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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Apply a transformative new technology to construction projects with this timely guide
The blockchain is one of the most transformative technologies to emerge in the twenty-first century. But Artificial Intelligence and Machine Learning will also have a profound impact in construction in equal measure. Both will influence how digitalization is applied and how these new technologies are addressed with an identifiable skills gap in their implementation.
Using a decentralized digital ledger to ensure that information stored across numerous computers cannot be altered, it provides a fully transparent and secure way of storing and sharing information. Transforming the Construction Industry with Blockchain provides a comprehensive overview of this technology and its applications in construction and the building trades. Beginning with an overview of basic blockchain principles and then moving to construction-specific applications, it provides a range of strategies by which construction professionals can increase and streamline their collaborations with other stakeholders and create smarter, more transparent projects.
Transforming the Construction Industry with Blockchain readers will also find:
- Case studies throughout showing blockchain at work in construction projects
- Detailed discussion of topics including improving data flows on construction projects, reducing sub-contracts and misaligned workflows, and many more
- Guidance for using blockchain to encourage sustainable and ethically-sourced design and construction
Transforming the Construction Industry with Blockchain is ideal for all construction professionals or potential stakeholders in building projects.
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Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Dedication Page
Preface
List of Figures
Biography
CHAPTER 1: Executive Summary
How Digitalisation Has Been Adapted into the Construction Sector
How Data Will Drive Generative Architecture Quicker Than We Think
CHAPTER 2: Data Handling
Data Mining
Data Communication
Data Filtration
Digital Control Room
Data Is a Commodity
How Information Can Be Filtered and How Blockchain Will Feature Herein
Information Revolution versus Intelligence Revolution
Digital Impact
CHAPTER 3: Trust/Opportunities
Leverage Devices
Light Touch for Agility
A Sentient Machine
Transference of Information
Unlocking Bottlenecks
CHAPTER 4: Education, Learning and Reskilling
Artificial Intelligence versus Human Intelligence
AI Money versus Performance
CHAPTER 5: Risk
CHAPTER 6: BIM
Collaborative Trust, Not Technology, Is Integral to This New Method of Working
Beyond BIM Level II
BIM as Management Rather Than Modelling
Bringing Performance into the Design
Materials
CHAPTER 7: Performance
Designer Reputation
Respect
Reward (Incentivisation)
Internet of Things (IoT)
Artificial Intelligence
Bots (AKA Robots)
Smart Objects
Large Language Models
Taxonomies (EU Directive)
CHAPTER 8: Better Practices
Promoting Motivation
Latent Redundancy
Remedial Action
Insurance Premiums
CHAPTER 9: Blockchain
Provenance
Cryptocurrencies
AECcoin
CERTcoin
Crypto Currencies, Historical Relevance to Form a Foundation of the Work Required
D'APPS, Most Interesting in How Blockchain Will Infiltrate Much of Our Everyday, Which Is Not Appreciated Today as It Should Be
Smart Cities, the Culmination of How These New Technologies Will Impact Architecture, Construction and The Whole Building Sector
Off‐Grid
Servicing
CHAPTER 10: Urban Resilience
Horses in New York
Autonomous Mobility
Contracting in General
CHAPTER 11: Case Studies
International Terminal Waterloo Station
Terminal 5, Heathrow
BLOX
Leadenhall Building
Foundation Louis Vuitton
CHAPTER 12: Sustainability
Circular Economies
Carbon Emissions
Certifying Carbon
Trading Carbon Credits
Trading in Carbon Exchanges
CHAPTER 13: Issues
Tokenisation
Wallets, DAPPs and Coding
Incentivised by the Token System
CHAPTER 14: Vapourised
CHAPTER 15: Dynamics
Brooklyn
Ground‐up
CHAPTER 16: Skills
Knowledge
Skills
Competences
CHAPTER 17: Rewarding Performance
Performance, Promoting Better Practices
CHAPTER 18: Smart Contracts
Smart Contracts, How They Offer Solutions But Also How The Legal Elements Are Against It
The Problem Area of Current Contracting
The Problem with Digitalisation
‘Build Trust’, Implementation of Blockchain in the Tender Process
‘Build Trust’ and Blockchain
‘Build Trust’ and Smart Contract
‘Build Trust’ and Future Visions
‘BIM Partner’, Implementation of Blockchain with BIM
Smart Contracts, the Legal Parameters and Challenges
‘BIM Partner’ and Smart Contracts
‘BIM Partner’ and Future Visions
Summary of Analysis
The Construction Blockchain
Construction Blockchain and Smart Contract
CHAPTER 19: Digital Twins
Robotics (Scanning and Clash Detection), The Mechanics of How It Will Be Implemented
CHAPTER 20: Conclusion
Disruptive Technologies, How It Will All Settle
Project Work
All Change
How Would You Get a Notorious Non‐payer To Step Up to the Plate?
References
Index
End User License Agreement
List of Illustrations
Chapter 1
Fig. 1‐1: Student project (Bryan Zou, Dominik Wawrzyniak, Guilia Perciasepe,...
Fig. 1‐2: Student project (Bryan Zou, Dominik Wawrzyniak, Guilia Perciasepe,...
Chapter 4
Fig. 4‐1: Student refurbishment project (Tomas Bottenelli).
Chapter 5
Fig. 5‐1: Terminal 5, Heathrow, London, Richard Rogers.
Fig. 5‐2: Student project (Erica Dima, Mathias Hammerdorf, Emma Grau, Gudrun...
