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12 AUG 2020

Construction Technology: Building the future in a Mirror World

Key takeaways:

  • ISO 19650 solidified the groundwork needed to establish the use of abstract data models of the entire project delivery system or, Digital Twins.
  • It is essential project teams possess the analytical skills required to secure the right data, of the right quality, to effectively employ machines and efficiently answer the right questions.

Blending Digital & Physical Worlds

In 1992, American computer scientist, artist and writer David Gelernter, released a book titled Mirror Worlds. In the prologue, Gelernter explains, “This book describes an event that will happen someday soon: You will look into your computer screen and see reality. Some part of your world – the town you live in, the company you work for, your school system, the city hospital – will hang there in a sharp color image, abstract but recognizable, moving subtly in a thousand places. The Mirror World you are looking at is fed by a steady rush of new data pouring in through cables.”[1]

Essentially, Gelernter foresaw that computers may, “free users from being filing clerks by organizing their data.”[2] A little over ten years later, observers may have said his vision was flawed. In 2004, Gelernter’s company, Mirror Worlds Technologies Inc., ceased operations. Almost thirty years after his book was published, it appears Gelernter was a head of his time. Today, in contrast, many of us are familiar with the notion of Augmented Reality (AR), Digital Twins and Smart Cities.

The role of standards in digital transformation

In 2018, the release of ISO 19650 may have been the tipping-point event that helped the engineering and construction sector establish a Mirror World of its own. It will certainly be recognized as a moment in which this sector, ordinarily regard a technology laggard, was presented with an opportunity to secure otherwise elusive efficiencies. Efficiencies facilitated by detailed computer modelling and simulation, at all stages in the development lifecycle.

This international standard is intended for “those involved in procurement, design, construction and commissioning of built assets” as well as “those involved in delivering asset management activities, including operations and maintenance.”[3] Traditionally, these two groups are concerned with CAPEX and OPEX respectively (i.e. capital expenditure and operating expenditure). In coming years, new processes underpinned by ISO 19650, enabling a CDE (common data environment), in conjunction with “new data” generated by emerging technology, will allow investors, stakeholders and the public at large, to take a more holistic view of capital asset delivery. TOTEX or total expenditure will better facilitate a discussion concerning full life-cycle costs, through design, construction, operation and decommissioning. By raising levels of awareness, not just in terms of monetary cost, but also other measurable attributes such as time and carbon emissions, stakeholders and society at large will benefit from the more careful and responsible expenditure of finite resources.

International standards, enabling the formation of a CDE, will likely serve as a catalyst for digital transformation. While the Fourth Industrial Revolution, characterized by a fusion of emerging technologies will drive high-levels of disruption in existing markets, one over-arching field will likely be most disruptive in the built and natural environment. That is data analytics.

Project delivery teams, “fed by a steady rush of new data”, may employ AI or advanced data analytics, to more accurately forecast or predict project outcomes. Organization’s with a leading-edge data science capability may go further and prescribe a course of action to better achieve the most optimal project outcome. It is a new era of data-driven decision-making, enabled by a machine-readable CDE, that promises to be most disruptive in our profession.

The People, Process and Technology Trichotomy

Often, digital transformation initiatives fail to meet expectations because one key element is either overlooked or not accounted for from the start. That key element is people.

Often, digital transformation initiatives fail to meet expectations because one key element is either overlooked or not accounted for from the start. That key element is people.

In isolation, process and technology may deliver artificial intelligence but if team members are not data literate, they will not be able to ensure adequate data quality or assess the validity of machine generated analyses. Similarly, if leadership teams are not digitally fluent, they will be unable to provide teams with the software or hardware tools needed to compete in this new era.

Ultimately, by elevating a team’s data science capabilities, organizations may move beyond the inherently flawed constraints of single-point, deterministic estimating and instead employ a more realistic, scientific approach, rooted in probability theory, that expresses a plausible range of goals or qualifies targets in terms of their confidence of success.

People, process and technology trichotomy
Figure 1 - Project Data Analytics & the People, Process, Technology Trichotomy

Developing a 21st Century Skillset & Closing the Data Literacy Gap

When teams possess the skills and knowledge to support advanced analytics, in addition to artificial intelligence (AI), benefit is derived from two other functions: augmented decision-making and collaborative adaptability.

Fundamentally, recent advances in AI are attributed to the brute force nature in which we can employ machines to perform an astronomical number of rapid calculations on a limited number of inputs. Although the number and speed of assessments people may perform may seem poor relative to machines, one crucial advantage we hold over them is that we may receive (if not comprehend) a near infinite number of inputs. It may be decades before machines are capable of contextual analytics, if it happens at all. For this reason, humans are essential to performing augmented decision-making. To emphasize this point, it is said, machines will not replace project professionals but project professionals who use machines will replace those who don’t.

Similarly, teams who possess 21st Century analytical skills can facilitate and benefit from collaborative adaptability. That is, teams who more effectively share data across the value chain, may collaborate to more effectively respond to risks or changing conditions than others who do not possess such insights. In this sense, data analytics offers a risk-based competitive advantage.

Great challenges, however, exist for many organizations in establishing and maintaining a minimally viable data science capability. Given the rapid pace of technological development, most employees leave full-time education with a data literacy gap. Institutions such as RICS have an opportunity to help members develop contemporary skills and knowledge, close this gap, and provide a means for our sector to establish a data-driven culture.

Finally, in this brave new world, professional standards have never been more important. To ensure big data does not generate big problems, transparency and accountability will remain vital traits. Further, ethical reasoning will be critical to ensure decision makers take reasonable steps to remove bias from their systems and final decision-making process.

In a Post-COVID-19 era, the democratization of data will expediate the rebuilding of economies, for the good of the environment and society at large.

  • James Arrow, FRICS, MzLNG Project Risk Manager at Total

References

  • [1] Mirror Worlds: The Day Software Puts the Universe in a Shoebox … How it will Happen and What it will Mean, David Gelernter, 1992, Oxford University Press

    [2] https://en.wikipedia.org/wiki/David_Gelernter (accessed July 25, 2020)

    [3] ISO 19650-1:2018, Organization and digitization of information about buildings and civil engineering works, including building information modeling (BIM)