# BIM-to-BRICK: Using graph modeling for IoT/BMS and spatial semantic data
interoperability within digital data models of buildings

Filippo Vittori a,b, Chuan Fu Tan d, Anna Laura Pisello* a,b,
Adrian Chong c, Clayton Miller* c aDepartment of Engineering, University of
Perugia, Perugia, PG, Italy
bCIRIAF – Interuniversity Research Centre, University of Perugia, Perugia, PG,
Italy
cCollege of Design and Engineering, National University of Singapore,
Singapore
dOpen Blue Innovation Centre, Johnson Controls International, Singapore
∗Corresponding Authors:
<EMAIL_ADDRESS>+65 81602452
<EMAIL_ADDRESS>+39 339 692 7839

###### Abstract

The holistic management of a building requires data from heterogeneous sources
such as building management systems (BMS), Internet-of-Things (IoT) sensor
networks, and building information models. Data interoperability is a key
component to eliminate silos of information, and using semantic web
technologies like the BRICK schema, an effort to standardize semantic
descriptions of the physical, logical, and virtual assets in buildings and the
relationships between them, is a suitable approach. However, current data
integration processes can involve significant manual interventions. This paper
presents a methodology to automatically collect, assemble, and integrate
information from a building information model to a knowledge graph. The
resulting application, called BIM-to-BRICK, is run on the SDE4 building
located in Singapore. BIM-to-BRICK generated a bidirectional link between a
BIM model of 932 instances and experimental data collected for 17 subjects
into 458 BRICK objects and 1219 relationships in 17 seconds. The automation of
this approach can be compared to traditional manual mapping of data types.
This scientific innovation incentivizes the convergence of disparate data
types and structures in built-environment applications.

###### keywords:

Digital twin , Building Management System , Automation , Occupants’ behavior ,
Data interoperability , Building operation

††journal: Automation in Construction

##  1 Introduction

Building management entails operating various systems and governing different
data sources about operations, monitoring, maintenance, energy management,
auditing, occupant comfort, safety, and security. Central to these processes
is the Building Management System (BMS), an overarching computer-based control
system that relays information from different building subsystems. It provides
data from the core operational systems of a building to monitor, configure and
regulate those subsystems. Internet of Things (IoT) devices can complement and
support these efforts as portable sensors allow for ancillary functions and
granular monitoring of facilities operations. Recently, Human-as-a-Sensor
(HaaS), serving as a supplementary data source, has shown effectiveness in
evaluating and optimizing the human comfort of the building [1]. These can be
in the form of digital surveys on wearable platforms that can provide direct
and more accurate feedback on various environmental, physiological, and
psychological parameters [2, 3]. Furthermore, Building Information Modeling
(BIM) can supplement the as-mentioned data with geometrical, spatial, and
contextual information extracted from a 3D model.

The aggregation of these data sources in a collaborative process can be
expressed as a digital twin, a concept that was brought to light for the first
time in 2015 [4] when NASA’s engineers and scientists created two identical
capsules. One of the capsules was sent to outer space, while the twin was kept
on earth to simulate the behavior and monitor the situation of the sent
apparatus. Currently, there is no standard definition of the _digital Twin_
concept as it depends on the context [5]. The various existing definitions are
related to specific characteristics derived from multiple use cases. The
understanding of _digital twins_ has continuously changed during its
development [6]. Although the definition of the concept is still under
development, the topic is gaining momentum and rising interest as one of the
multiple forms of the current digital transformation. Digital Twins are used
in several fields. Already in 2015, Rosen et al. [4] investigated the
inherence of the topic within the manufacturing field, focusing attention on
Digital Twins’ potential within the context of Industry 4.0 and CPS (cyber-
physical system) modularity. This kind of approach is probably the most simple
and immediate, but it represents only the baseline within the framework.
Intelligent transport systems represent other methodology applications,
providing an example of a different conception of targeted entities: not only
objects can be mirrored and become part of a Digital Twin. Rudskoya et al. [7]
introduced the concept within Intelligent Transport Systems to solve the main
problems of the transport network and effectively develop it. In this context,
the purpose of the digital twin adopted emerges from the building industry’s
demands to monitor and control assets closely, providing real-time insights
into a building’s performance and events [8]. It increases the dimensionality
of information by being able to relate to data in a representative manner
visually and better improve the operations and performance of various
activities in the building’s lifecycle [9, 10, 11]. In achieving such an
integration, better interoperability of those heterogeneous data sources is
necessary but often lacking in existing buildings. Information on point naming
conventions in BMS, drawings, IoT devices, and other details are typically not
standardized and hard to interpret without first-hand knowledge [12, 13]. This
situation has also led to other issues, such as becoming a significant barrier
to the deployment of building applications for automated commissioning, fault
detection, and diagnostic and optimization of operations [14, 15].

