Figure 1: challenges & barriers in current sustainable practice in FM
In this five-part series on Practical BIM Implementation for Facility Management (FM), we introduced the 3×3 principle as the fundamental digital twin framework in the first article. The 3×3 principle framework breaks down the level of information needed to support different personas, associated tasks, and responsibilities. It provides an efficient and effective platform to facilitate what is required for various roles to perform their functions without overwhelming them. Next, we introduced the necessity of modular data analytics to cover various aspects of the facility’s performance. By coupling these module breakdowns with a scoring system, the performance of the facility’s facets can be easily tracked, monitored, and maintained accordingly.
In this article, we will dive into sustainability, a trending topic across the project delivery stage, which is viewed as complex because it involves competing criteria. We will also present the sustainability module, dissect its complexity and offer a potential solution to facilitate sustainable practices in FM.
Complications in Current Sustainability Practice in FM
Sustainable development is a difficult problem that involves multiple stakeholders, interrelated components, confusing systems, and competing criteria throughout the building lifecycle, from concept formation to operations and maintenance. Despite the complexity, sustainable development is a necessary path to ensure it meets present needs without compromising future generations. Various standards and the international efforts of the United Nations, the U.S. Green Building Council (USGBC), and Building Research Establishment (BRE), have led to the establishment of guidelines and certifications to assist with designing, measuring, and implementing sustainable development. While building technology’s advancement demonstrates tremendous opportunities to help the Architecture, Engineering, Construction, and Owner (AECO) industry from the design phase through the operational stage, it also presents several challenges and barriers to information and data utilization to support the sustainable development process. In addition to commonly-found issues of data quality, consistency, and interoperability during the project lifecycle, other major challenges and barriers are as illustrated in Figure 1.
Lack of Domain Knowledge
One of the significant hurdles to sustainable FM is the lack of domain knowledge. There are many barriers to sustainable facility management implementation, including lack of knowledge, senior management commitment, time and financial constraints, and lack of capability. However, insufficient knowledge is the most significant. Typically, facility managers do not have sustainable operations as part of their required domain knowledge. Compounding the situation is a severe scarcity of sustainability-related information to support facility management’s sustainability activities.
With the data disconnection issue impacting every stage of the building lifecycle, it becomes a main impeding factor to sustainable development. This is due to tool interoperability, the need for diverse domain expertise, different lifecycle activities, and lack of a central repository for sustainability-related data that winds up being scattered. The data disconnection between the design and analysis platforms creates a discrepancy between the analyzed data and the actual documented design. This disconnection between the design and construction models compromises the as-built information from the original design intent. The problem is further compounded by the data disconnection between as-built and FM models which costs extra time and effort for data collection. This loss of several important sustainability-related attributes hinders sustainable facility management activities.
Lack of Information Exchange Standards & Requirements
The amount of data and information involved in the AECO industry is growing exponentially and combined with this data explosion, creating a digital transformation era wave. In an ideal scenario, the increase of data and information can present several opportunities for cost and time reductions, promote human comfort, increase space usage, decrease energy consumption, and improve indoor environmental quality. However, due to the lack of data information exchange standards and requirements, these data sources cannot be effectively managed and leveraged, leading to inefficient decisions and processes.
Some examples of data-related issues include but are not limited to inconsistent naming, formatting, and data storage, ambiguous and invalidated data sources, and insufficient or irrelevant information. Several coding standards and data exchange formats, i.e., OmniClass, UniFormat, and COBie, were developed to address this issue. The development of the ISO 19650 series international standard was intended to regulate information management over the whole building lifecycle. However, none of the existing standards touch on sustainability-related attributes and requirements for streamlining the data transition from design to operations.
