By Dr. Jeff Chen, George Broadbent, and Dr. Eve Lin of Microdesk with Kai Yin, and Jiayi Yan of ZhiuTech
In previous installments of this series, we tracked the journey of digital twin development from a high-level design philosophy to how to fulfill multi-stakeholders’ demands; design a modularized scoring system to evaluate building performance quantitatively; and implement a sustainable data dictionary that integrates the concept of sustainability and its thresholds into the scope of the digital twin. Although there is still no standard definition of the digital twin in the AECO industry, we can create a picture of it that contains:
- A digital replica of real-world built assets
- An adaptive and flexible data analytics and reporting platform to serve multiple-stakeholders
- A comprehensive platform that embraces and evaluates various aspects of the building
- A flow that integrates all data coming from connected data sources for targeted purposes/use cases
In short, the ideal digital twin will serve as the center-hub of data integration. This last installment of the series discusses the data integration of related channels, deciphers the end-user-oriented digital twin design approach, and introduces utilizing artificial intelligence (AI) to streamline data integration while providing a better user experience.
Before the deep dive, we need to discuss the critical and unavoidable topic that determines the final success of data integration – the Common Data Environment (CDE).
Common Data Environment (CDE)
The CDE has been primarily defined as the standardized source of information used to manage graphical and non-graphical data for a BIM program/project. In 2020, the series “New Era of BIM Lifecycle Implementation” discussed at length the importance of the CDE. It suggested using a Data Dictionary Management System (DDMS) to host a CDE which would support different stakeholders and enhance accessibility for various parties. Instead of focusing on the traditional scope of CDE highlighting the project delivery stage, it looked at bridging the gap between the Project Information Model (PIM) and Asset Information Model (AIM), or the project delivery and operational phases. These stages from the angle of the digital twin are still very BIM lifecycle-oriented.
Next, we are going to enlarge the definition and use cases of CDE to apply to a wider scope. With the expansion of our understanding of the BIM lifecycle and broadening the digital twin concept, we have moved outside the box to better understand the depth and width of a digital twin as the center-hub of data integration. Therefore, CDE use cases are not limited to the BIM project handoff but encompass every possible connection that may be integrated and collaboratively utilized. Several areas that will benefit from the CDE are presented.
Computerized Maintenance Management System (CMMS)
A Computerized Maintenance Management System (CMMS) centralizes maintenance information and facilitates the processes of maintenance operations. It helps optimize the utilization and productivity of physical equipment/assets. Today, a CMMS has a close collaboration with the BIM lifecycle. In most cases, it utilizes a translated, light-weight model as the 3D representation of its assets. Figure 1 presents an Autodesk Forge®-based model viewer embedded in IBM® Maximo.
With the integration of the 3D model viewer, several asset management/facility management (AM/FM) related records can be correlated directly or indirectly with the BIM model, such as Work Orders, Service Requests, Asset Meters, Location Meters, Asset Specifications, and problem/cause/remedy (PCR). This enables end users to conveniently click the view of the desired 3D object to query all connected records, or vice versa. Some of the data could be synced from the BIM model (e.g., manufacturer, serial number, or voltage), while others including work orders and service requests can be generated during the operational stage. However, how and when to collect the necessary data needs to be clearly defined, and then agreed upon, and regulated across the entire project lifecycle. CDE plays a vital role at this point in streamlining the entire process. Without a well-defined and executed CDE that regulates the data framework across the CMMS and BIM, the efficacy of data integration will be compromised. Similarly, space management systems, real estate customer relationship management (CRM) systems, enterprise resource planning (ERP) inventory systems, and other systems that could be implemented with the BIM lifecycle will all benefit from the existence of a predefined CDE.
Geographic Information System (GIS)
One current trend is the usage of GeoBIM, or BIM and geographic information system (GIS) integration, which is the process of “blending” the BIM model into layers of geospatial context (e.g., ESRI ™or Google Maps™). GeoBIM fuses geographic information and BIM design and construction data to the same platform to perform better targeted data analytics and reporting. GeoBIM enables users to smoothly switch between GIS and building views to aggregate related data on different levels, to make more precise and practical decisions. According to Cory Dippold, Vice President at Mott MacDonald, when GIS is engaged in the scope of the digital twin, CDE is often interpreted as Connected Data Environment, or Collaborated Data Environment. The metadata remains under the control of its appropriate host and is exposed through standardized connections for integration and access. In this circumstance, the standards-based CDE is essential to keep all connected databases talking in the same language. The development of GIS-BIM CDE is more critical for sizeable public transportation agencies like the Port Authority of New York & New Jersey (PANYNJ). The agency spans across two states and has substantial influence on the area’s infrastructure, mobility, and quality of life. Development of a standardized CDE between PANYNJ’s facilities, line departments, and all contractors and service providers has proven beneficial.