Often, a manual and time-intensive process of mapping and interpreting the
necessary data for these software tools has to be carried out [14, 16, 17].
This has led to hesitancy, delays, and additional costs [18] [19] in adopting
more innovative, energy-efficient, and optimized building operations, which
can mean significant undesired consequences by the infrastructure landscape on
a national or global level [20, 21]. Easing the process of adopting national
and international standards that define entities and concepts in
interoperable, machine-readable semantic models for buildings is thus critical
to minimize this issue. Doing so can also facilitate and encourage the
adoption of more intelligent, modern technologies, increasing cost-savings for
building management in the long run, [22]. As such, this project aims to
create a convenient process for buildings to onboard one of those standards
that enable data interoperability and serve as a common platform for the
buildings’ metadata. A plugin in Revit has been created to convert BIM to
BRICK schema, automating the aggregation and mapping of all the available
information. It has been successfully demonstrated on SDE4, a National
University of Singapore building, combining its architectural, mechanical
systems, human comfort, and BMS information.

###  1.1 Data integration in buildings

Moving towards more holistic and innovative management of buildings requires
integrating data from diverse sources relating to their operations and space.
It presents a need to eliminate silos of building information, such as from
BIM, mechanical systems, floor plans, IoT devices, BMS, and occupants, to
create a single pane of glass perspective of its operations and lifecycle
processes. As such, data interoperability is an essential enabler to this
effort by harmonizing contextual information of the different data domains and
bringing them to a shared environment. This unification can allow a virtual
representation of the building in a digital twin representation. In this
regard, semantic web technologies like ontologies and resource description
frameworks (RDF) can facilitate the standardization of entities, concepts, and
relationships to realize a shared understanding of knowledge in a particular
domain [23, 24]. The semantic information and standardized syntax can be
embodied in knowledge graphs, represented in their nodes and edges. Its usage
provides an avenue for creating Linked Data [25] through integrating and
extending to other heterogeneous information and ontologies.

Given its potential in the built environment, several building-related
ontologies using semantic web technologies [26] have been introduced in the
recent decade, such as Project Haystack [27], Building Topology Ontology (BOT)
[28], RealEstateCore [29], Digital Twin Language [30], Smart Appliances
Reference Ontology [31], and BRICK schema [32]. Within this work, the BRICK
schema is chosen because it includes a tagging system similar to Project
Haystack that augments tags with formal semantic rules that promote
consistency and interoperability, it can be easily integrated with other
ontologies (i.e. Building Topology Ontology and Smart Appliances Reference
Ontology), and it is enriched with dedicated classes to represent occupants.
In addition to the cited reasons, BRICK enhances the IFC capabilities of
relationship description. Brick builds upon prior work and introduces a number
of novel concepts. Brick uses easy-to-understand Tags and TagSets to specify
sensors and subsystems in a building [33]. Garrido-Hidalgo et al. provide a
solid example of the integration of BRICK with other models, interlinking the
overall BRICK data model to five other ontologies. Further noticeable
integration involving the BIM framework is carried out by Zhu et al., who
focus the research on information models for highway operational risk
management based on the IFC-Brick schema [34].

###  1.2 Current approaches to data integration

Before syntax and metadata of building operations can be expressed in one of
these formal ontologies and specifications, a transitional process from their
existing representations is required. Formulating semantic graphs for building
operations is often challenging in having to infer from existing metadata that
often lack consistency or are based on arbitrary naming conventions [35, 36].
For instance, building management systems (BMS) point labels are a primary
source of building operations metadata. Even though there are existing open-
source tools such as OpenRefine [37], a significant manual effort to map them
to RDF representation can still be required due to several irreconcilable
definitions [38]. The manual inference process requires understanding the
context of each entity in the building and relating it to its associated sub-
system and spatial information. This can be further exacerbated by including
data from standalone IoT devices or disparate sources such as human comfort
parameters from HaaS. Current efforts in unifying operational and spatial
information in some of these ontologies often involve an additional process of
manual cross-linking, mapping, or inserting a common identifier of the
different datasets [38, 39, 40, 16]. For instance, Chamari et al. developed an
approach of integrating Industry Foundation Classes (IFC) and BMS as RDF [39].
However, a mapping table containing all the entities and a text file defining
the relationships has to be created to facilitate the process.

Farghaly et al. presented a method for integrating building-related assets,
Linked Data, and BIM. To cross-link the different data sets, the authors
manually map the other assets to their related information. The reliance on
human reconciliation of these buildings’ assets is further highlighted by
Weimin et al., who pointed out that existing point names and other point
attributes of buildings were not easily decodable by machines [38]. In their
efforts to combine data from Sustainment Management System with Revit, Loeh et
al. have to manually enter a common identifier for each component of BIM [41].
Manual rework is also necessary for Dave et al. to integrate IFC with IoT
devices through Open Messaging interfaces [42]. Without standardized export
guidelines for the web, it is tedious to reconcile objects in IFC with sensor
data. These manual interventions can add additional complexity and be
susceptible to errors in how they are implemented.