Chapter 10
Fig. 10‐1: Drone‐taxi in a Copenhagen car showroom.
Fig. 10‐2: Interior of drone‐taxi.
Chapter 13
Fig. 13‐1: Student project (Tomas Bottinelli).
Fig. 13‐2: Student project (Tomas Bottinelli).
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Dedication Page
Preface
List of Figures
Biography
Begin Reading
References
Index
Wiley End User License Agreement
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Transforming the Construction Industry with Blockchain
Enhancing Efficiency, Transparency, and Collaboration
James Harty
KEA, Copenhagen School of Design and Technology Copenhagen, Denmark
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To my beloved Lene
Preface
The construction industry is in a state of flux. Some would say that is an understatement. Some would say we are building too much. Some would say we need to recycle and transform what we have. Most would agree something has to change. If the industry was a country, it would be the third worse in the world after China and the United States of America, replacing India for carbon‐dioxide embodiment.
Moreover, than efficiencies, better methods are needed to bring transparency to the table so that we can reward better practices, which in turn will lead to better collaborations. Digitalisation too is at way too many differing levels, depending on where you are coming from and how you engage as a stakeholder across the whole spectrum of the industry. It is also unfolding in multiple ways, bringing a plethora of new techniques, reflecting and contradicting each other, in equal measure.
Whatever the situation, Thom Mayne of the American Institute of Architects in Las Vegas said:
“It’s about survival. If you want to survive, you’re going to change; if you don’t, you’re going to perish. It’s as simple as that … you will not practice architecture, if you are not up to speed with this …”
While this addressed BIM adoption, it is still most relevant today, because the industry is fragmented, conservative and most unproductive. But it is not all doom and gloom, many innovations and developments have heralded new methods, scope and made strident steps to make buildings better, leaner and operational performers. The procurement of a building should not end with its handover, but rather should open an embracing relationship for its entire life and beyond.
Such an arrangement requires marshalling, highlights skill‐gaps that need to be filled and raises the bar with thresholds to be reached and completed, without missing a heartbeat. The competences involved delivering this seismic shift might appear daunting, but help is at hand. An overbearing word is blockchain and often is held at arm's length, but with closer examination much of the hype can be massaged into meaningful material.
Don Tapscott likens the situation to that of the internet's evolvement, which brought us ‘e‐mails, the world‐wide web, dot‐coms, social media, the mobile web, big data, cloud computing and the early days of the Internet of Things’. Essentially, this was an internet of information. What is needed now is an internet of value, with assets becoming interactive. This includes identity and reputation, money, intellectual property, contracts, assets and energy. So, the matrix is moving from being squared to being cubed, adding a totally new dimension.
Malachy Mathews sees the combination of BIM and blockchain as evidence of value transactions. He claims this platform will disrupt the design and construction industry, and that data, as a commodity, has value. This is the crux of the matter, and that BIM‐based collaborative technology will see merely self‐serving contracts consigned to failure. This is to be endorsed and encouraged, and it will see hierarchical structures replaced by networked ones, becoming more efficient, enjoying higher valuations, being fault tolerant and self‐regulating, through machine learning.
Construction professionals have often strived to recover the intrinsic value of their labour. Blockchain offers a new value proposition to extract reward not just for the collaborative services but also for the intrinsic intangible value across the life cycle of a facility. It becomes the gift that keeps giving, a contract that rewards value, a contract that does not reward non‐performance (which is just as important) and an agreement that releases payment when due or expected.
The intension of this book is to demystify the layers and levels being amassed upon us today. The aim is to clarify how we have gotten here and what needs to be done to regulate and adopt these new paradigms. It has been my great pleasure to write this book and I hope you enjoy reading it. It is intended to bring a professional handbook aspect to the subject and a referenced content manual to dip into when needed. Each chapter will attempt to be concise and complete within itself, so that it can address problems and issues to respected areas of interest. While the entire book brings an overall treatment of the topic, blockchain is changing at a rate of knots which will be tracked throughout the timeline. I hope you like it.
List of Figures
Figure 1.1
Student project (Bryan Zou, Dominik Wawrzyniak, Guilia Perciasepe, Oliwia Mazurek & Wikoria Ekiert)
Figure 1.2
Student project (Bryan Zou, Dominik Wawrzyniak, Guilia Perciasepe, Oliwia Mazurek & Wikoria Ekiert)
Figure 4.1
Student refurbishment project (Tomas Bottenelli)
Figure 5.1
Terminal 5, Heathrow, London, Richard Rogers
Figure 5.2
Student project (Erica Dima, Mathias Hammerdorf, Emma Grau, Gudrun Sebaldus & Nermin Al‐Shakargi)
Figure 10.1
Drone‐taxi in a Copenhagen car showroom
Figure 10.2
Interior of drone‐taxi
Figure 13.1
Student project (Tomas Bottinelli)
Figure 13.2
Student project (Tomas Bottinelli)
Biography
James Harty is a lecturer at KEA, Copenhagen School of Design & Technology. His PhD researched “The Impact of Digitalisation on the ManagementRole of Architectural Technology,” (Harty 2012). James was instrumental in the school's adoption of BIM in 2006, with the implementation of collaborative methodologies leading to deal with disruptive technologies.
