSpace Episode 24 (Sep 9, 2015).
  2. Please also refer to earlier post: Model Uses Conceptual Structures (Sep 9, 2015)
  3. There are currently 125 Model Uses across all categories and series (as of March 24, 2016). For a full list, please refer to BIMexcellence.org/model-uses.

38. Conceptual Reactor

Conceptual-Reactor

The BIM Framework Conceptual Reactor v1.0 (full size, current version)

The BIM Framework Conceptual Reactor explains how existing conceptual constructs – terms, classifications, taxonomies, models and frameworks – are used to identify, explain and test new constructs.

The conceptual reactor thus allows the BIM framework to be continuously extended according to evolved research aims and objectives - represented as input 1 (or 'in1'). By integrating existing conceptual structures (in2) with new knowledge gained through literature reviews, and data collection (in3), the reactor can then generate new conceptual structures (output or ‘out’) after passing through an iterative, three-stage theory-building process. This process has been identified by J. Meredith (1993) (J. R. Meredith, Raturi, Amoako-Gyampah, & Kaplan, 1989) and includes three repetitive stages - description, explanation and testing:

  • First, the Description Stage develops a description of reality; identifies phenomena; explores events; and documents findings and behaviours;
  • Second, the Explanation Stage builds upon descriptions to infer a concept, a conceptual relationship or a construct; and then, develops a framework or a theory to explain and/or predict behaviours or events. In essence, the explaining stage develops a testable theoretical proposition which clarifies what has previously been described; and
  • Third, the Testing Stage inspects explanations and propositions for validity; tests concepts or their relationships for accuracy; and tests predictions against new observables.

37. Model Uses - Conceptual Structures

Model Uses are the “expected or intended project deliverables expected from generating, collaborating-on and linking 3D models to external databases” (BIM Dictionary, 2015). Each Model Use represents a set of defined requirements, specialised activities and specific project outcomes, grouped together under a single heading. 

Model Uses [1] rely on the conceptual structures of the BIM Framework, namely: the Tri-Axial Framework, Competency Framework, and BIM Ontology - Figure 1:

   Model-Uses-Conceptual-StructuresFigure 1. Conceptual Structure underlying Model Uses (Full Size v0.3 or Older Version )

As highlighted in Figure 1, Model Uses are supported by three conceptual structures [2] - Updated May 2, 2016:

  • Within the Tri-Axial Framework, Model Uses are deliverables [Tri-axial Framework>Fields>Field Components>Deliverables (Model-based Deliverables, identified through the Information Management Lens)] (refer to Papers A2 and A5);
  • According to the BIM Ontology, a Model Use is a knowledge block [BIM Ontology>Knowledge Objects>Knowledge Sets>Knowledge Blocks> Information Uses > Model Uses] (refer to Thesis, Appendix A); and
  • Within the Competency Framework, Model Uses are competency topics [Competency Framework> Competency Hierarchy>Competency Tiers (Domain)>Competency Set (Operation)>Competency Topics (9 Topics)] (Refer to Paper A6).
 

 
[1] Model Uses are discussed in detail  within Episode 24 on BIM ThinkSpace.

[2] The number of structures supporting a BIM Framework part is proportional to its conceptual strength.

 


36. BIM Ontology

  BIM-OntologyFigure 1.  The BIM ontology v3.1 (Neuron Model v0.4, Updated July 28, 2016)

IMPORTANT: newer versions of the BIM Ontology are published as a BIMe Initiative resource. As of July 28, 2017, the image above and the information below may be out of date.

The BIM ontology is an informal, semi-structured, conceptual domain ontology used for knowledge acquisition and communication between people. It is intended to represent knowledge interactions (push/pull) between BIM players, their deliverables and requirements (Figure 2) as described within Papers A1 and A2 (Succar, Sher, & Aranda-Mena, 2007) (Succar, 2009) and facilitate the validation of conceptual models (Shanks, Tansley, & Weber, 2003).