###  1.3 Objective and scope

To provide a more intuitive and automated conception of a standard metadata
environment, this work introduces a process to generate an RDF graph of all
available spatial, geometrical, mechanical, occupants, and operational
information in the BIM of a building in an automated manner. It eliminates the
need for the extensive and manual transitional and multi-layered integrative
process employed with current methodologies. The result of this work is a
convenient plugin created in Revit using Dynamo to automatically infer and
extract all the included entities, their relationships, and data availability
and subsequently construct an integrated knowledge graph. Moreover, with the
inclusion of the BIM authoring software identifiers, a bidirectional link
between Revit and BRICK is included in the generated model. This helps in the
association of BRICK data back to Revit for relaying any changes or updates to
building operations. The requirements to be addressed by the developed plug-in
regarding information collection involve the objects created directly both
inside the targeted authoring BIM platform and gathered from external sources
and associated relationships. A crucial requirement for the methodology is the
capability to access a targeted ontology directly inside the environment to
translate the data structure deriving from the aforementioned sources. The
main requirement for the developed software is to eliminate the manual work of
converting the information collected into the targeted ontology-based
resulting model and generate an ontology-based output to be visualized in a
dedicated viewer. Furthermore, the ability to update the generated ontology-
based model by simply re-running the application and to automatically include
in the generated ontology-based output relevant information and parameter
values to unequivocally relate every single object to the BIM version of it
represent additional features included to streamline the process and improve
the obtained framework proposed.

In summary, the final objective is to streamline the process of linking
metadata generated in BIM to RDF-based data comprehensive environments by
automating the generation of an RDF graph (BRICK-based) and constructing an
integrated knowledge graph. Furthermore, the objective aims to establish a
bidirectional link between the BIM source and the BRICK model, to enable
efficient communication and updates between the two frameworks. To accomplish
this objective, the first task performed within this work is a preliminary
analysis of the modeling capability of the targeted BIM authoring platform,
both in terms of punctual objects and the relationship between objects (i.e.
logical relationship between elements, system modeling capability, the spatial
distribution of objects, etc.). The second step performed involved the
analysis of possible ways to integrate additional sources and external sources
of data inside the authoring platform. The third step of the work was focused
on the analysis of possible ontologies to standardize and embrace in a
comprehensive environment the data treated within the BIM (and external
sources) environment. The fourth step performed consisted of a comparative
analysis of the current possible ways of translating and linking the data
between the targeted BRICK ontology and the BIM environment, with manual
conversion and no link possibility as a result of the analysis. Stemming from
the listed tasks, the last task of the work was generating a procedure and a
new methodology supported by specifically designed tools (BIM-to-BRICK) to
accomplish the aforementioned objective. Figure 1 represents a visual
structure of the task sequence.

Figure 1: Main tasks performed to accomplish the final result of the current
work

The paper is organized as follows: Section 2 introduces the proposed
methodology for the process and add-in creation in translation between BIM and
its corresponding BRICK model. Section 3 presents its application and
evaluation of a study building, and Section 4 performs a discussion of the
main findings. Eventually, Section 5 reports on the conclusions and remarks.

##  2 Methodology

This section outlines the main features of the methodology proposed to create
a direct bi-directional relationship between a BIM model and an associated
BRICK model. The chapter begins by giving an overview of the introduced
workflow, then the add-in BIM-to-BRICK is described in further detail.

###  2.1 General description

The main aim of the proposed work is to develop a methodology (and associated
tools to make it applicable) focused on the automated process of convergence
between the BRICK and BIM data structures – a process that manually would take
huge amounts of manual effort. The methodology is developed using Autodesk
Revit, a commercial building information modeling (BIM) platform provided by
Autodesk. Dynamo, a visual programming plug-in for Autodesk Revit, and its
embedded CPython engine are used to generate the BIM-to-BRICK add-in, which
converts a BIM model to a BRICK model directly. The general workflow is
exemplified by Figure 2, which consists of four main phases. In Phase 1, the
support BIM architectural model is created, then a second BIM MEP (Mechanical,
Electrical Plumbing) model is generated in Phase 2. The architectural model
defines the geometrical aspects of the building, identifying the main
component such as floors, walls, windows, and rooms. The BIM methodology
allows every object within the project model to be enriched with information
that characterizes the elements. The data can be related to geometrical or
quantitative aspects and then be used to identify relationships between the
components.