Internet of Things (IoT)
The Internet of Things (IoT) describes the network of physical objects that are embedded with sensors, gateways, and other technologies for the purpose of connecting and exchanging data over the internet. IoT was introduced into the AECO industry years ago and now fulfills an essential role in the digital twin. As discussed in earlier parts of this series, the modularized digital twin system is driven by the utilization of all corresponding types of IoT implementation. When engaged with a digital twin project, IoT readings need to be collected in the data gateway , massaged in a data warehouse and then assigned to certain assets or locations. Appropriate data analytics and reporting are also essential to interpret the readings in a practical manner. Here, CDE is the foundation to facilitate the “handshaking” between IoT readings with CMMS data requirements and BIM objects. In a real-world project, substantial time is spent on mapping thousands of IoT readings with thousands of data points coming from thousands of assets. A solid CDE will pave a wide and flat road for data mapping and integration and therefore benefit digital twin implementation.
Spreadsheets & Miscellaneous
At least for now, the spreadsheet is a tool that will not be eliminated no matter how advanced the technology. The spreadsheet is still widely used to support multiple processes including engineering design, estimation, data collection, submission, and O&M records. While using spreadsheets can be challenging, they are still essential for some processes, serving as a means for gathering important information required by the digital twin. Specific databases can host these spreadsheets and integrate them but only if they speak the same language from the beginning. in fact, the PANYNJ still uses spreadsheets for most of their asset data collection. However, these spreadsheets are automatically generated from their asset data requirement center hub (ARID) and follow the same data standard used throughout the entire agency. And once the collection work is done, the spreadsheets can be easily uploaded for data validation and then uploaded to their EAM CMMS system.
Figure 2 illustrates a sample logic diagram of how each data resource could work cohesively under the regulation of CDE. The CDE is the data foundation of the entire data management plan that provides the framework for the upper data structure to build on. Once all the pieces are ready to assemble, it is time to talk about how to connect them together.
End User-Oriented Approach
Data integration is just a fancy concept if there is no efficient and effective way to consolidate data from disparate sources into a single place for the user to access. To avoid that, the goals from the end users’ perspective must be considered when initially designing the digital twin. With clearly understood expectations, we can better define functional requirements, then structure them into use cases, and dissemble use cases into data requirements. The latter one is what we should follow since it produces the digital twin which the end-user really wants. It is often called an end-user-oriented approach. It requires the digital twin program designer/architect to consider the following questions to make the final product practical and targeted.
- Who are the targeted end-users?
- What are their expectations?
- To what extent can expectations be met? What technologies should be incorporated?
- Will the data requirements be comprehensive and sufficient to support all defined requirements? If not, what else must be put into the data requirements?
As an example, a client from the facility management department wants to enrich the work order in the CMMS with quality information, such as 3D asset representation, IoT sensor reading history, past work order history, and, if possible, information about its connected objects and surrounding area. This is a reasonable expectation since it will have the AM/FM operator better prepared for the onsite job. The 3D navigation function saves time that would be spent searching for 2D drawings and information about connected objects and the surrounding area can determine if special tools are needed. The sensor reading history helps in diagnosis and making better onsite decisions. In this case, data integration will include the BIM model, which provides geometrical location information, CMMS, which provides O&M information and IoT sensor (with related gateway and data warehouse), which offers meter reading information. If a CDE was established and implemented, these three data sources could be mapped and work together via a unique identifier. The CDE also provides the opportunity to connect the workflow with other compatible data sources. In this case, the PCR, a popular decision-making assisting module, could be plugged in to help the operator diagnose the root cause and suggest corresponding remedy strategies. When designing the PCR database, its hierarchy should follow the asset classification requirement, so future cross-referencing is possible.
Artificial Intelligence Integration
Picture a scenario designing a generic digital twin platform for multiple clients. The most significant challenge is to collect all their expectations and find a way to fulfill all of them with one platform. The end-users could come from different areas with diverse backgrounds and various jobs. Therefore, expectations will be substantially different. In 2020, we conducted a survey of people, mostly from facility management, which explained what an ideal digital twin would be like and asked for three ways in which one might help them. The results varied from “I would like to know where is the mech room on the first floor” to “I would like to know which room will be impacted if I shut off this valve.” Some questions like “How many entrances does this building have?” or “What is the square footage of the ballroom?” seem irrelevant, but have their real-world use cases. That means if the digital twin is used as a dashboard that serves employees who are not familiar with the building, it will meet their basic need to better understand the building, instead of just answering operation & maintenance (O&M) related questions.
