Several coding standards and data exchange formats, i.e., OmniClass, UniFormat, and COBie, were developed to address this issue. The de - velopment 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 Computer- ized 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 man - agement. 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. A Sustainable Data Dictionary Management System (SD- DMS) To Connect the Dots In response to the presented issues, we propose a Sustainable Devel - opment 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 proj- ect 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 variousAI- and ML-enabled algorithms to evaluate data health from different aspects, such as semantical analysis (fuzzy analy- sis), 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
Figure 2: components of an SDDMS
support sustainable design and operation. The SDDMS’s data require- ments for each building element include (1) sustainability-related attributes, (2) location properties, and (3) correlations and synergy
information, as illustrated in Figure 2. Sustainability-Related Attributes
Several property criteria are tracked and designed based on a proj - ect’s performance development goals during the early design stage. These can include a window’s thermal transmittance, thermal resis - tance, 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 track - ing 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 Location properties include (1) geo-spatial information (i.e., site, building, floor, room, area, and volume); (2) space usage; and (3) oc- cupancy information (i.e., occupancy type and density). This informa- tion 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 ac- ceptable 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:
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