Figure 2: Main phases of the workflow for BIM-to-BRICK model generation

A link between the two entities is established to coordinate the development
of the baseline for the digital twin. Different models allow users to focus
specifically on the targeted discipline, enabling a more productive real-time
collaboration and more organized information in independent environments.
Complementing the architectural model, the MEP model includes system-related
data such as ducts, VAV, fan coils, and thermostats into BIM. BMS-related
information is inserted as part of the MEP model with custom Revit families
developed explicitly for this work. The parameters of the custom families
allow users to insert the relevant data such as an identifier (fundamental for
establishing a one-to-one relationship between the BIM and BRICK framework),
hosting room, time series identifier (useful to access external data), and
master panel name. The created parameters aim at identifying the roles,
associations, and information of the BMS points. In Phase 3, as part of the
MEP model, occupants-related information in external datasets is added to the
BIM environment. Such data can be obtained from wearable devices and thanks to
human comfort surveys. Since the occupants do not have a physical
representation within the BIM model, this external data is programmatically
accessed using Dynamo. A single environment with the required information from
architectural, MEP, and occupant-related data is established at this stage. In
Phase 4, BIM-to-BRICK, a Revit add-in to convert the BIM model resulting from
the first three phases into a BRICK model is created. A Revit identifier is
included for all the entities in the delivered BRICK model, allowing users to
generate, modify and update the result at any moment along the development of
the digital twin.

###  2.2 Add-in description

BIM-to-BRICK is the add-in application that can be executed to conveniently
convert a BIM model into a BRICK model, generating an optimized and user-
friendly workflow. Compared to existing processes of referring and translating
from the BMS point labels or creating a table of the different entities and
relations, it automatically infers their metadata from its location and
associations in BIM. To ensure the scalability of the provided tool, it is
created using Revit API and built-in parameters. The Built-in parameters, or
OOTB (Out of the box) parameters, are provided by default within a project or
family file. Thus, they cannot be changed or deleted when this framework is
employed. This feature is included to improve the robustness and resilience of
the tool, which can thus be used from multi-language authoring platforms to
exchange information consistently.

####  2.2.1 Integrating BMS and systems information

This methodology relies on default Revit components and custom Revit families
to integrate BMS information inside the BIM environment. The Revit database is
organized into four levels of hierarchy: category, family type, and instance.
A Revit family is a set of elements in a specific category. It is not only a
component of the project, but also the carrier of various additional
parameters and information [43]. The default BIM components of the
architectural model provide basic information about the building that will be
defined in the resulting BRICK model, such as the name of the building, its
floor levels, and the general context for the construction site. In addition
to the elements provided in the architectural model, ducts, air terminals,
spaces, and associated HVAC zones are added in the MEP model. These default
elements are essential to create the context for the BIM-to-BRICK add-in to
understand the layout and distribution of the BMS system. The core information
about the BMS entities, such as element relationships, time series
identifiers, and control logic, is then integrated with custom families.
Within this work, three representative objects are generated to address the
requirements of a basic BMS system: Variable Air Volume box (VAV), Fan Coil
Unit (FCU), and Thermostat. The developed elements can be enriched with BMS-
related information. Data connectors, a native tool of the used technology,
allow users to define the link between the used components. An example can be
represented by a VAV box controlled by (and thus, linked to) a thermostat
inside a generic room. In other words, connectors allow expression in terms of
Revit API, the control logic between the BMS elements. VAV and FCU families
exploit the rectangular duct connector to link to the duct net. All the
families are equipped with room calculation points used within the Revit
project environment to calculate the location of the single component inside
the building. This information helps infer how the HVAC zones are related to
the rooms and other BMS components.

The developed elements can be enriched with BMS-related information. An
example can be represented by a VAV box controlled by (and thus, linked to) a
thermostat inside a generic room. In other words, connectors allow expression
in terms of Revit API, the control logic between the BMS elements. VAV and FCU
families exploit the rectangular duct connector to link to the duct net. All
the families are equipped with room calculation points. Room calculation
points are used within the Revit project environment to calculate the location
of the single component inside the building. This information helps infer how
the HVAC zones are related to the rooms and other BMS components. The custom
families host custom project parameters within the project MEP environment.
Project parameters are containers for information defined by the user and then
added to multiple categories of elements in a project. These parameters are
defined explicitly during this work to allow users to easily input information
regarding the components and time series identifiers that can be embedded in
the resulting BRICK model. Figure 3 shows an overview of all the elements,
properties, and relationships involved in the proposed methodology:

Figure 3: Programmatically inference of elements, properties, and
relationships of BMS entities in BIM

