Preventing BIM being just a buzzword

One of the criticisms you may hear about BIM is that it’s just a buzzword, or that it’s an idea that quickly will be replaced by something else. A good response to this argument is to make an analogy with email. Although lots of new communication tools have evolved from that platform, the basic process of sending digital mail is still used by the majority of businesses and has lasted for a long time since its invention.

By looking at other industries, you can be confident that investing in improving your digital processes is always worthwhile. A balanced approach is getting up to speed with new technology that will take BIM to the next level, so that the appropriate concepts in this chapter are only a stepping-stone away when the time comes for you to make the leap in your business.

Industries that have already gone through digitization are now full of pioneering research and amazing innovation. But the 2015 Ford Trends report found that 64 percent of adults across the globe say technology is moving too fast to keep up with.

Gartner, the IT research and advisory company, produce the Hype Cycles diagram; see www.gartner.com/technology/research/methodologies/hype-cycle.jsp. It describes the way most groundbreaking technology gradually evolves from being an obscure innovation into common usage. Often you see a peak of hype where news media heralds the technology as the solution to everything and then disillusionment when it doesn’t quite live up to those expectations, before an eventual adoption when people accept it into mainstream use.

With all new technology, you have a choice whether or not to adopt it in your organization. Traditionally, technology can take a long time to be proven, but increasingly new technologies are moving through the cycle really quickly. If you ignore opportunities now, then the gap between the technology-led expertise and the stuffy, analog processes of old is only going to get bigger, as more and more innovation pushes everything forward. It’s always a balance of risk versus reward. The earlier you adopt, the higher the risk you’ve moved too early. Wait too long and you risk missing out on opportunities.

Here’s a good term for the future of industrial activity: Industry 4.0. It describes the fourth industrial revolution, because three previous industrial revolutions have occurred: beginning with mechanization and steam power, evolving with assembly-line manufacturing, and most recently electronics, IT, and robotics making up the third. Industry 4.0 moves automation to another level while also making the most of sensor-rich data and intelligent feedback. In particular, Industry 4.0 is likely to be the first time all industries will connect, from manufacturing to energy, to explore how technologies developed in one industry can be quickly repurposed in another.

Collaborating online

Thinking about online information is pretty magical. When researching this book, we were looking at articles that were probably being read at exactly the same time by someone in India, Russia, and Australia. Perhaps by you! All that content is open to just about everyone with an Internet connection.

Key BIM themes like online digital collaboration can seem quite difficult to make sense of. It simply means working together using Internet-based methods of exchange. Users all over the world can join forces, applying trained knowledge and disparate skills to achieve common objectives. This online collaboration sounds a lot like what BIM is trying to achieve. (Refer to Chapter 6 for more information about collaboration and BIM.)

You’re more than likely already collaborating online. You may share photos with friends and family on social media platforms like Twitter, Facebook, and Instagram, and people probably comment on them or link elsewhere, like sharing data. In academic or office environments, you may do group work on virtual files such as reports developed by multiple authors. Have you ever made a change on Wikipedia or written an article yourself? People make thousands of changes all the time. These aren’t perfect analogies for BIM, but they’re good examples of cooperative working.

One of the best examples of digital collaboration and perhaps the most exciting is online gaming, especially the massively multiplayer online role-playing games (MMORPGs) like World of Warcraft and EVE Online, and multiplayer games like Call of Duty on Microsoft Xbox Live, Valve’s Steam, and PlayStation Network. Refer to the nearby sidebar for more on digital collaboration in video games.

Communicating and sharing data

A big part of BIM is sharing information and using it to make better decisions. One area where future technology is going to impact all industries is more systems that constantly collect data as people live, work, and shop while connected online. Already, these systems are storing unprecedented levels of information.

What’s the point in having loads of data if you aren’t able to interpret it and gain from the “hidden” information within? Billions of pieces of data may exist that nobody’s looking at.

Think about how retail companies collect and use your data. Initially, marketing companies tailor their advertising more closely to your interests so you’re more likely to buy their products, but service companies also tailor finance and information products to your personal needs. You can apply exactly the same logic to building information. The data that sensors in future built assets collect will benefit commercial applications and will also improve user experience in new projects.

Without trying to sound like an episode of your child’s favorite TV show, sharing is important. Increasing access to important data is going to include a bit of risk sharing and a few nondisclosure agreements. Sometimes the data may not be useful to you, but could really help out someone else. Today’s episode of For Dummies was brought to you by the letters B, I, and M!

Empowering your business with information

One of the major changes in the last few years is that you don’t need to be a statistician to interpret all the numbers. This trend will continue. New software and tools are making it far easier for you to see the power in your data through visualizations. We’re convinced that built environment owners, designers, contractors, and operators will begin to connect their BIM data together to understand their portfolios as a whole and see where they can make new savings.