The BIM ontology includes BIM-specific concepts, their relations and attributes which facilitate analysis of domain knowledge (Noy & McGuinness, 2001), enable the construction of a domain framework (Studer, Benjamins, & Fensel, 1998), and support knowledge acquisition and communication (Milton, 2007a, 2007b) (Cottam, 1999) (Studer et al., 1998). Figure 2 below illustrates how ontological objects underlie the BIM Framework. The concept map (Figure 2 - right) is a visual representation of the ontological relationship between the three concepts (BIM Fields, BIM Stages and BIM Lenses); while the visual knowledge model (Figure 2 - left) abstracts these relations into the Tri-axial Model, a simplified graphical representation to facilitate communication. As discussed in Papers A1 and A2, this combination of visual modelling, driven by explicit ontological relations, renders the BIM Framework and its many conceptual constructs more accessible for analysis, modification and extension.

Also, as depicted in the Conceptual Hierarchy post, ontological relations enable a ‘conceptual mesh’ linking different types of conceptual constructs: frameworks, models, taxonomies, classifications and specialized dictionary terms. 

BIM-Ontology---2-Images
Figure 2. Visual Knowledge Model (left) + Concept Map (right)

 

Generating the BIM ontology

The BIM ontology has been generated by amending and reusing existing ontologies; a process recommended by Noy and McGuiness (2001). The reuse of an existing ontology followed Gruber’s criteria for shared ontologies: clarity, coherence, extensibility, minimal encoding bias and minimum ontological commitment (Gruber, 1995). Based on these criteria, the BIM ontology was first derived from the General Technological Ontology (Milton, 2007a) (Milton, 2007b) and the General Process Ontology (Cottam, 1999). While earlier iterations of the BIM ontology followed source definitions, newer iterations are more closely matched with the conceptual and practical requirements of the BIM domain.

Knowledge Objects

The BIM ontology comprises of four high-level knowledge objects: concepts, attributes, relations and knowledge Sets (Table 1):

 

Knowledge Objects

Description

Examples

I

Concepts (Table 2)

A mental construct

Component; Document; Role

II

Attributes (Table 3)

Values and qualifiers associated with Concepts

Cost; Count; Description

III

Relations (Table 4)

Connections between Concepts; the effect of one Concept on another

Approves; Detects; Supplies

IV

Knowledge Sets (Table 5)

A purposeful compilation of Concepts, their Attributes and Relations

Knowledge Foundations; Knowledge Blocks; Knowledge Views

Table 1. BIM Ontology Knowledge Objects (v3.0, last updated August 16, 2015)

 I. Concepts

Ability

Activity

Certificate

Component

Conception

Conceptual Construct

Constraint

Data Source

Data Use

Deliverable

Designation

Document

Document Use

Effect

Equipment

Event

Example

Facility

Format

Function

Hardware

Incentive

Information Use

Knowledge Domain

Lesson

Machine

Measurement

Medium

Message

Method

Milestone

Model

Model Use

Place

Player

Product

Proof

Recommendation

Representation

Requirement

Responsibility

Result

Role

Rule

Scenario

Software Application

Space

Standard

System

System Unit

Target  Test Tool  Trigger    

Table 2. Concepts (51 Concepts in v3.10, last updated July 28, 2016)

II. Attributes

Name

Description

Name

Description

Availability

An integer or string indicating the basic existence or availability of a concept

Location

The coordinates of an object within a physical space

Cost

A monetary value expressed in whole numbers, fractions or decimals

Market

A defined economical boundary

Count

An expression of elemental numbers using integers

Order

An arrangement whether chronological or spatial – not preferential or developmental (refer to Grade)

Description

An explanation expressed using words, phrases and sentences

Proposition

A mutually exclusive distinction between clear choices

Grade

A variables denoting preference or developmental achievement expressed in integers, percentages or text

State

A description of condition whether temporary or permanent

Link

A hypertext connection

Time

An expression of chronology expressed in minutes, second, days, etc…

Language

The language used to define a concept or a relation

Type

A differentiation of genus

Table 3. Attributes (14 Attributes in v3.0, last updated August 16, 2015)

III. Relations

Below is a list of all standard relationship labels (158 as of Aug, 2015) connecting different Concepts:

  