####  2.2.2 Integrating occupants’ information

Integrating occupant data within a digital twin of a building invest a pivotal
role and highlights the importance of this approach in enhancing building
performance, occupant comfort, and overall sustainability. By considering
occupant behavior, preferences, and needs, the digital twin can provide
valuable insights for optimizing building design, operation, and energy
management strategies. The integration of occupant data in the framework not
only enables a more accurate representation of real-world conditions but also
facilitates data-driven decision-making processes for building stakeholders.
In the current methodology, occupants-related information is stored within an
external dataset, and occupants do not have a physical representation inside
the existing models. Intensive longitudinal methods have begun to emerge as a
way to characterize occupants for various built environment objectives [1].
This section of work focuses on integrating intensive longitudinal data within
the BIM-to-BRICK conversion workflow. The procedure’s first step builds upon
the BRICK schema’s extension to represent the metadata of occupants provided
by Luo et al. [44]. The instances of the occupant data are programmatically
generated from the external dataset to BIM. For each occupant added to the
model, the procedure defines the general information of the subject according
to the extension of the BRICK schema for occupants (i.e., age, gender,
ethnicity…). The second step of the procedure focuses on finding the
occupant’s location inside the building. The provided methodology is intended
to convert the Geographic Information System (GIS) automatically coordinates
system (latitude-longitude) to the local coordinate system (X, Y, Z) to define
the spatial representations of each entity directly within the Revit
environment. A preliminary manual process is required to find the relative
roto-translation and the scale difference between the actual construction and
the modeled building. In addition, the source database is supposed to indicate
the altitude of the measured point to locate the latitude and longitude
coordinates in a three-dimensional space. When the underlined preliminary
requirements are met, a python routine based on the UTM python package [45],
included within the BIM-to-BRICK add-in, links the collected latitude-
longitude data to the Revit database. The occupants’ location is represented
as a point object in the Dynamo environment. This solution allows the user to
identify geometrical relationships between the occupants and the building
components (i.e., rooms, HVAC zones…) without creating redundant BIM objects.

####  2.2.3 BIM-to-BRICK software description and UI

The methodology described in Section 2 is presented as a convenient Revit add-
in called BIM-to-BRICK. BIM-to-BRICK can generate a bi-directional link
between BIM and BRICK models. Figure 4 shows the developed code embedded
within BIM-to-Brick:

Figure 4: High-level schematic of the Dynamo canvas showing the BIM-to-BRICK
developed logic

The add-in is developed inside Autodesk Dynamo by combing pre-set nodes with
custom-developed Python nodes. The developed result can be discretized in four
main groups of performed actions, as shown in Figure 4:

  1. 1.

Data collection from an external database

  2. 2.

UTM conversion, translating latitude-longitude-altitude coordinates to (x,y,z)
coordinates

  3. 3.

Collection of data from the Revit database

  4. 4.

Interpolation of data and final BRICK model generation.

The generated link can be defined as bi-directional because every object
existing in the BIM model and included in the conversion obtains a traceable
BRICKified analog at the end of the process. Every element in a Revit model
has a unique element identifier used to identify elements in the model. The
created BRICK elements preserve the ID deriving from the BIM model, allowing
the user to trace every single created BRICK object back in the source BIM
model.

The main objectives of BIM-to-BRICK are:

  * 1.

Allow fast and efficient generation of BRICK models based on BIM models

  * 2.

Allow users to edit and review the BRICK model of a building promptly

  * 3.

Provide an intuitive tool to generate BRICK models

  * 4.

Provide a new link between the BIM and BRICK paradigm

  * 5.

Move a step towards the concept of digital twin

An integrated BRICK model associated with the modeled instances is generated
using the add-in described in Sections 2.2.1 and 2.2.2. No additional inputs
are requested from the add-in to create the BRICK model. A more complex BRICK
model of the building is obtained when all the custom parameters shown in
Figure 5 (BIM relationships) are filled with the required information. Figure
5 shows an overview of the BRICK classes and instances (including the
occupancy extension) obtained at the end of the conversion.

Figure 5: Overview of the BRICK classes, instances, and parameters considered
within BIM-to-Brick

BIM-to-BRICK adds to the basic functionality of Autodesk Revit with a
dedicated tab. The graphical user interface is divided into two main sections.
Starting from the left, a preliminary set of user-defined parameters is
provided. Then on the right, in the convert section, three push buttons allow
users to generate the BRICK model. The _BMS_ push button can create a BRICK
model involving all the building equipment and building management system
(BMS). Similarly, the _People_ push button can create a model of the occupants
involved within the given analysis. Lastly, the _DigitalTwin_ push button
combines the previous ones, obtaining a BRICK model of the complex system made
of building, occupants, and their interactions. Table I collects an overview
of the possibilities offered by BIM-to-BRICK.

BIM-to-BRICK modules capabilities
---
Module |  Involved objects
BMS |  Building equipment and Building Management System (BMS) of the targeted systems
People |  Dataset of occupants involved within the analysis
Digital Twin |  Building equipment and Building Management System (BMS) of the targeted systems, dataset of occupants involved within the analysis
Table 1:  BIM-to-BRICK different modules overview

A Terse RDF Triple Language (Turtle) file of the BRICK model is generated and
stored in a target folder by pressing one of the commands to execute the
process. Simultaneously, the add-in automatically proposes to the user
BrickStudio [46], a web-based RDF visualizer able to parse and generate a
visual representation of any remote RDF resource. Eventually, the turtle file
can be loaded in BrickStudio (or other BRICK rendering platforms) to obtain a
graphical representation. Figure 6 represents the developed graphical user
interface.

Figure 6: BIM-to-BRICK graphical user interface

##  3 Implementation and Results

This section describes the application of the provided methodology and the
implementation of BIM-to-Brick. The chapter begins by identifying and
elaborating on the main features of the building and database used. Finally,
the obtained results and potential future applications are identified.