He works with Common Data Environments, mapping virtual worlds on to reality and making digital twins relevant to Smart‐Cities and Off‐Grid, using Blockchain, IoT and AI. He has co‐authored a book, “Getting to Grips with BIM” (Harty et al. 2016), and he sees BIM as instrumental in tackling climate change, sustainability, especially embodied carbon, and performance‐based design.
His Master’s in Urbanism mapped the housing types and urban morphology of a satellite town near Dublin (Dun Laoghaire), and he was a co‐author of a report preserving “Temple Bar” in medieval Dublin. He is currently researching a Horizon 2020 project, “ARISE,” implementing Blockchain and construction in blended learning across Europe, addressing all sectors in construction.
CHAPTER 1Executive Summary
What is disruptive, fragmented and works with insanely minimal profit margins? As I said at the BIM Coordinators Summit, even the dogs in the street know the answer is construction, an industry which should know and do better. Even in terms of productivity, the sector is performing worse than back in 1960, because players can and often do get paid twice for double work, knowing that work will be demounted, in order that underlying work can be completed, before the work is assembled again. Making below cost bids also leads to needing requests for information and delays to lengthen engagement and encourage litigation.
What is needed is a method to actively engage stakeholders, even beyond handover, and more importantly to reward such an endeavour. If there is an incentive towards continued engagement, the benefits and potential is breath‐taking. Key to this is performance, and key to performance is measurement. If a project is proposed that will save 20% in energy over the next 20 years, a method is needed to avert green‐washing, to deliver the goods.
If such a situation existed, it creates a method to reward this performance, by say, paying a 5% of that saving to the perpetrator, as a reward, for making the building that was needed both for the client/user and society in general. A repeating paid‐out dividend is an incentive not known in the industry today. Once established better practices prevail, making performance a central pillar in the mix, improving the sector. In order to verify and validate such endeavours, blockchain enters the fray. Blockchain offers a trusted framework for data validation. It records performance in a decentralised, immutable open‐sourced manner.
To implement this new environment, smart contracts are needed. They use if/then structures to administer the work. They provide protocols that verify, simplify and enforce performances. Once met, they trigger a payout to the recipient, rewarding their successes. They also promote such activities and identify best practices. They change how buildings are made and they bring life‐cycle assessment into procurement.
Automating this process opens the industry to integrating the Internet of Things (IoT) and artificial intelligence (AI). Digitalisation also brings common data environments (CDEs) and cloud computing, together with virtual reality (VR), artificial reality (AR) and mixed realities (MRs) into the new paradigm. AI introduces machine learning (ML), which will have a profound effect on proceedings. It can learn from analysing data and metadata and draws ever improving explicit data mining, as it is applied to large language models optimising and completing tasks unsupervised, freeing up time for other activities.
While ML is a subset of AI, it also gives birth to deep learning (DL), making expert systems, that can begin to think for themselves. It can identify potential bottlenecks and foresee mismanagement or toxic procedures that can be addressed before they become hazardous. This will transform the industry and make it a profitable, healthy, positive sector. Keeping tabs on this new paradigm requires a single source of truth and here blockchain comes to the rescue, ensuring probity and controlling procedures so that poor procedures become less attractive, being eventually phased out.
Finally, as Paul Doherty said at BIM Coordinators Summit last year: ‘AI will not take your job, people using it will’.
How Digitalisation Has Been Adapted into the Construction Sector
If paper gave us the frontal architecture of proportioned edifices placed in the landscape marking territory, digitalisation gave us an additional aspect of parametrics. This brings a need to analyse and rethink data flows. Covering the work phases, actors, flows, covering, tasks and addressing the problems of these processes, skills and technologies.
Arto Kiviniemi has also drawn a comparison that in nature material is expensive while shape or form is in comparison free, while in traditional construction form has been expensive while materials are cheap. It is therefore most likely in the future form will be cheap and materials expensive, meaning that (3D) printing complex forms will not be a problem (Kiviniemi 2015). To illustrate this, he points to the forms of fractal shells for complex forms, modern monolithic offices for cheap shapes and the likes of the Olympic Bird's Nest stadium in Beijing or the Disney Concert Hall in Los Angeles as parametrical wonders of the world.
In building a case for change, Standards Australia makes a strong case that the construction sector forms a major part of the national economy (Standards Australia 2023). It quotes the sector as accounting for over one million people or 10% of the total workforce, generating over $360 million in revenue at 9% of Australia's Gross Domestic Product (GDP). Finally, it reports that every million invested contributes $2.93 million in output or $1.3 million of GDP.
Handheld devices have become ubiquitous on building sites and their use is becoming more and more entrenched into many practices, whether it is confirming completions, requesting information, reading QR codes, and even providing learning environments to access instantly, solutions and methods so that there is a readily available solution to potential bottlenecks, while reducing incorrect actions. They record situations and can instantly fetch the latest data or information or identify mistakes and can allocate the remedial action, saving time, effort and waste (Figure 1.1).
How Data Will Drive Generative Architecture Quicker Than We Think
Each wave of a new technology is usually heralded as a time‐saving feature, leaving more time for design. This new void typically gets filled up with the protocols needed to implement these new competences. Automating this becomes a game changer. We are on the cusp of generative architecture where function becomes a mechanism to derive a design. Computational algorithms analyse and explore possibilities based on specific goals or constraints.
Fig. 1‐1: Student project (Bryan Zou, Dominik Wawrzyniak, Guilia Perciasepe, Oliwia Mazurek & Wikoria Ekiert).