Abort

Adopt

Aggregate

Affect

Allow

Allow for

Analyse

Append

Approve

Arrange

Assemble

Assess

Analyse

Audit

Authorise

Build

Buy

Capture

Cause

Certify

Check

Choose

Classify

Complete

Collaborate with

Collate

Collect

Commission

Communicate with

Compare

Conduct

Confirm

Construct

Consult

Contact

Contain

Continue

Control

Coordinate

Decrease

Delimit

Deliver

Demolish

Demonstrate

Deselect

Design

Detect

Determine

Describe

Develop

Discover

Divide

Discuss with

Document

Draw

Educate

Empower

Encourage

Enforce

Engage with

Establish

Estimate

Exchange

Explode

Extract

Evaluate

Fabricate

Facilitate

Federate

Follow

Forecast

Function as

Gather

Generate

Guide

Has part

Has resource

Identify

Ignore

Implement

Improve

Incentivise

Increase

Inform

Initiate

Innovate

Integrate

Interchange

Interview

Invent

Involve

Join

Know

Lead

Link to

Locate

Maintain

Make aware

Make

Maintain

Manage

Maximise

Measure

Merge

Minimise

Model

Monitor

Notify

Observe

Operate

Own

Participate in

Perform

Plan

Populate

Prepare

Prescribe

Prioritise

Procure

Produce

Prove

Provide

Provide for

Pull

Push

Qualify

Quantify

Question

Receive

Recommend

Regulate

Reject

Replace

Require

Review

Revise

Run

Sample

Select

Share

Simulate

Size

Start

Stop

Supply

Survey

Test

Track

Train

Transfer

Transform

Transmit

Understand

Update

Use

Validate

Verify

Visualise

Warn

Write

 

Table 4. Relations (158 Relations in v3.0, last updated August 16, 2015)

IV. Knowledge Sets

Knowledge Sets are higher order Knowledge Objects composed of the other three lower order concepts, relations and attributes.

 

Name

Description

Example

1

Knowledge Foundations

A structured view of concepts and their relations. Knowledge Foundations include dictionaries, classifications, taxonomies, models, frameworks and theories

The BIM Ontology, The BIM Framework, Granularity Levels, Organizational Scales…

2

Knowledge Blocks

A self-contained knowledge item used to build larger knowledge structures  

A competency item, dictionary item, model use…

3

Knowledge Tools

An interactive view of concepts and their relations intended to assess, assist and educate its users. A tool has modifiable variables leading to varied outputs based on inputs

A calculator, an online tool, a cad software…

4

Knowledge Workflows

A repeatable set of activities conducted as part of a larger process to deliver a measurable outcome

An assessment methodology, a knowledge capture technique, a construction method, a verification routine….

5

Knowledge Views

A delimited self-contained view of multiple concepts and their relations - irrespective of knowledge content format (text, images or graphs) or knowledge content medium (hardcopy or softcopy)

A training manual, journal article, CAD drawing, poster, web page, video, concept map, repertory grid, process map, concept map, flowchart, Gantt chart…

Table 5. Knowledge Sets (4 sets in v3.1, last updated July 28, 2016)

 

 

Publication Log - Summary 

V.

Date

Description

1.0

18 Oct ‘07

Initial version submitted as part as a research proposal at the University of Newcastle, NSW

1.2

6 Dec ‘08

Published as Table 6 within Paper A2

2.0

13 Dec ‘13

Published as Appendix A of the PhD Thesis

3.0

16 Aug  ’15

First published on BIMframework.com

3.01

29 Apr  '16

2 new Concepts added, 1 Concept modified

 3.02

28 Jun '16

5 new Concepts added

3.03

2 Jul '16

1 Concept modified

3.10

28 Jul' 16

1 Concept added, 1 Knowledge Set renamed

3.11

21 Aug' 16

2 Concepts added

3.12

23 Jan' 2017

Removed the ‘active’ tone from all Relations. Added 1 Concept + 2 Relations. Modified 1 Concept

 

References

Cottam, H. (1999). Ontologies to Assist Process Oriented Knowledge Acquisition (Draft). Retrieved from http://www.inovexadvancedsolutions.ltd.uk/spede/default.htm

Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International journal of human-computer studies, 43(5-6), 907-928.  Retrieved from http://www.sciencedirect.com/science/article/B6WGR-45NJJDF-K/2/b47f5cb67315c76b60ac39f44e0a2cec

Milton, N. R. (2007a). Knowledge Acquisition in Practice: A Step-by-step Guide: Springer, London.