###  3.1 Case study: SDE4 – The building and the systems

SDE4 is a building located in Singapore within the NUS School of Design and
Environment (SDE). This six-story building has an overall floor area of 8588
m², hosting several functions, including providing office spaces, lecture
halls, laboratories, and co-working environments. It consists of 86 zones,
many of which are naturally ventilated (NV), including transitional (e.g.,
corridors), semi-open spaces (e.g., the plaza and terraces), and service rooms
(e.g., toilets and storage). Other zones are either mechanically ventilated
(MV) or use traditional air-conditioning (AC). Many spaces adopt a hybrid
cooling system (HC) that employs both AC and ceiling fans with a design zone
set point temperature of 27 degrees Celsius and elevated air movement via
ceiling fans [47]. The building’s systems are linked and implemented through a
BMS to control and manage the system operations. SDE4 is a prototype of net-
zero-energy and sustainable design thinking in the tropics. The building has
been awarded a Green Mark Platinum certificate [48].

###  3.2 Database: Intensive Longitudinal Indoor Comfort dataset - People

This work stems from a longitudinal dataset from a previous study involving 30
participants. The subjects involved in the experiment expressed using the
Cozie application [2, 49] dynamic preferences about their thermal, lighting,
and noise-based perception [1]. The study also collected temporal data from
fixed IEQ sensors in the indoor environment (temperature, humidity, noise, and
carbon dioxide) and physiological data from the smartwatch (heart rate, near
body temperature). This study is one of several similar recent efforts to
collect longitudinal subjective occupant data in the built environment using
smartwatches [50, 51, 52] The delivered software uses the information
retrieved from the database to characterize the experiment participants in
terms of identifier, age, and gender. The BRICK classes used are the
_Occupant_ class and the relative _individual_ subclass. Class properties are
used to define the age and gender of the individuals [44]. To avoid non-
defined values deriving from the experimental campaign, the experiment focuses
on 17 participants. Occupants’ location is collected through a couple of
latitude and longitude coordinates via Bluetooth Low Energy (BLE) beacons.
Further descriptions of the experiment settings and data collection are
provided by Abdelrahman et al. [47]. The latitude and longitude measurements
are coupled with every measurement within the cited experimental study to give
the coordinates to be converted and locate people inside SDE4, according to
the conversion procedure described in Section 2.2.2. Figure 7 illustrates the
result of the processed locations of occupant information in Revit. Every blue
point represents a subject’s location at a given timestamp.

Figure 7: Occupant locations reflected as representative points objects within
the BIM model

###  3.3 BIM-to-BRICK: Application on SDE4 building

SDE4 building is used to demonstrate the proposed methodology to assimilate
its data with the longitudinal personal comfort dataset. The automatically
generated BRICK model harnesses the information input within the linked BIM
model of the building, consisting of the architectural and MEP discipline. The
file size of the architectural model is 30,760 KB, while the file size of the
MEP model of the building is 15,784 KB. The geometrical aspects and the
location of the objects inside the building are accounted for in the framework
of the architectural model. In contrast, the MEP model provides information
about the mechanical systems and related BMS controls. The longitudinal
database completes the framework with the required data associated with the
human side of the delivered result, leveraging the cited extension of the
BRICK schema to represent metadata of occupants. The combination of the SDE4
BIM model and the selected database allows BIM-to-Brick to generate the RDF-
based model. The add-in equips the user with the option to create three
different models depending on the information included in the conversion.
Within this first application of the procedure, all three options are
generated and investigated in the graph model visualizer. All the entities and
logical relationships involved in both the dataset and the models are
converted to the new RDF-based form, following the schema reported in Figure
5. The different information embedded within the models generates different
shapes and complexity of the networks. The “People” version of the model shows
a relatively small and straightforward chart involving only the
characterization of the subjects and the relationship with the experimental
environment generated within the RDF schema. This version embeds one building,
nine building levels, 48 rooms, and HVAC zones, and 17 individuals. The
generated code filters only the relevant subjects to be considered within the
analysis (as underlined in Section 2.2.2), obtaining the final considered
sample of 17 subjects. The resulting BRICK model is generated in 11 seconds.

Figure 8 shows the classes and relationships created by BIM-to-BRICK and an
overall view of the rendered BRICK model. The “BMS” version of the model shows
a more complex network involving the characterization of the mechanical and
related BMS system, also capturing the relationships between the two entities.
The information retrieved from the architectural BIM model is crossed with the
data deriving from the MEP discipline, allowing the systems to relate to the
building’s physical aspects. This version embeds one building, nine building
levels, 48 rooms, and HVAC zones, two fan coil units equipped with CO2 sensors
and thermometers, 14 fan coil units equipped with thermometers, and 52 VAV
boxes equipped with CO2 sensors, hygrometers, and thermometers. The resulting
BRICK model is generated in 15 seconds. Figure 9 shows the classes and
relationships created by BIM-to-BRICK and an overall view of the rendered
BRICK model. The _Digital Twin_ model shows the complete version of the BRICK
model that BIM-to-BRICK can provide. This solution involves all the components
deriving from the BIM models and the dataset, creating all the relationships
described in Figure 5. This version embeds one building, nine building levels,
48 rooms, and HVAC zones, two fan coil units equipped with CO2 sensors and
thermometers, 14 fan coil units equipped with thermometers, and 52 VAV boxes
equipped with CO2 sensors, hygrometers and thermometers, and 17 subjects. The
resulting BRICK model is generated in 17 seconds. Figure 10 shows the classes
and relationships created by BIM-to-BRICK and an overall view of the rendered
BRICK model.