Lack of Data Mapping & Synergy
Coupling IoT and sensor technologies with Building Management Systems (BMS), Building Automation Systems (BAS), and Computerized Maintenance Management Systems (CMMS) are widely adopted as favorable facility management tools. The further integration with Artificial Intelligence (AI) and Machine Learning (ML) allows CMMS to provide preventive maintenance, fault detection and diagnostics, and various advanced capabilities to support facility management. While these solutions seem promising, without mapping the collected data to meaningful performance indicators or predefined operation strategies, they do not provide the information to support effective facility management. For instance, what does it means when a carbon monoxide sensor reads a value of 10 ppm? What are the predefined operational strategies? What are the related building assets that correlate with this reading? Is it good or bad when a light level sensor is reading 400i lux in terms of visual comfort? The answers depend on the space usage and standard of choice. These readings provide no value if missing the associated context. Moreover, one asset might correlate with different performance indicators. For example, increasing the outdoor air rate will improve indoor air quality but use more energy. This contextual information is critical to consider when choosing operational strategies and is often missing in current FM systems.
Figure 2: components of an SDDMS
A Sustainable Data Dictionary Management System (SDDMS) To Connect the Dots
In response to the presented issues, we propose a Sustainable Development Data Management System (SDDMS) to bridge the gaps and enable a smoother utilization of technologies. SDDMS is built on top of an existing Data Dictionary Management System (DDMS), which was introduced in an earlier article. It adds sustainability-related data requirements to enhance sustainability programs throughout the project lifecycle. The existing DDMS is an intelligent cloud-based solution developed to bridge the gaps between the project information model (PIM) and the asset information model (AIM) during the operational stage. It adopts various AI- and ML-enabled algorithms to evaluate data health from different aspects, such as semantical analysis (fuzzy analysis), grammar and spell check, completeness and uniqueness analysis, and classification consistency analysis. A key attribute of a DDMS is it applies cloud-based ML based on backend labeling to tagged asset classifications and attributes from different entities to establish a robust knowledge base for intelligent recommendations. The implementation of the DDMS has proven to be a fundamental and practical approach as a data management tool to support a range of stakeholders and activities beyond the operational phase as a central data management vessel throughout the project lifecycle. However, the existing DDMS has not fully addressed the need specific for sustainable development. Therefore, the proposed SDDMS focuses on each building element’s sustainability-related data requirements to extend the DDMS’s use to support sustainable design and operation. The SDDMS’s data requirements for each building element include (1) sustainability-related attributes, (2) location properties, and (3) correlations and synergy information, as illustrated in Figure 2.
Several property criteria are tracked and designed based on a project’s performance development goals during the early design stage. These can include a window’s thermal transmittance, thermal resistance, solar heat gain coefficient (SHGC), embodied carbon, recycle content, etc. However, these informative values are often lost during the operational phase and not integrated with the performance tracking platform. These attributes can be crucial information to support sustainable design, operation, and renovation strategies and bridge the lack of domain knowledge. The sustainability-related attributes can also provide decision support for facility managers to determine how to improve specific performance aspects, such as improving lighting fixtures’ efficiency to decrease lighting power density, lowering SHGC to reduce cooling load, or decreasing temperature set point to decrease the heating load.
Location properties include (1) geo-spatial information (i.e., site, building, floor, room, area, and volume); (2) space usage; and (3) occupancy information (i.e., occupancy type and density). This information is crucial to establish the correlation between the element and its location context for determining the performance thresholds for each building element. For example, a window with higher SHGC is acceptable in a storage room but not sufficient in an office area since a storage area has broader temperature tolerance than the office area. The space properties are the essential key to link building elements to their correlated performance criteria.
Correlations and Synergy Information
In the SDDMS, each element and its sustainability-related attributes should include the following correlation information:
- Associated Sustainable Key Performance Indicators (KPIs): Each element and its attributes might correlate to one or more KPIs and could positively or negatively impact others. Therefore, mapping every element and its attributes to their associated KPIs can provide a holistic perspective and trade-off studies to identify the optimized strategies during the design and operational stages, and bridge disconnected data and the lack of data mapping and synergy.