Fig. 1‐2: Student project (Bryan Zou, Dominik Wawrzyniak, Guilia Perciasepe, Oliwia Mazurek & Wikoria Ekiert).
So instead of consulting a book of Architects' Data, such as Neufert, the programme does this donkey work. For example, designing a school for 400 students, the number of classrooms, washrooms, shared and ancillary spaces can be generated automatically, leaving the designer time to mass and form the project to other parameters (Figure 1.2). And this is only scratching the surface, robotics and 3D printing could automate much more of the process, computer numerical control (CNC) controls machines by means of a computer meeting specifications by following coded programmed instructions without manual intervention.
Programmes exist to parse building regulations, to control delivery and to check compliance throughout the procurement process and beyond. Applications and robotics, better known as APPs and BOTs today provide an array of handlers to aid and abet a whole range of things. What brings all these things together is blockchain, and it is what coordinates and holds all in perspective in this new paradigm.
As Don Tapscott claimed in his TED talk in June 2016:
‘The technology likely to have the biggest impact on the next few decades has arrived. And it’s not social media, it’s not big data, it’s not robotics, it’s not even AI, and you will be surprised to learn that it is the underlying technology of digital currencies like Bitcoin. It’s called the Blockchain’
(Tapscott 2016a).
CHAPTER 2Data Handling
Undoubtedly, data collection is the foundation for making better decisions to drive improvements, but it becomes complicated if there is too much data and it is not in an ordered fashion. Questions about ownership and management also make it difficult to navigate and its filtration and usefulness come into play as we wade into the melee. It can also grow into a data lake where in future it can be used in predictive maintenance. Data handling also requires an agreed environment in which all stakeholders are considered and have an agreed protocol for the exchange and use of the data generated through a project. It is built over four phases in which:
The first sets out the processes, where the scope and content are broadly outlined for each stakeholder. This means it is an internal process, setting one's house in order before engagement.
Second is the sharing phase, where information content is considered complete for some disciplines (those closely related to each other) and is subject to review and modification to dovetail with the other parties.
Third is the publishing phase where the endeavours of the first two phases blossom, and the data is definitive but subject to revision. In this phase, it is operational.
The final phase is archival, meaning it is kept for the record and is accessible or valid but is now superseded.
In general, it can be seen as a fluid process, but with defined framing and defined channels of communication. By definition, it is easily accessible to all stakeholders who must each have a position and responsibility towards the content. It provides a method of traceability, meaning accountability is embedded in it. It must cover all data formats, which have to be agreed and understood, and it must contain open protocols for the exchange and accessibility of data. It has to be updatable and be confidential and secure.
By having such a model, then the flows and previous bottlenecks are made easier. All stakeholders can easily engage and be content that the data is up‐to‐date and accurate. It replaces the requests for information (RFIs), which could seriously delay a project, due to the time and analogous nature of the information flows. Typically, in the past, a request for information might only happen at a fortnightly site meeting, meaning it was first addressed at the next meeting and approved at the following meeting. This process when it becomes digital removes much of the downtime and the waiting for approvals.
It removes much of the paperwork, often taken up an entire day at the end of the week, which was dreaded and derided, with sayings like the job's not done, till the paperwork is done. Now an item of work can be allocated, when completed can be shown as finished (completion photo or similar) and approved for payment all within a cloud solution. This digitalisation process is revolutionising the industry, and once adopted is rarely rescinded or withdrawn in favour of the older methods. It is controlled by an International Standard, ISO 19650 (previously being the PAS 1192 series) and manifests itself through many platforms. These include but by no means are exclusive: Autodesk's BIM 360, Dalux Field, Box Delivery and FM, Revizto, ASTA Progress Mobile App and Field View.
On the construction site, in order to ensure proper coordination and dissemination of tasks SISK, an Irish contractor, involved with Center Parcs leisure centre, used digital project delivery (DPD) methods divided into fourth dimension construction sequencing, building information modelling, a progress mobile app (ASTA) and another called field view to keep everything up to scratch. Field view was used in their quality sign‐off procedures, to ensure environmental and safety auditing on a hand‐held device, reducing paperwork and waiting time for approval. Not only does this improve the day‐to‐day management of the site but also it builds into a bigger picture where things can be monitored, best practices gleaned and improvements made on evidence‐based decisions, making the whole enterprise better and fit for purpose.
This reduces time spent using paper formats, it allows for tasks to be easily raised in a meaningful way while saving time with inspections. Tracking live progress was critical for them on the project, using the ASTA mobile app, a planning software which allows GANNT charts to be prepared so that informed decisions can be made while reducing the amount of data inputs previously made manually. This improved and increased programme awareness throughout the entire team made data entry faster, increasing value added time for better planning.
BIM, especially 4D BIM was instrumental on site to utilise and plan the works effectively. Their software allows for the collation, distribution and management of all of the project and technical documentation. This means that their designers can off‐feed their information through one platform allowing collaboration for the various stakeholders across the project. It is a key delivery tool that has been around a while but in combination with the other tools adds to the overall success to the project.
4D modelling has been used on site in conjunction with the handling, the logistics, the supervision and day‐to‐day delivery scheduling and the call‐offs from their suppliers moving outside of the site and involving third parties in a lean process that could cause multiple delays previously. A reluctance to implement such strategies was quickly dismissed as the benefits accrued during the project as workers saw the improvements to workflows and reaped the paybacks in their workloads with improved efficiencies.