Figure 8: A _People_ BRICK model generated by BIM-to-BRICK: a) A synthetic
view of the BRICK classes involved is automatically generated by the BRICK
Viewer, and b) Overall view of the rendered BRICK model within BrickStudio
Figure 9: A _BMS_ BRICK model generated by BIM-to-BRICK: a) A synthetic view
of the BRICK classes involved is automatically generated by BRICK Viewer, and
b) Overall view of the rendered BRICK model within BrickStudio Figure 10: A
_Digital Twin_ BRICK model generated by BIM-to-BRICK: a) A synthetic view of
the BRICK classes involved is automatically generated by BRICK Viewer, and b)
Overall view of the rendered BRICK model within BrickStudio

###  3.4 Potential applications of the SDE4 BIM-to-BRICK framework

The first application of the delivered methodology provides a BRICK model of
the considered parts of the SDE4 building, starting from the existing BIM
models. This sub-section underlines potential applications and related
outcomes of the obtained result.

####  3.4.1 Empowering the information of the models

The delivered methodology can provide a BRICK model starting from an existing
BIM model. BIM models are commonly offered within the design process of a
building because they are the environment in which designers of various
disciplines collaborate and create the final drawings needed for the
construction phase [53]. Creating a BRICK model from scratch is possible
thanks to understanding the project documents and the manual translation of
the information within the RDF-based language. With the provided methodology,
all the information contained within the BIM models is automatically
converted, obtaining the same result as what is accomplished with a manual
process. The logical relationships between the component of the BMS system can
easily be input by designers within the last phase of the design and
construction process, mitigating the need for highly specialized human capital
wholly dedicated to the task. Once the information is correctly set within the
BIM model, the conversion can be performed in seconds instead of days or
weeks.

####  3.4.2 Instant updating of the BRICK model and visual interaction

The created framework allows users to interactively link the information
present in the BIM and the BRICK model. A BRICK model usually presents itself
in two possible formats: a series of lines of code or a visual representation
(graph format). The BIM model equips users with 3D environments where
components can be easily visualized, located, and edited. Because of the
provided bi-directional link of the information, the object within the BIM
models presents a unique analog within the created BRICK model. The identifier
parameter plays a fundamental role, allowing users to focus on the same
element in both BIM and BRICK models. Consequently, both models can be
modified and updated smoothly. Figure 11 exemplifies the concept of the ID
parameter for a thermostat instance:

Figure 11: The ID parameter as the link of the two platforms: a) ID parameter
within the rendered BRICK model inside BrickStudio, and b) ID parameter within
the source BIM model

####  3.4.3 Standardizing the approach to extend the framework

The delivered methodology sees the first application to the SDE4 building,
empowering the existing BIM model. Still, the workflow and the related add-in
can be applied to every building information model set according to the
information shown in Section 2. The SDE4 can be the first case-study model to
use as a template for future implementation in other buildings.

##  4 Discussion

The proposed work opens new possible developments related to the integration
of the data deriving from the buildings’ design, construction, and operation
phases. This section discusses outcomes and the potential value for
researchers and professionals deriving from the workflow application and
related BIM-to-BRICK add-in. At the end of the chapter, limitations of the
current state of the framework are underlined and discussed, posing the basis
for future developments of the work.

###  4.1 Bridging data silos with common languages

The main aim of the current work is to define a linking workflow between BIM
models and BRICK schema. This effort moves towards avoiding silos of
information created by the difficulty of exchanging information generated by
different sources but with a common denominator: the considered object. Data
interoperability is thus a key component, and using semantic web technologies
such as resource description frameworks is a suitable approach.
Interoperability between different platforms and formats represents the
current main challenge to exploiting the vast amount of information that the
AEC (Architecture, Engineering, and Construction) industry is generating [54,
55, 56].

###  4.2 Automation of data model integration

This study leverages the capability of BIM to manage related data with
automation routines. The paradigm introduced with BIM changes the designers’
way of managing and authoring the design. Building a model of information is
not drawing. The drawing document is one piece of information that can be
extracted from the model. This approach opens new possibilities to generate
some parts of the design automatically. Several studies focus on the
possibility of automating repetitive tasks, reducing the time and resources
needed across all the design, construction, and operation processes [57]. The
control logic can be embedded by creating an automatic process, generating
workflows that augment the base capability of the authoring software used to
design the future building. New rules and features can be added [58, 59], and
new sources of information can be embedded within the resulting output [60,
61].