- Data Collection Project Phase and Responsible Stakeholder: For the SDDMS to be the central repository throughout the project lifecycle and bridge the gap from lacking information exchange standards and requirements, it is necessary to specify the project phase and responsible stakeholder of each building element and its attributes. This will prevent the data loss during each data exchange stage. Clearly defined data collection phases and ownership can also ensure the information is diligently collected and recorded with its corresponding purpose in the SDDMS without duplication and inconsistency.
- Supported Project Activities: This is where the correlation activities that the data support are specified. With this information, the SDDMS provides knowledge references to support activities during the project lifecycle and establishes the correlation to the sustainable KPIs. During the design stage, energy simulation data can, later on, support energy performance monitoring and management during the operational phase. This information can bridge the gap between data disconnection and the lack of domain knowledge.
- Associated Green Building Standards: This information establishes the data foundation for sustainable performance evaluation against the green standards of interest and promotes green standards as a sustainable development reference. The associated green building standard information can provide an overview of a project’s KPI fulfillment level to other major green building standards, facilitate the project to meet a specific green building standard, or establish the performance threshold as the benchmark for operational monitoring, as well as illustrate the synergy to other reference green standards.
- Associated Building Automation System (BAS) and Building Management System (BMS): This final step correlates each element’s attributes with the BAS and BMS. This information is essential to set up the control mechanism and logic of the BAS and BMS. It also provides the correlation mapping between sustainable KPIs and different controls of BAS and BMS. For instance, the lighting control system manages a lighting fixture operation, but it also impacts a space’s energy performance. Holistic control of a building requires consideration of each variation’s influence and trade-offs for KPIs. One value change might impact the performance of another. Therefore, data’s association to BAS and BMS in the SDDMS serves as the foundation for the advanced automatic control mechanism.
Figure 3: use cases of the SDDMS for energy and air quality monitoring
Example Use Cases
We present two use cases of the SDDMS for setting up an office space’s monitoring dashboard with two sustainable KPIs: energy use intensity (EUI) and carbon monoxide (CO) concentration, as illustrated in Figure 3.
Typically, the energy meter readings show the cumulated energy consumption of a specific space area during a set period. A monitoring system can continuously collect the data, save them as data historians, and plot the result on a real-time dashboard. However, without comparing to a set threshold to trigger either the facility manager’s action or automatic responses, the collected data is of no use to facilitate the building’s operation and maintenance. Both the energy meter’s reading and its metering space area are required to provide a comparable value as EUI to establish a proper performance threshold. The SDDMS can then provide a range of energy performance thresholds considering the space properties and its correlated green standards. Using EnergyStar as the benchmark standard, the EUI threshold of an office is 52.9 kBtu/sq. ft. This value can then be used to set the alert in CMMS to inform the facility manager or set up appropriate automatic responses. The CMMS or facility manager can then refer back to the SDDMS to isolate the building elements and attributes that impact the EUI value. The list can be further utilized to configure operation and maintenance strategies.
Similarly, for the use case of air quality monitoring, CO concentration is the KPI. A CO sensor reads 10 ppm and feeds the information to the performance dashboard. The SDDMS can then provide a performance threshold considering the space properties and correlated green standards. Since it is an office space, a healthy indoor CO concentration should be maintained within 9 ppm per WELL Building Standard. Therefore, the value can be used to set the alarm to inform the air quality condition and trigger corresponding actions accordingly.
To continue the previously-introduced digital twin framework, modular analytics, and scoring system, we present an SDDMS that can potentially bridge the gaps of lack of domain knowledge, data connection, information exchange requirements, and data mapping and synergy to facilitate sustainability in FM. While several essential information and data specifications mentioned in this article are not new, these data requirements are currently utilized and applied sporadically in different green building standards, building data exchange standards, and BIM implementation framework. While the development and adoption of ISO 19650 series presents potential benefits to the project lifecycle’s data management, an SDDMS would still be the essential foundation for supporting the data flow and activities throughout the building lifecycle, especially for sustainable development. The final installment of this series will summarize and discuss the promising integration of BIM, AM/FM LOD, modulization, health assessment, data analytics, reporting, and sustainability into one platform with generalized case studies to disclose its full potential and functionality to benefit end-users from all levels.