Building information models (BIM) offer a multi-dimensional information structure and visualization tool for design, construction, and operations. The U.S. Department of Defense (DoD) has committed to applying BIM information standards and technologies for new construction and major retrofits. U.S. Army Engineer Research and Development Center - Construction Engineering Research Laboratory analysis shows roughly 3.5% of all U.S. Army facilities designed and constructed between 2007 and 2012 have met the new BIM requirements. All three DoD services now require BIM for new facilities. However, while BIM is revolutionizing the new construction process, the DoD is missing a large opportunity to reduce the total cost of ownership for its existing facilities by applying BIM to the long-term operation of their entire portfolio.

The objective of this project was to demonstrate that a retro-fit BIM can be produced cost-effectively, and that this standardized information model of the building and its assets can provide essential context to analyze and visualize data collected from control systems.

Technology Description

The model-driven energy intelligence (MDEI) pilot made use of Honeywell-developed tools for generating building models and integrating data and contextual information into the model.

BIM Builder

BIM Builder imports computer aided design (CAD) (two-dimensional vector graphics), and allows a user to selectively choose elements from those CAD drawings to generate a medium-fidelity three-dimensional model of the facility, spaces, and related equipment, semi-automatically. BIM Builder and the resulting models are not meant to compete with tools such as Autodesk for new construction, but to address an unmet need for operations.

Automatic Context Discovery for Control Information Integration

Most building management systems produce a vast amount of information about the performance of buildings and equipment, including sensor data, equipment status, and scheduling. However, that data can be difficult to access for analytics due in part to the lack of supporting context, such as the proper name of the asset or the part of the building it affects. Quickly and cost-effectively finding and contextualizing the valuable data points has been a limiting factor to developing analytics tools that can deliver new value from data generated through digital control and monitoring of equipment.

The Auto-Context application leverages patented algorithms and techniques to classify and map digital control system (DCS) points to common categories and to identified assets (equipment) using a partially-automated process which leverages semantic standards. Algorithms identify and map the unique tokens in the local naming convention, according to domain rules. The results are queried against a standard set of system aspects to find the best matching description of the point and its role. The process is robust to most control system types, so long as some human readable description or name is present for processing. The result is a scalable solution that reduces the manual effort of contextualizing the raw telemetry from an existing system.

MDEI User Interface

The facility manager or energy manager typically uses a vendor-specific console to access process trend data, and they can also turn to CAD drawings or their recall of a facility, or in rare cases, a complete BIM. While these sources of data have been available to energy managers for some time, they can rarely be referenced simultaneously for a complete picture of building operations or for easy, regular viewing; it is not typical to have on-demand access to integrated information.

Using the physical information model provided in the BIM and attaching the operational information provided by the control system (through the Auto-Context discovery process) can provide rich information about how and why a building performs as it does. The MDEI User Interface (UI) provides the navigation environment that the energy analysts and EM utilized to access BIM and energy information.

By providing a means to put information about energy performance in context with the building structure and heating, ventilation, and air conditioning (HVAC) design, root causes for anomalous behavior can be more easily understood. Combined visualizations reveal the behavior of specific systems or subsystems that are affecting energy use in the facility.

Demonstration Results

This project sought to drive the variable costs (labor) of producing a BIM to $1.00/100 square feet (ft2). The results indicate that an average cost of $0.50/100 ft2 can be achieved and that, as building size grows, this cost goes down. In the hands of a trained user, BIM Builder has the potential to generate a BIM for an arbitrarily large facility in a relatively fixed amount of time (2-4 hours on average).

The Auto Context tool significantly reduced the manual effort of contextualizing raw telemetry from an existing system. Using this partially automated mapping method, the project team was able to complete the mapping of 847 points in under two hours, exceeding the performance goal. This lowers the barrier to utilizing more legacy data in the future.

The full MDEI system was installed at Fort Jackson, South Carolina, and made available to the energy manager there. Monitoring was conducted for one full year between September 2013 and September 2014.

Detailed review of energy performance and the associated equipment behavior has led to the following conclusions about the energy savings potential on the subject facilities.


Simultaneous heating and cooling

Continuous Operation

Incomplete Shutdown

Building Number





Terminal unit behavior, including reheat and overall system operation

RTU behavior, and gas use for heating in vehicle bays

Scheduling anomalies


  • Improved scheduling
  • Retrofit improvements to air delivery to principal working space
  • Improved scheduling
  • Additional automation on vehicle bay IR units
  • Improved scheduling

Potential Savings (%)




Annual Savings (kWh)




Annual Savings (MBTU)




IR = infraredkWh = kilowatt hourMBTU = one million British thermal unitsRTU = roof-top unit

Feedback from the energy manager at Fort Jackson was positive; however, the circumstances of the deployment environment made it inconvenient to regularly access the data and integrate the tool in the normal workflow. These difficulties stem largely from the isolation of control systems on dedicated networks, and the difficulty of information integration across network boundaries.

Implementation Issues

BIM receives a great deal of attention for new construction, but the real asset management problem is the more than 90% of existing facilities, many of which will be managed for another 50 years. Long-term management of this information resource, both for newly constructed facilities and those with legacy data sources, represents a large gap in the understanding of how to better enable the Directorate of Public Works or the U.S. Army Corp of Engineers to keep information about facilities current and accurate with respect to facility and equipment condition. Aside from specific technology gaps, work processes and data management processes must also be addressed.

The availability of information about the real-time operation of facilities is another significant barrier to widespread application across DoD. Network compliance barriers, information security policies, and a lack of instrumentation at many facilities will have an impact on the widespread deployment of energy monitoring solutions such as MDEI.

As DoD modernizes information management across its installations, there are opportunities to design for data quality and data management to support the long-term development and management of information resources so that they can be more readily exploited for further benefit to facility managers.

  • ESCO ,

  • Decision Support Tool ,

  • Energy Modeling