The digital twin concept is gaining momentum and rising interest as one of the
multiple forms of the current digital transformation [5]. The framework
outlined is inserted within the definition of a digital twin as a possible
answer to the research and industry’s demands to closely monitor and control
assets, providing real-time insights into a building’s performance and events.
The proposed approach wants to underline the importance of the capability to
convert information from one form to another, moving toward an inclusive,
dynamic environment. The integration between BIM models and other languages,
such as a graph, RDF, or GNNs, represents a further step in the digital twin
direction [62, 63].

###  4.3 Limitations of the methodology

The methodology and tool provided within the current work primarily
demonstrate how it is possible to carry out an efficient and rapid conversion,
establishing a two-way link between BIM and BRICK models. The current version
of the add-in is developed inside a proprietary platform (Autodesk Revit),
preventing at the moment direct usage inside other vendor’s platforms
(Bentley, Nemetschek, Trimble, etc.). The capability of Autodesk Revit to
manage and export IFC (Industry Foundation Class) models, including the
relevant information for the BRICK conversion, is the base reason for the
software choice to allow the development of the work in an interoperable
framework. The methodology can still be applied, but the add-in BIM-to-BRICK
should be replaced with alternative workflows. The considered classes and BIM
objects are limited to some examples that include critical features useful to
show the potential of the delivered workflow and add-in. Many other objects
and classes must be involved in the analysis to have a fully operable BRICK
model. Another limitation of the workflow is the usage of custom parameters
that let the users enter the required information to perform the conversion.
The code is break-down into standardized modules related to BIM, BRICK, and
external components, facilitating future developments of the code.

###  4.4 Future developments

Future developments will move further steps toward the interoperability of the
delivered framework. BIM-to-BRICK is developed inside Autodesk Revit to allow
the user’s authoring of the BIM model to be converted. This is the first step
needed to equip professionals and researchers with usable tools. Within the
interoperability framework, the next steps of the work will be focused on two
main areas. The first area is related to refining the delivered solution,
investigating the capability of the considered parameters to be quickly
included within the IFC export process in a standardized way. The second area
of study will further develop the Python-based created code with the final aim
of creating a parallel version of the add-in independent from the Revit
environment. This last deliverable will be focused entirely on the IFC format,
aiming at converting models from every source BIM platform. The capability of
the methodology to involve more complex systems is another field of needed
development. The interaction between BIM models can cover more than a single
building. The linking capability of the models allows users to create complex
systems of related buildings in terms of location, geometry, and information
[64]. The approach provided within this work can be enriched, including the
conversion and the managing of the information deriving from a complex system
of buildings. Furthermore, other data sources can be easily embedded within
the provided routines: one example is the BRICK extension of the BRICK schema
to represent metadata of occupants that, within this work, allowed the
inclusion of the subjects’ experimental data residing on an external database.
The provided methodology is conceived to be the first step toward expanding
the framework to more and more complex systems, following the lead of the
BRICK schema. In order to enhance the robustness and applicability of the
delivered methodology, we acknowledge the significance of incorporating
statistical analyses on future target samples involving real-world buildings
and experiments. These analyses can provide valuable insights and validate the
effectiveness of the proposed approach in real-world scenarios. In summary,
future developments will be directed towards creating environments that
prioritize interoperability, with a strong emphasis on incorporating the
Industry Foundation Class (IFC) format into the process and exploring the
possibility of utilizing the cloud as a direct host for both the model
components and the final BRICK output. The cloud-based approach opens up
opportunities for enhanced processing capabilities and further advancements in
the field.

##  5 Conclusion

Data interoperability is an essential enabler for building operations’
technology to scale and adapt by removing the reliance on customized
integration. Adopting a standard in both the buildings’ metadata definitions
and BIM can facilitate the deployment of more efficient operations of its
various lifecycle processes. However, current adoption is often tedious and
requires some degree of manual intervention and interpretation. In recognizing
that this is one of the critical bottlenecks in the push towards a unified
protocol, this project aims to provide a bypass to programmatically infer and
extract all available entities in the existing BIM of a building to formulate
its BRICK schema representation. The proposed BIM-to-BRICK work uses Dynamo, a
visual programming plugin in Revit, to access all entities and their
associations based on their location and system information within BIM. Upon
this, an inference strategy is followed to determine the various BRICK
entities and relationships required in building the BRICK model of the
building. This approach is successfully demonstrated on SDE4 with the objects
and associations of all available architectural, mechanical, BMS, and occupant
comfort information mapped to the BRICK model. It is designed to be a
straightforward and user-friendly tool for adopting such schema that can be
used in deploying various portable building applications. Extending this
approach to other schemas and standards should thus be possible by simply
changing the inference strategy in this approach. Increasing the accessibility
for onboarding an interoperable standard can pave the way toward more
efficient building management, leading to a more adaptable and sustainable
infrastructure outlook.

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