All industrial sectors would like to achieve higher levels of efficiency, less idle time, and better performance. While most sectors are currently enjoying the benefits of automation and digitalization, the AECO sector continues to struggle with this transition. The sector still relies on human intervention in decision support throughout its building asset life cycles. The process of ‘capturing’ these decisions, which can be achieved by the digitalization of AECO processes, is complicated by the complex and diverse dynamics inherent within the sector. As a result, there is no one-size-fits-all solution to the problem. A wide range of ever-evolving industry standards are in place which regulates requirements related to building assets, but the industry’s practical, hands-on experience remains ‘embedded’ in individuals such as architects, engineers, facility managers, site managers and foremen.
A comparison of the life cycle of a building asset to a product in the manufacturing sector results in the development of a product prototype, which must function for the next 100 years. This encapsulates very well the challenges and zero failure requirements facing the AECO sector.
Why do we need digitalization and digital twins in the AECO sector?
The figure above demonstrates that productivity increased within the AECO sector will not be achieved without automation and digitalization. Digitalization supports decision making at many stages. It provides faster and more precise production forecasts; it reduces deviation detection times and long-term storage, and promotes learning following errors. These are just some of the most important benefits. The AECO sector has introduced digitalization in the form of Building Information Modelling (BIM), but this tool benefits only a very limited aspect of the building asset life cycle. BIM has become the de facto standard for the building design and operation phases, but its impact on construction and deconstruction is questionable.
BIM adheres to the same standard (ISO 10303) as the manufacturing industry when it creates a digital version of a building asset. This enables the AECO sector to benefit from the best practices now being applied in the ongoing industrial revolution in the manufacturing sector (Industry 4.0), and to accumulate knowledge on how to establish digital twins that can represent building assets.
While BIM enables the standardisation of information sharing and exchange, the loss of information that occurs during the ‘transformation’ of models from their native modelling language to BIM represents a major bottleneck. Initiatives are currently being developed to counteract this limitation, such as IfcOpenShell, but we are still awaiting reports of success.
However, BIM cannot, and never will, be able to capture information that is not originally intended to be incorporated as part of a building asset, such as the simulation results from HVAC tests. In most cases, such information is given to facility managers and stored in some form of long-term storage system for use only when thorough checks are required after an error has been detected. Moreover, ad hoc changes related to new calculations and weather conditions, especially during the construction phase, which may be highly useful information in the operations phase, may go unrecorded. In most cases, BIM models are updated with so called ‘as-built’ information that reflects, but does not copy, reality, once the construction phase is completed.
Current analytical tools use BIM (either ‘as-designed’ or ‘as-built’) as a basis for suggesting improvements and plan changes, and to run a variety of simulation scenarios. Since these calculations are based on a snapshot of the building asset (an exported BIM file), it is clear that they have inherent limitations.
It is very important to separate the concept of digital twins from simulation. Simulations are essentially static processes, providing a visual overview and calculations based on pre-recorded data. Digital twins, on the other hand, are dynamic entities that are fully connected to a construction site, providing a full digital representation of all its chaotic complexity.
Digital twins offer the key to higher productivity in the AECO sector, especially in the construction phase. Currently, quality control, scheduling and logistics during the construction phase are in most cases performed on the basis of the experience of humans. Knowledge is transferred between specific construction projects only via the individuals who participated in them. Digital twins enable the recording and analysis of data, and offer a key tool for safeguarding long-term learning from the actions taken throughout the building asset life cycle.
Why now?
The evolution of the ISO 19650 standard, or information models in the construction sector, first published in 2018, has highlighted the needs and opportunities related to the organization of information management.
During the life cycle of a building asset, an enormous amount of information is generated and lost during transfer among the various stakeholders. The more widespread use of BIM, and the option to establish a common data environment (CDE), may serve to address this problem. The interconnection between the various ISO standards underpinning this effort is shown in the figure below.
While this need was identified by the manufacturing industry as early as in 2003, and reflected in the publication of the standard ISO 14649, the task has proved to be much more challenging for the AECO sector, as previously described.
Moreover, the manufacturing industry has recently established the standard ISO 23247, which may act as motivation for the establishment of a digital twin standard for the AECO sector, expected by 2033. Some of the key developmental events and their dates are shown in the figure below.
Why a digital twin and not BIM alone?
While BIM exerts its main impact during the design phase, digital twins are designed to reduce the overall costs involved in producing a building asset. BIM-based workflows and analysis may be useful in preventing expensive design changes during the construction phase. However, they cannot speed up the detection of deviations or help to reduce rework or waiting costs. A digital twin can be regarded as part of a site manager’s subconscious. The well-known MacLeamy Curve, shown in figure below, shows that the digital twin’s contribution can be visualized in terms of lower overall costs and reduced effort. This is because the twin enables the long-term storage and re-use of experience in a variety of different building asset projects.
The difference between the use of a digital twin and BIM alone can be explained as follows: while BIM provides a snapshot of a building asset at a given point in time, the digital twin provides both the history of, and future predictions related to, the building asset.
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