One key development in BIM and knowledge management is the need for new roles in the construction industry. Most likely more industry professionals and built environment companies will employ data scientists for deep analytics. You’ll be able to add more value to projects than ever before if you have new insight by using connected data to become indispensable to your clients, co-workers, or customers.

You need to understand what your new information means and apply the lessons to the next day, the next project, or the next building. You can take lessons from the successes and, more so, the failures.

Moore’s law is about how many transistors you can fit on an integrated circuit, and roughly that number has doubled every two years — a good general rule for computing power’s exponential growth. Recently, Mark Zuckerberg of Facebook described how much information people are putting out into the world and suggested that the amount of data people share will also double every year, just like Moore’s law. With all the sensors and devices around you, the amount of information you individually share may double every year or two years. In your personal life, you may feel that sharing has become natural to you and you may not even notice how much private information you’re sharing. Well, that’s happening with building data now too.

Sensing building users’ behavior

Big data analysis from the built environment could generate new efficiencies in the way people use buildings and built assets, especially in terms of energy and cost savings. But it relies on knowing a lot more about users than owners and operators ever have before. The future of BIM and big data could also have a negative effect if users are unhappy about sharing data about their activities and movements and if all this data capture opens up buildings to new security risks.

In Chapter 15, we mention how important carbon targets are to governments all over the world and that massive problems exist with how humanity uses existing buildings. The majority of existing building stock is still going to be around in 25, even 50 years’ time, so you need to know how it’s performing and then make renovation as efficient as possible. Two of the fundamental savings that BIM and big data can generate are

• Carbon cost: Through big data, clients, designers, and operators possibly can understand how different building types, materials, orientations, and energy schemes are performing across multiple locations. Guaranteeing the performance of buildings against anticipated energy usage will be much easier.
• Financial cost: Large contractors and multidisciplinary organizations are already comparing all of their projects around the globe in terms of cost data to understand the true cost of construction. Assessing where savings can be made, either by sourcing materials and products in different locations or by using this information to make tender prices far more accurate, becomes possible.

In the future, understanding how energy use and cost data varies across the built environment as well as how people use buildings differently will be possible. It’s not science fiction to imagine individuals being tracked around urban environments, including the interior of buildings, to assess how spaces and functions are being used and where bottlenecks of people occur. This information can then form a feedback loop that can help new designs better meet the needs of users and improve efficiency. You’re already being tracked around airports for security purposes, entertainment parks like Disney to optimize sales and marketing, and some conferences to inform the layout and content of future events.

In the 1950s, it became popular to have time and motion analysts in factories, to note where new efficiencies were possible. It started as a good idea. (Every item should be within reaching distance — why should someone walk from her desk to pick up an item and take it to where someone else fitted it?) But then quickly workers resented the analysts (Sorry, your bathroom break was too long). A new way to think of BIM as behavior management is that governments and local organizations could measure positive improvement in social or behavioral activity, such as faster recovery in the hospital or better results in schools. (Chapter 19 discusses behavior management in greater detail.) So where does that take you eventually?

The number of sensors in the built environment will likely increase along with more wearable technology for building users. Some people say all this tracking is all bad news. They see behavioral monitoring, like a black box in your car that could lower your insurance depending on your driving style, as Big Brother–style surveillance. Here are a couple of imaginary scenarios:

• You’ve been in six different fast food outlets this week. Your health insurance premium just increased.
• You’re spending money at a casino. Your personal loan request has been refused.

Whatever sensors and observation you put in your projects, consider the privacy issues that could result. In particular, increasingly privacy-conscious Millennials may resist the idea of sensors hidden everywhere and in everything. BIM in the future will require some ethical decisions about the collection of built environment data and how it’s made available for shared use. Here’s the general rule: If you’re not comfortable sharing information about yourself, don’t expect others to be comfortable with your building collecting that data about them. People are generally more prepared to share data about their activity if it’s anonymized (which means that no personal information is collected that can be used to trace data back to an individual, just generic facts) or if it’s clear what the purpose of collecting data is. Secretive collection of data seems more sinister than openly explaining the benefits of information gathering.

Automating rule checking and machine learning

You need to verify that you’ve matched building code requirements like fire escape, universal accessibility, and thermal design at regular stages in your project, usually for external organizations to assess. By cross-referencing your project against the relevant codes or by running simulations through the information model, you can demonstrate that you’ve complied with the requirements. You can show that all users can safely exit the building in an emergency or that you’ve managed heat loss and gain across your project.