Milton, N. R. (2007b). Specification for the General Technological Ontology (GTO). http://www.pcpack.co.uk/gto/notes/files/GTO%20Spec%20v4.doc Retrieved from http://www.pcpack.co.uk/gto/notes/files/GTO%20Spec%20v4.doc

Noy, N. F., & McGuinness, D. L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. http://www.lsi.upc.edu/~bejar/aia/aia-web/ontology101.pdf Retrieved from http://www.lsi.upc.edu/~bejar/aia/aia-web/ontology101.pdf

Shanks, G., Tansley, E., & Weber, R. (2003). Using ontology to validate conceptual models. Communications of the ACM, 46(10), 85-89. doi:10.1145/944217.944244

Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1-2), 161-197.  Retrieved from http://www.sciencedirect.com/science/article/B6TYX-3SYXJ6S-G/2/67ea511f5600d90a74999a9fef47ac98

Succar, B. (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18(3), 357-375.  Retrieved from http://dx.doi.org/10.1016/j.autcon.2008.10.003

Succar, B., Sher, W., & Aranda-Mena, G. (2007). A Proposed Framework to Investigate Building Information Modelling Through Knowledge Elicitation and Visual Models. Paper presented at the Australasian Universities Building Education (AUBEA2007), Melbourne, Australia. http://aubea.org.au/ocs/viewpaper.php?id=15&cf=1

  


35. Point of Adoption

The Point of Adoption (PoA) model is a distillation of three implementation phases: readiness, capability, and maturity. As a term, PoA identifies the juncture(s) where organizational readiness transforms into organizational capability/maturity. It also identifies the juncture(s) where technological invention and a procedural innovation transforms into organizational - as well as market wide - diffusion:

Point-of-Adoption

Point of Adoption model v1.1 (full size, current version)

As explored in Figure 1 above, transformative BIM adoption starts at the Point of Adoption (PoA) when an organization, after a period of planning and preparation (readiness), successfully adopts object-based modelling tools and workflows. The PoA[1] thus marks the initial capability jump from no BIM abilities (pre-BIM status) to minimum BIM capability (Stage 1). As the adopter interacts with other adopters, a second capability jump (Stage 2) marks the organization’s ability to successfully engage in model-based collaboration. Also, as the organisation starts to engage with multiple stakeholders across the supply chain, a third capability jump (Stage 3) is necessary to benefit from integrated, network-based tools, processes and protocols (refer back to BIM Stages).

Each of these capability jumps is preceded with considerable investment in human and physical resources, and each stage signals new organizational abilities and deliverables not available before the jump. However, the deliverables of different organizations at the same stage may vary in quality, repeatability and predictability (refer to BIM Maturity Index). This variance in performance excellence occurs as organizations climb their respective BIM maturity curve, experience their internal BIM diffusion, and gradually improve their performance over time[2].

The multiple maturity curves depicted in Figure 1 reflect the heterogeneous nature of BIM adoption even within the same organization (e.g. sample Organization X) has a compiled rating of 1c, 2b and 3a). This is due to the phased nature of BIM with each revolutionary stage requiring its own readiness ramp, capability jump, maturity climb, and point of adoption. This is also due to varied abilities across organizational sub-units and project teams: while organizational unit A1 (within Organization A) may have elevated model-based collaboration capabilities, unit A2 may have basic modelling capabilities, and unit A3 may still be preparing to implement BIM software tools. This variance in ability necessitates a compiled rating for organization A as it simultaneously prepares for an innovative solution, implements a system/process, and continually improves its performance.

Note: the Point of Adoption model is also discussed  (along with the UK BIM Maturity model) in Episode 22 on BIM ThinkSpace.

Update (May, 2016): below is a short video explaining the above on the Framework's YouTube channel:

 



[1] The Point of Adoption (PoA) is not to be confused with the critical mass ‘inflection point’ on the S-curve (E. M. Rogers, 1995) (Everett M Rogers, Medina, Rivera, & Wiley, 2005); or with the ‘tipping pint’, the critical threshold introduced by Gladwell (2001).

[2] The X-axis in Figure 1 represents time relative to each PoA, not as an absolute scale. That is, this version of the chart does not represent a snapshot view of compiled capability/maturity at a specific point in (absolute) time.