Maximising these activities on site allows lean constructions to improve work sampling, just‐in‐time (JIT) delivery and something they call ‘kitting’. Kitting involves the correct quantities being sent out to the correct assemble and installation points. In their many on‐site warehouses, it is used to utilise the kitting. Essentially, a kit of parts is put together for each and every job, just like flat‐pack constructions that come with the correct number of parts and the correct number of fittings, needed to assemble it in‐place.
This reduces wastages and promotes collaboration, as when it is bagged, boxed or palleted, the receiver knows that all that is needed is supplied and does not need to be quality checked and accounted for, improving cross platform collaboration for all concerned. So, it comes to the right location with the right quantity. For example, a boiler kit for a particular lodge. This means that the fitter receives the right quantity and the right specification in the bag, eliminating waste while improving the workflow.
If kitting is a method used for management of materials, then work‐sampling is a method used for management of resources. Sampling is a method of observing activities ongoing so that they can be classified. Basically, a worker is assigned to monitor blocks of work lasting an hour where all activities are recorded as value added, a support activity or waste. Following the exercise areas of waste are high‐lighted and reviewed to see if they can be eliminated if possible. This might include a method of detailing to make the task easier, to make some activities simpler or quicker and to look how the crews were set‐up managing materials and accessories in the field.
The benefits of work sampling in one instance meant that they were able make a 22% productivity improvement observed in key trades, bringing packages back within budget. So, in a fast track, large‐scale unique project with its own set of key challenges, the team has used cutting edge technology to help deliver this project in an efficient and effective manner. This means that they remain on‐time, on‐budget, delivering to the highest level of safety and quality. Embracing technology can be a challenge at times but their future success depends upon it, says Brian Kennedy of SISK contractors (Kennedy 2019).
So, the research question would be: What is the process that is being replaced? What are the advantages of implementing such a regime? What are the benefits of adoption? Where does CDE lead stakeholders to in the next phase, BIM Level 3, Building Lifecycle Management? What is being replaced is inefficient methods that support all the problems that have beseeched the industry. The advantages encourage better collaboration, remove double work and improve productivity. It fosters a better method for better practices, and it can reward such practices.
Having a single source of files, typically in a cloud or, in a common data environment (CDE) means that there are no revision mis‐matches, that files are always up‐to‐date, and accountability is easier to handle as there is trail of who has done what and where, it is traceable. Also, as BIM Level 3 expands, it can generate a digital twin which means that the project is built virtually before it is realised. This means that errors and erratic solutions can be caught before stepping on site. The twin allows what‐if scenarios to be confronted, and this enforces facilities management issues to be addressed and tested. The twin replaces the Facilities Managers (FM'ers) making a fresh clean sheet report after handover.
Data Mining
If data is ubiquitous (and it is), then feeding artificial intelligence (AI) more and more data becomes an endless thankless cycle. Moreover, the AI's appetite becomes voracious and grows out of control within the realms of human cognisance. ‘The AI dilemma’ (Harris and Raskin 2023) discusses that 50% of AI researchers believe there is a 10% or greater chance that humans will become extinct from our inability to control AI. Harris and Raskin outline that new technologies need to be responsible for their consequences. This is akin to engineers of an airplane telling you there is a 10% chance that the plane they have created and of which you are about to board has a 10% chance that it will fall from the sky with you looking out the starboard window (ibid).
Pointing to how social media got it wrong with fake news and similar and the need to be allowed to be forgotten, and the need to protect data, which were all unforeseen consequences of the new social media rolled out before their full impact was known, they now point to ChatGPT and how it will be unleashed on the poor unsuspecting public again. But now they point to an exponential rise and even a double exponential rise in how AI is using machine learning to create a monster.
The ills of social media are numbered as: information overload, addiction, doom‐scrolling, influencer‐culture, sexualisation of kids, QAnon, shortened attention spans, polarisation, bots, Deepfakes, Cult factories, fake news and the breakdown of democracy. AI, they see as compounding the following: reality collapse, fake everything, trust collapse, the collapse of law and contracts, automated fake religions, exponential blackmail, automated cyberweapons, automated lobbying, biology automation, exponential scams, A–Z testing of everything, synthetic relationships and AlphaPersuade.
This is off‐topic but unnerving none‐the‐less. Some of these are self‐explanatory but some are totally unheard of. The last, AlphaPersuade, draws my attention, in that it will relentlessly bombard me in all manner of means, to sway me over to a totally different set of values. This is in effect Deepfakes, that will relentlessly pursue my values and impose their views above mine, until I either accept their content or in despair give up my own values in dereliction of my own disposition. They predict the end of elections because of the polarisation of voters and the beginning of this could be said to have occurred in the recent US presidential elections.
ChatGPT is a chatbot using generative pre‐trained transformers (GPTs) to accumulate data from many sources. It creates a language. The transformers are text, images, etc. used to predict or create a new one. This is called a generative large language multi‐model but where, they maintain, content‐based verification ceases to be created. A machine they claim does not have a sense of framing or morals to know whether something is good or bad. They cite the instance of a 13‐year‐old girl asking her personal AI on Snapchat about someone grooming her for sex with absolutely no warning signs of the inherent danger being raised by My AI, in fact scented candles might make it a memorable first night, she is told (sic).
They call for rules of engagement, primarily that new technologies require a new class of self‐imposed responsibility; that the new powers released in these technologies create a race to maximise engagement in the engagement economy (wanting to keep a person on the app for as long as possible), and that without any coordination that such an endeavour is doomed to tragedy (ibid). They see AIs as having a bias without transparency.