Remember part 1 addressed the importance of giving the correct information to the right people and the major challenge to arrange a digital twin platform to meet all these needs. The traditional way was to include buttons and menus on the platform and hope end users could find what matched their need. If new demands were brought up and no existing function matched, corresponding development would start, and a new button added. However, we know the speed of development always lags behind the pace of further requests, which means the platform, crowded with buttons, will not be flexible and sustainable from a long-term perspective.
This is where AI shines. Its introduction is a promising trend in the AECO industry, and has been gaining popularity in assisting design, construction, and logically, the long-term use case of O&M. With the solid data foundation CDE paved for all upper structures, AI can freely reach each database to grab helpful information, aggregate, analyze and present to the end-user. Then the end-user no longer needs to worry about finding the button or requesting a new feature. Instead, they ask a question in a natural way such as “please show me where the air supply system connects to room 101” or “give me a list of all pending work orders.” Trained Natural Language Understanding (NLU) AI receives the question, identifies the intent, capture the keywords, and reaches corresponding databases to grab information. A pilot proof of concept has already been pitched and is in partnership with the IBM Watson Platform by integrating BIM, the Autodesk® 360 platform, and IBM ®Maximo®. For example, when asked to “give me a list of all pending work orders,” AI will understand it is a CMMS query and call Maximo API to fetch all pending work orders, collect filtered I.D.s, then link back into the model viewer, and highlight related 3D objects in the viewer. From there, the user could click and get comprehensive BIM properties and access other peripheral information such as surroundings. Figure 3 demonstrates a use case that asks the AI to list all switch gear on the first floor and prepare to run a circuit analysis for the selected one.
The digital twin follows a path of creating virtual copies of physical locations, processes, and assets. It does not have a solid boundary that defines the scope of work since the whole point of the digital twin is to assist end-users to better understand the physical counterpart and get the most value from it. Therefore, the digital twin should be designed and built from the end-user’s perspective rather than as a showcase of technical capability. No one can build the perfect digital twin overnight. However, each step taken, provides an opportunity to ensure its place on the roadmap with clear goals in mind. A digital twin can embrace all data resources and services that matter to the final goal, from spreadsheets to the AI assistant. Several years ago, we realized BIM is more like lifecycle data management rather than a modeling tool. Today, we are expanding our vision to create the digital twin that connects it all together.
Click here for last month’s installment, Sustainability in Facility Management.
Dr. Xifan Jeff Chen is the EAM Assistant Director at Microdesk, and head of EAM Strategic Advisory Service. Jeff specializes in providing strategic consulting services for clients, conducting and implementing BIM, EAM and GIS integrated solutions, and developing digital twin methodologies for lifecycle BIM implementation.
George Broadbent is Microdesk’s Vice President of Asset Management and has worked on a variety of projects including the rollout of Microdesk’s Maximo and Revit integration solution, ModelStream. George works closely with key stakeholders to identify strategies for asset management projects and manages the effort to build out new systems.
Dr. Eve Lin is a EAM Strategy Consultant and Sustainability Lead at Microdesk, Dr. Eve Lin specializes in providing strategic and technical solutions for clients to facilitate sustainable practices throughout the project lifecycle. Her involvement includes building performance simulation, design automation, BIM and GIS integration and development of digital twin solutions.
Kai Yin co-founded China-based ZhiuTech in 2017 and is Vice President of Product Development. The company’s mission is adopting technology for the AECO industry, creating customized solutions and bringing value to clients’ real life work cases. He previously worked as a mechanical engineer at Arup, and technology consultant at Microdesk. Kai earned a bachelor’s degree in civil engineering from Purdue University, and master’s degree in building science from the University of Southern California.
Jiayi Yan is the co-founder and senior engineer at Beijing ZhiuTech Co. Ltd. in China. Jiayi graduated from University of Southern California specializing in building science and she is a PhD student of University College London dedicated to digital twin topics. She has years of experiences working as a building technology consultant providing technical solutions and implementation plans for world-leading enterprises in the United States and China. Jiayi specializes in BIM, sustainability, and city-level digital twin development in urban regeneration from multi-stakeholder perspective.