This compliance checking isn’t just about building codes either. Certain clients and owners have strict and onerous briefing requirements about program and performance. When you add this to the simulation and telemetry from Chapter 19, you can understand the potential in that overall process. We think a huge increase in the automated checking of design and construction properties against constraints like these will occur.

Another technology, machine learning, allows you to take this automation one step further. Imagine you could teach a computer to make decisions for itself, instead of having to control it with programming. Well, you can.

Machine learning is a way of building algorithms that learn from real-time data and recognize patterns. Similar to you making a judgment call or a prediction based on what you already know, in simple terms a computer will get better at a task the longer it gets to perform it. Consumer websites or streaming music services use machine learning to understand your likes and dislikes and suggest new titles and appropriate advertising. It’s common and many of the online services you use will apply machine learning to understand you better.

For example, generally at Christmas people like to listen to festive music and famous holiday music on repeat. It’s vital that the streaming music service can differentiate between this kind of repeated seasonal listening and the new favorites you’re prepared to listen to all year, probably because you’ve skipped the track a few times in January. That’s machine learning.

In construction, machine learning is a perfect match for the sensors and building telemetry, as we discuss in Chapter 19. Can your built environment learn to think for itself? We think it can. Already many examples exist of buildings predicting what will happen soon.

Embedding thousands of sensors working together can provide some key benefits. A good example of this is the collaboration between the elevator manufacturer ThyssenKrupp and Microsoft. The sensors in their systems can tell

• Exactly where the problem is: The key benefits of sensors embedded into the working parts of elevators is that when failure does occur, diagnosing the problem doesn’t take hours. The sensors tell the story of exactly what’s wrong down to the precise part that needs attention. A lot of cars have diagnostic technology, and buildings aren’t far behind.
• How hot the motors are getting: Elevators can warn of changes before they become problems, like a motor sending data to warn about irregular temperatures before it overheats. Microsoft provides cloud-based dashboards for all sorts of analytics like this, and the manufacturer says these early-warning systems provide high levels of uptime.
• How often the doors have opened: Another benefit is pre-empting maintenance. Say that the failure point for doors is thought to be 100,000 opening cycles. ThyssenKrupp and Microsoft can program the elevators to recognize when they’re approaching this number and to recommend maintenance. However, maintenance staff can add to the model the status of the doors at the point of maintenance, so the algorithm gets adjusted accordingly, perhaps because maintenance was required sooner or actually the doors were fine.
• How often the doors have opened on certain floors and at what times of day: The previous two benefits are companywide information, but you could also use machine learning at a building level. If the elevators can learn when the arriving and home-time rushes are and on what floors, they can make sure the cabs are already in the right places and moving at the optimum speeds to match users’ behavior.

Using AI and BI

Take machine learning to its logical conclusion, and you get artificial intelligence (AI), which is simply the intelligence that computers or other machines can demonstrate, and eventually, how closely it can mimic human intelligence, learning, and perception. By combining AI with big data from the built environment, you can analyze and interpret BIM content more quickly, efficiently, and cheaply than human data scientists could ever achieve.

For example, IBM made Watson, a supercomputer. Watson processes information more like a human than any computer in history. By interpreting language naturally and gradually learning, Watson can analyze the messy ocean of data across the Internet like you’d conduct research for a report or an article, carefully selecting the best information and making intelligent assessments.

This kind of AI can begin to make judgments and draw conclusions with far better access to data than you can achieve on your own. Imagine AI optimizing design forms or finding the most efficient structural solutions. AI will increasingly understand plain-language questions like “What is the most sustainable but durable material for this façade?” and “What are the advantages and disadvantages of this site?”

According to MIT Technology Review, Watson is about to overtake human understanding of oncology, the study of tumors and cancer. When the article says “overtake human understanding,” this means in terms of book-smarts, because Watson is able to read the latest research in volumes no human could achieve. So how does this change things? Doctors won’t need to read every journal in sight. Watson will provide information and diagnosis, allowing the doctor to care and treat the patient, human to human.

Recently, business intelligence (or BI) software has also become very popular. With BI software, the process of retrieving answers to business questions from big pools of data is simpler and more attractive, often allowing for the design of infographics and visual dashboards. Reporting is no longer in boring, dumb spreadsheets, but live and updatable real-world queries and reports. This is important for BIM because it highlights the need for models to be able to answer plain-language questions written by humans, especially nontechnical users and clients.

Decisions in the future of BIM will be made at lightning speed as systems begin to automate the choices you previously needed to make with your project team. Now they’re going to become predictive — perhaps completely out of your hands! What decisions does your building, project, or city need to make? Here are some examples:

• Open another window to regulate temperature.
• Open another lane for traffic to reduce congestion.
• Open another hospital to ease healthcare demand.