Conversely, Joe Lubin, co‐founder of Ethereum, sees ‘some kind of a truly decentralised autonomous organisation owned and controlled by its non‐human value creators, governed through smart contracts’ as going ‘All the way’ towards decentralising the enterprise with blockchain providing a frictionless efficient mechanism to run the show (Tapscott 2016b).
Vitalik Buterin, who created Ethereum, imagines a bot that could roam the internet with its own wallet, learning, adapting and developing a taxonomy penultimately leading to full AI (ibid). So, Don Tapscott sees blockchain as being the keeper of law and order, and as having the transparency so that any such shenanigans will be exposed for all to see (ibid). Admittedly there are a number of years between both positions and one which is changing at a rate of knots, to which I shall return.
Data Communication
Building information modelling brought with it a whole host of procedures, processes and methodologies about how we interpret the data generated. What is this data and how does it communicate across the platform are two interesting questions to be raised. Data conveys information about quantity, quality, status, statistical with meaning or sequential impacts having value.
It communicates with each other by having a tagging system, identifying each piece of data with a classification system to be managed across the platform. Ownership or authorship are also two very important factors in generating and valuing input to the project. From this remuneration and rights can be assigned to all stakeholders and third parties.
A typical classification system will identify the elements, usually with regard to where they are in the project. A coding process takes place to give a unique tag to each building part or process. This usually involves the tracking of metadata associated to each element to document it in the project. This data includes (but not exclusively) project id, organisation id, project title, business type, knowledge, discipline, contract, procurement, work area, work phase, file type, storey, sheet name, level, information, purpose, exchange and status. Under knowledge differing types are defined including architecture, urban and landscape design, interiors, industrial design, surveying, information and communication technology, mechanical and structural engineering. So, in a Danish system (bips, now Molio), the following code would have the following definition:
|4_A6_K01_H1_E1_N01
Project ID (‘four’ from a known list), ‘Main Project’ Workstage, ‘Architectural’ Discipline, ‘Plan Drawing’, ‘First Floor’, Running Sheet ‘Serial Number’. This would be entered on the drawing sheet title block and be unique within the documentation.
Other classifications include Uniclass (UK), Omniclass (USA), SfB (S) and DBK (DK). When federated models from differing stakeholders are set together, methods are needed to translate and interact with each other while accepting each of the other systems. Most of this happens on a like with like basis. Problems only arise when a field is not catered for and so translating back returns a void. Industry Foundation Class (IFC) is heralded as an open‐source format to which all authoring programmes can write to but from which no authoring can occur.
Moving a simple wall in and out of several programmes can lead to data being dropped. Typically, a field would have no corresponding field in the new format and if not critical would be dropped. On passing back that field would be voided (Pazlar and Turk 2008). Even using IFCs, evidence has been shown that all export functions were not supported. Pazlar found that something as simple as a wall hatching or pattern being lost in a vertical section. This puts the onus on the operator to be vigilant, not blindly trusting the mapping process (ibid).
IFCs, while offering a common base for all stakeholders are often disliked by vendors, as it allows competitive software platforms to match and offer compatible solutions to each other, and in so doing offer alternative viable results. So often, with a new revision of a software platform, a new feature is not matched or covered in the pooled information, giving them a temporary advantage being unsynchronised until rectified. This time difference is exploited to the major vendor's advantage, and countered with this is advancement and not doing it would be regressive.
This hustle and bustle, both from vendors all the way to users, causes everyone to be on their toes, and this leads to Information and Communication Technology (ICT) protocols where all is agreed and is binding at the outset of a project. They are unique to each project because of the makeup of each and every project team. They are also dependent of the client, being a smart player or dumb, wanting the capital cost to be kept to a minimum, without understanding the intricacies of this new paradigm. Because they are agreed at the outset, they often miss nuances in the project and can become redundant in long running projects.
The complexity of this new layer has meant that when embraced the early adopters are happy to sit on their laurels and become somewhat hesitant towards continued expansion, in the belief that they are holding the holy grail. This means that BIM Level III is not being adopted and rolled out as was expected and anticipated. All in all, this can be seen as disingenuous at best and limiting in best practices at worst.
To correct this situation, certain automations need to come into play. First and foremost a coding to translate and match each and every element in the project. Indeed, when opening a project, the classification system could be chosen, independent to the other stakeholders. These choices can be recorded and noted for subsequent project and a taxonomy can grow and be beneficial to similar projects. This can also apply to stakeholders where teams that work together in similar operations can predetermine many features so that an intimacy and familiarness grows with like‐minded participants. Conversely, a new contributor can rack up a project to their requirements so that they can compare like with like. All circumstances are covered.
Secondly, these protocols become invisible, removed from the initial pitches so that energy can be focused on other aspects of the job. This frees up much effort and stress for other challenges of the project (BIM Level III, for example). Control of these additions can be tackled in the common data environment (CDE) so that whatever handheld device used can be tailored to the users’ preferences without detriment to the rest of the team. Delegating so much authority requires a robust platform, which needs to be verified and vigorous. The app running all this is a form of Blockchain and will work in the background, seamlessly with all the other players.
