BIM for Infrastructure: A Focus on Asset Management

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Champion(s)

Will Duke

Spy Pond Partners

[email protected]

Louis Feagans

InDOT

[email protected]

Trish Stefanski

MnDOT

[email protected]

Buffy Conrad

MDOT SHA

[email protected]

410-441-0495

Others Supporting Problem Statement
Please add at least one supporting organization.
Potential Panel Members
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BIM for Infrastructure: A Focus on Asset Management


Funding

$500,000

Research Period

18-24 months

Description

Research is needed on the importance of data governance from the conception of a project’s data dictionary, through the inventory and condition assessment and continuing with the data management and integration into transportation asset management systems. A question worth pursuing is whether all aspects of language, wording, numbering, and measurement units should be standardized or if template guides could be developed for each agency to standardize their unique asset type requirements, but in a nationally recognized format for easy translation.
After establishing governance routines for asset data collection and management, the next phase of research would involve the security aspects of an agency’s data as well as the quality assurance measures applicable to grow confidence in the data’s quality. A full review of best practices for data security procedures could break the barrier of IT to asset manager. Additionally, once definitions and governance procedures are established, the quality assurance process becomes more stream-lined and gives better confidence to the decision makers.
Asset managers know the data they need, and the data collection methods have been identified. What is needed is guidance on how to use the available data collection methods to meet the needs of asset managers.
BIM standards need to account for the fact that we have less data on existing assets than newer assets. However, it is existing infrastructure that has the most needs.


Literature Search Summary

● FHWA - Identifying Data Frameworks & Governance for Establishing Future BIM Standards
● AED80 has a subcommittee on BIM, who has a sub-sub committee on BIM & AM
● PIARC TC 3.3 has a group working on TAM/BIM integration.
● NCHRP Report 831: Civil Integrated Management (CIM) for DOTs.


Objectives

● Guidance on establishing BIM data governance and quality standards to support asset management.
● Recommend standards for data transfer between data collection and asset management systems.
● Develop maturity scales for BIM implementation and establish appropriate maturity level for integration of TAM
● Research on BIM applications to support DOTs' data governance specific to the collection of data by one part of the agency can be used directly by other parts of the agency
● Evaluate cost effectiveness of collecting and managing data through BIM at a sufficient level of quality.
● Aligning the focused but detailed project-level data with network-wide but less detailed TAM data.


Urgency and Potential Benefits

Please describe the urgency and potential benefits of the project.

Implementation Considerations

Please provide implementation considerations for the project.

Champion(s)

Will Duke

Spy Pond Partners

[email protected]

Louis Feagans

InDOT

[email protected]

Trish Stefanski

MnDOT

[email protected]

Buffy Conrad

MDOT SHA

[email protected]

410-441-0495


Others Supporting Problem Statement

Please add at least one supporting organization.

Potential Panel Members

Please add at least one potential panel member.

Person Submitting Statement

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Notes

TRB Research Ideas – Data Quality/Standardization
• Data quality and confidence
• standardize terminology between different systems so singles source can inform GIS/500 reports/DELPHI/FMIS etc. so reports all use the same words or numbers the same way
• Updated asset type definitions and extraction methodologies.
• Performance Metrics for Assets other than pavement and bridge, i.e.. signals, signs, barriers, culverts
• Asset ratings biases, potential to rate lower to obtain funding

TRB Research Ideas – Data Governance
• Our largest challenge is data governance, feature collection and maintaining asset/inventory data
• Data governance is still looming large from an implementation perspective
• Data history, implementation and its security (both cyber and other forms of security)




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