44. Information Cycle

Updated Dec 6, 2020 | The Information Cycle Model (previously Information Management Cycle model) employs the three Project Lifecycle Phases – Design, Construction, and Operation (Succar, 2009) [1]– to identify Information Actors and their high-level Information Actions connecting several Information Milestones within the Lifecycle Information Transformation and Exchange (LITE) framework (Succar and Poirier, 2020) [2].

Information-Cycle-v0.7Information Cycle Model | v0.7 at OScale 8, GLevel 2 | Full Size

This high-level conceptual model – covering OScale 8 [3], shown at GLevel 2 [4] - describes the Information Cycles that Structured Information pass through. Each 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). If the same Physical Asset is iteratively renovated or reused, the information persists over many Cycles.

The Cycle connects four Information Milestones derived from the LITE framework and forming a rotating triangle (3 vertices). The first vertex [i] represents both Expected Physical Assets and Targeted Digital Deliverables (Information Milestones 3 and 4). The second vertex [ii] represents Actual Digital Deliverables (Milestone 6), and the third vertex [iii] represents the Actual Physical Assets (Milestone 7). These three vertices are connected by Information Actions conducted by Information Actors.

Information Actions

As discussed in the LITE framework, Information Actions may either be Forward Execution Actions, connecting one Information Milestone to the next one in the Information Cycle, or Reverse Measurement Actions, needed to verify the accuracy, validity, and quality of an Information Milestone against a previous one. In the model, three primary forward actions are shown - Prepare, Manage, and Use:

  • The Prepare action refers to all activities to collate, harmonise, and otherwise prepare information for management during each project lifecycle phase. Here, preparation refers to both “Anamnesis, dedicated to the survey and collection of facts about the building; and Diagnosis, dedicated to the analysis and interpretation of the collected facts to obtain the necessary understanding of the building and its performance” (Scherer & Katranuschkov, 2018, p. 55) [5];
  • The Manage action refers to all activities to generate, exchange, and otherwise manage information – whether operational, tactical, or strategic (Hosseini, Roelvink, Papadonikolaki, Edwards, & Pärn, 2018) [6]- during each project lifecycle phase; and
  • The Use action refers to all activities that benefit from managed information during each project lifecycle phase – for example: the operation, maintaining, replacing, refurbishing, renewing, upgrading, and re-purposing of Physical Assets (TfNSW, 2015) [7] during the Operation Phase.


Information Actors

This conceptual model and the overall LITE framework does not differentiate between human and machine Actors, nor between discipline and information Actors (i.e. between a design professional and the design information manager) as the separation between these Actors– due to automation and Artificial Intelligence (AI) – is progressively blurring. That is, Actors can be humans, machines (computers/robots), and their hybrids (augmented humans or humanoids/cyborgs) who are (i) acting on existing information to generate new information, (ii) transforming inputs into outputs, and (iii) delivering products and/or services. A Single Actor may be acting alone throughout the whole project/asset lifecycle (e.g. a human Actor designing, constructing, and living in their own forest cabin, or an AI-enabled machine designing, delivering, and utilising a complex mineral-extraction facility on Mars). Multiple Actors may also be acting in sync/sequence at different phases of an asset’s lifecycle to design, deliver, and utilise an asset.

The model identifies three nominal Actors operating throughout the Information Cycle:

  • Design Information Actors: executing the transition from [i] Target Deliverables (Expected Physical Deliverables & Targeted Digital Deliverables) to [ii] Actual Digital Assets and measuring (e.g. verifying or validating) how well Actual Digital Assets match with their respective Targeted Digital Deliverables;
  • Construction Information Actors: executing the transition from [ii] Actual Digital Assets to [iii] Actual Physical Assets and measuring (e.g. testing or confirming) how well Actual Physical Assets match with Actual Digital Assets; and
  • Operation Information Actors: executing actions applied to [iii] Actual Physical Assets (e.g. operating, maintaining, and decommissioning). These actors can either (a) measure - e.g. capture or monitor - how well an Actual Physical Asset matches with the Expected Physical Deliverables within the same Information Cycle, or (b) measure one or more Actual Physical Assets to identify new [i] Target Deliverables within a new Information Cycle.

Actors may overlap and replace one another. Depending on Asset Scale [8] and the diffusion of technologies, processes, and policies within a market (Succar & Kassem, 2015) [9], two or even a single Information Actor may be able to conduct all execution and measurement actions across an Information Cycle.


Versions and acknowledgements

This post includes an updated visual knowledge model (v0.7) and updated explanatory text to align with the now published LITE framework (Succar and Poirier, 2020). The original version 0.3 – Information Management Cycle model - was published through this blog on July 25, 2017 and can still be accessed as an image file from here (v0.3 was reviewed by Dr. Sheryl Staub-French, Dr. Julie Jupp, Dr. Marzia Bolpagni, and Mr. Victor Roig Segura). An updated version 0.5 was later published in Jan 21, 2018 and included important modifications – image file can be accessed from here. For a more detailed review, the updated post including the v0.5 model is available for download as a pdf file from here (148KB).



[1] Succar, B. (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18(3), 357-375. DOI:

[2] Succar, B., & Poirier, E. (2020). Lifecycle information transformation and exchange for delivering and managing digital and physical assets. Automation in Construction, 112, 103090.

[3] There are 12 Organisational Scales – refer to Post 11

[4] Granularity Levels clarify the extent of informational detail within a conceptual model, matrix, tool, or document. There are five Granularity Levels (GLevels) progressing from lowest to highest:

[5] Scherer, R. J., & Katranuschkov, P. (2018). BIMification: How to create and use BIM for retrofitting. Advanced Engineering Informatics, 38, 54-66. DOI:

[6] Hosseini, M. R., Roelvink, R., Papadonikolaki, E., Edwards, D., & Pärn, E. (2018). Integrating BIM into facility management: typology matrix of information handover requirements (Vol. 36)

[7] TfNSW. (2015). Systems Engineering, Transport for NSW, New South Wales Government (T MU AM 06006 GU). Retrieved from

[8] Asset Hierarchy and Asset Scale are covered in Post 30 -  

[9] Succar, B., & Kassem, M. (2015). Macro-BIM adoption: Conceptual structures. Automation in Construction, 57, 64-79. DOI:

43. 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 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 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 ( 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.

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.


27. Conceptual Hierarchy

image from www.bimframework.infoConceptual Hierarchy Current Version, full-size image (older version v1.0)

The BIM framework is composed of several interrelated conceptual constructs: models, taxonomies, classifications and dictionaries. A common conceptual ontology connects all conceptual constructs and makes explicit the relationship between them. Below is a generic description of the depicted conceptual parts:

Frameworks show “the gestalt, the structure, the anatomy or the morphology of a field of knowledge or the links between seemingly disparate fields or sub-disciplines” (Reisman, 1994, p. 92).

Models (conceptual models) are simplified representations and abstractions of the “enormous richness of this world” (Ritter, 2010, p. 360) (Lave & March, 1993).

Taxonomies are an efficient and effective way to organize and consolidate knowledge (Reisman, 2005) (Hedden, 2010). A well-structured taxonomy allows “the meaningful clustering of experience” (Kwasnik, 1999, p. 24).

Classifications are the “meaningful clustering of experience” (Kwasnik, 1999, p. 24) and “lies at the heart of every scientific field” (Lohse, Biolsi, Walker, & Rueter, 1994, p. 36). Classification is also a heuristic tool useful during the formative stages of discovery, analysis and theorizing (Davies, 1989).

Dictionaries constitute a web of meaning (Cristea, 2004) connecting terms to each other and to other knowledge bases.