Data Filtration
Because data is so pervasive, controls are needed to make it compliant with our wishes and how we want to use and engage with it. This requires a form of filtration to bring relevant facts to the fore and prioritise the worthiest of them for the case being examined. Essentially, what is needed‐to‐know and nice‐to‐know. This is not to say or affect the cohort as is, but to bring a sense of order to the proceedings. How this is managed and who decides on the issues become most compelling. There needs to be a hierarchy and a sense of relevance so that the convoy is not drawn to the bottom of the seabed, drowning in a data overflow.
Many procedures and data streams have been suggested and implemented. But this only opens a plethora of differing protocols which might or might not be transferable or translated across the domain. We are back at the classification system again, but with a new angle, relevance and probity. There is now a need to be knowing of what the subject or object requires and to be all‐knowing in delivering this quantitative master class.
Procedures and methods have been made and handed down through the ages by those who have worked and generated this data, but now we are entering an overhaul of data's relevance and metadata's impact on the areas of interest and scope of the projects in which we are involved. There is a danger of flooding the domain and of making the same mistakes twice, not learning from past encounters. This latter point is most valid when it comes to repeat work and the benefit of hindsight in learning new situations while being in command.
Experience has great value, but in an ever‐changing world in which we live, it can be too expensive to pay for, and so other methods are deemed necessary to complete the gaps in our skill sets and how such a set is acquired. Shortcuts sound as if corners are being cut but if it results in qualified decisions being made with robust results, then what is not to like.
Fast forward and automate these issues and a formidable enterprise emerges. Fundamentally, it identifies the most relevant pieces of data, removing redundant stuff so that qualified decisions can be made. Furthermore, it might also implement these things so that the user is freed from a paper trail that could be fatal in its workload and demand. In such a scenario, the process proceeds trustfully to the stakeholders with a controlling sense of euphoria not seen before.
This whole effort will and can be augmented by blockchain. We do not question google maps when they show us the landscape. We accept many apps per se, without thinking about the consequences. If it then ranks and rates the various modes of transport, if we seek directions, it will inform us the best method. This is blockchain at a basic level. We do not flinch at this information and accept it untarnished.
Digital Control Room
A digital control room could be described as a war room, in that it is a command room, which is the epicentre where co‐ordination and collaboration takes place while a project is in procurement. It can be used in design phases as well as construction itself. It is a place where all issues and hot topics can be highlighted to relevant stakeholders. It balances out any discrepancies, where progress can be broken down to each trade, in order to manage a project efficiently. The workflows allow a lean operation where issues and root causes can be brought to the attention of the design team very quickly. It brings many JIT features to the fore.
It also brings together an enormous visual aspect so that you can manage and resolve all areas of the project. It comprises of a digital whiteboard which has a physical size of up to 4 m, with a virtual length of up to 90 m. Paddling motions of the hand push and pull the pages back and forth. It is populated with Gantt charts, excel sheets, documents and models, which can be interrogated in real time. Integrated Concurrent Engineering (ICE) plays a big role here. ICE is a social method, helped by technology, to create and evaluate multi‐discipline, multi‐stakeholder virtual design and construction (VDC) models extremely rapidly in real time.
Where it will transform itself is with blockchain. Suddenly a list of to‐dos can be vanquished as automated apps and bots complete tasks. Again, I refer to low‐tech, as we might call it today, including all Internet 3.0 situations. These include Internet searches, Google rankings and Facebook algorithms, which furnished a whole generation with facts, and pumped them with add‐on features to extend their attention spans and promote extended engagement. The bottom line invariably was a cash incentive that primarily matched sellers with possible buyers. The incremental possibilities went unnoticed with the general public, but it made multi‐billionaires of the owners of such platforms. This was totally unprecipitated by both governments and the populus in general, and subsequently much damage was done.
Data Is a Commodity
Data is a commodity; therefore, it has value. Crass as it may seem, it is most compelling. Once accepted, it can become obsessive, in that the possibilities and potential explode into reinventing new business models, rebuilding and changing the boundaries in which we operate. It creates an immersive place to develop the project, it provides a place to showcase aspects of the project and it allows all stakeholders to explain how they got to where they are, from a striking standpoint.
Data has also been called the new oil. It powers many of today's transformative technologies, feeding AI and automation, while predictive analytics is growing enormously. Data in its raw form is largely useless, so it needs to be cleaned, sorted and refined to reveal the information needed for strategic business decisions. This begins to draw attention to its quality, and appropriateness.
Design in the built environment generates a lot of data, which needs to be appreciated and analysed properly. The data associated with this process informs specific design solutions driven by multiple, usually competing objectives that need to be taken into consideration during fast review cycles. The fast review cycle is a new phenomenon not previously encountered live on the fly, but rather racked up through hard experience and always in hindsight.
Data can be as simple as floor areas, to more elaborate metrics such as thermal performance, carbon footprint or contextual integration, derived by a plethora of time‐consuming analyses (Kosicki et al. 2021). Previously, a building's agenda was merely functional and aesthetic, but increasingly energy, CO2 and sustainability are pushing performance to the fore with qualified metrics. So how can we deal with this, in production, classification and hence filtration and finally the reuse of these findings for future projects.
Through handheld devices we, the general public, have generally surrendered or waved our rights to privacy of our data and so in exchange for free access to platforms, data has become a currency today, driving advertisements and empowering third parties to bombard us with unsolicited feeds. AI stands as major player with big potential to embrace architects' knowledge and skills, enabling faster design analysis and expanding creative capabilities.
The first approach involves the application of surrogate modelling to replace conventional and time‐consuming modelling processes with economically efficient predictive models. These models are spanning from intelligently simulated results and quickly detected design within historical data, therefore enabling quick and informed decision‐making. On the other hand, second approach introduces design‐assisted modelling, ideal for being integrated into designers' thoughts and ideas to facilitate architectural processes that are missing analytical cycles (Rajković 2023).
In architecture, datasets exist in different and various file formats. Al, on the other hand, is equipped with image, graph, text and voice‐based tools capable of searching into digitally archived data from thousands of projects and file formats. Al efficiently identifies data relevant to specific design task, leading to significant time and cost savings (ibid).
Key features of Al to the construction domain include optimised construction planning, contractor and subcontractor selection from tendering procedures, smart construction site management, health and safety management, advanced technologies implementation, waste management and sustainability. These contributions in the construction sector represent a significant shift towards enabling efficiency, safety and sustainability within the industry. They reflect Al's potential to revolutionise the construction sector and help its environmental impact while optimising project planning and execution (ibid).
AI is invaluable to address challenges in complex or labour‐intensive tasks. This covers automation, robotics, risk aversion, efficiencies and last but not least health and safety. AI can extract valuable learning from the digital process. Its position in the critical mass means that it can act swiftly to recover just‐around the‐corner failures and analysis of the data both now and historically to plan and plot better engagement across the whole enterprise. It can deliver more intelligent and informative reflections to generate new reasoning and tacks. This will lead to improved project efficiency, better quality, rewrite collaboration and meaningful sustainability.
Project management is the 4D and 5D of modelling, namely time and resources, together with quality, to meet the wishes and requirements. But there are soft issues around each and every project, such as the pervading economy, the reigning political regime, physical and social factors which can influence any and all decisions in a project. Each project can be altered by outside concerns such as supply‐chain delays, workforce recruitment and poor reworked effort.
So, managing the available resources is paramount, addressing conflicts in scope, cost, time and quality are always to the fore. Discovering these before they become serious is in everyone's interests. AIs ability to match human behaviour with learning algorithms enhances a new platform beneficial to both parties. Whether AI makes the decisions or presents them to the stakeholders is currently in its infancy, but just an automatic self‐driving vehicles are getting better at negotiating the road network due to the magnitude and numbers of vehicles collecting the data leads to better cognitive decisions, so too will its impact in construction.
This will also affect code compliance, as rules and algorithms learn best practices, so that they can be patched into current practices. Collaboration will also be aided as AI can package deliverables into better forms of exchanged data. By this is meant that formats and classifications can be aligned automatically and seamlessly to all stakeholders, by optimising the process, enabling the optimising of the data into formats expected in each disciplinary silo, which currently is dealt with using ICT contracts offering the least form of resistance.
Similarly, robotics will bring new scenarios to the building site reducing the workers to exposure to dangerous environments and activities. This will also be driven by the shortage of qualified professionals in the digital world. The EU us undertaking extensive programmes and platforms to address this problem, but it means having portals and applications for training and retraining both the blue‐ and white‐collar workforce. But as Paul Doherty said at the BIM Coordinators Summit in 2023:
‘AI will not take your jobs, but people using AI will’.
This was in his presentation: ‘Unlocking The Metaverse: Digital Real Estate, BIM and the AEC Industry’ (Doherty 2023).
In the adversarial economy (adjudicated by the legal sector), AEC professionals have been incentivised to minimise the transfer of information between parties. This is counterproductive. Blockchain has the ability to affect even smart contracts, that is to say, that if there is something to be done when it is complete, it can be appraised in real time, and payment can be made and verified.
Blockchain uses cryptography to create a trusted framework of data. This is called a ledger. Blockchain also means that the records are decentralised, immutable and readily available. Blockchain can be used to:
Establish and verify identities,
Record transactions,
Register and track assets,
Share information and more.
An important benefit of blockchain is that it creates a single version of the truth, thereby eliminating redundancies, outdated records and conflicts. It also allows organisations to improve trust, efficiency and the user experience without replacing legacy systems or losing existing data. Most importantly, it can validate. This is a backend feature, which allows employees and employers to remove a painful part of the hiring process where letters and paper versions of documents must be supplied and verified.
Blockchain offers three elements.
First, blockchain has a trace and traceability, a real‐time method of showing where the student is on their learning path.
Second, it can be a ledger, noting what a person has learned without being compromised.
Last, it can reward such practices with a coin that the student earns for completing modules, guaranteeing evidence to would‐be third parties. While sounding relatively nominal and simple, this ability is intrinsic in a method needing transparency regarding demonstrating incorruptibility and robustness that stands up to scrutiny.
The competitive book named below, ‘BIMand Construction Management’, combines theory and practice in equal amounts to both direct the reader and stimulate him/her as to why. This engagement is a good balance, becoming the go to book on the subject.
I was a co‐author of ‘Getting to Grips with BIM’ which covers the theory, the practice while being relevant to small medium entrepreneurs (SMEs) who most needed it. I would hope to bring these features in a new book.
How Information Can Be Filtered and How Blockchain Will Feature Herein
With regard to contracts, there are three basic essentials to their creation: agreement, intention and consideration. So, both parties must agree, there must be something to be done and there must be remuneration for the said work. Automating this process, with a view towards smart contracting, would see a movement away from analogue content towards digital means. This means that tender material would move from drawings and descriptions to modelling. So previously, the work would be registered and written down, now it is embedded in the model.
