Data, Machine Learning, and AI: Weaving Innovation into Design and Construction

April 30, 2019 | 7 minute read
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In the latest interview in our “Trailblazers” series highlighting innovation leaders in engineering and construction, we sit down with Tomislav Zigo, vice president, virtual design and construction, for Clayco.
Zigo shares his professional background, how the 2008 economic downturn impacted innovation within construction, and several large innovation challenges the industry faces. He also discusses how organizations can cultivate innovation in E&C, as well as how artificial intelligence (AI) and machine learning are poised to transform the industry.

Dr. Burcin Kaplanoglu, executive director and innovation officer of Oracle Construction and Engineering, leads the conversation.

BK: Tell us about your current role. How has your career evolved since you started in the industry? 

TZ:  I currently work at Clayco Inc. as vice president of virtual design and construction. I’ve been with Clayco, a real estate developer and design-builder, for over 10 years. In my current role, I’m responsible for leading and managing our technology team – specifically the virtual design and construction (VDC) group.  

I worked as a registered architect for over a decade on large health care projects prior to joining Clayco. After transitioning to the design/build side of the business, I noticed the complexity in today’s construction industry – including how technology and people shape project delivery and what we execute.

Clayco is focused on transforming the construction industry over the next five to 10 years into a comprehensive digital process, with the intent to capitalize on the vast amount of data we’ve gathered over the past 15 to 20 years.

BK: As you stated, it's about making these three things come together—the people, process, and technology—to help these projects work. What’s your view of the state of innovation in the industry? 

TZ: The construction industry—as we know it today—is very transactional. We form relationships based on the projects we’re working on. Until recently, we formed relationships for the sole purpose of becoming more efficient at our jobs.

Clayco created a unique opportunity for a more comprehensive approach toward technology adoption within the Design/Build framework.

This industry still lags at directing a meaningful portion of our revenue towards pure, industry-focused research and development. The manufacturing industry invests 5 to 8 percent of annual revenue into research and development.

In comparison, the construction industry devotes merely 0.2 to.3 percent of annual revenue into research and development.

It’s relatively easy to explain why most of the innovation doesn't come from the construction industry. The good thing is, an increasing number of large companies and industry leaders are recognizing that growth potential is intrinsically bound to the level of research and development financing.

BK: Do you believe that the challenges we faced back in 2008-2009 forced us to start innovating and for the construction industry to look at things differently? 

TZ: 2008-2009 shed light on the need to create a more efficient business model. The economic downturn provided enough time for several of us in the industry to try to understand which direction the industry should move.

Clayco decided to invest in innovation versus trying to stay afloat by cutting costs in innovation.Those willing to take the risk of learning and innovating are typically bound to harvest the fruits of their labor down the road.  

In one respect, those years have had a negative impact on our industry’s income and our ability to retain jobs. However, this kind of lean thinking made us realize that we can't move forward unless we innovate and learn.

BK: Those innovations and the challenges we had during those years forced us to stop and look at our business models. What are the biggest challenges to innovation you see?

TZ:  One of the most fundamental challenges we’re facing is misalignment between academia and the industry. Academia and the industry should collaborate more to develop apprenticeship programs and focus on problem-solving research opportunities.

Construction and engineering labor should also be ready to hit the ground running on their first day. Labor will be responsible for innovating and changing what we do within the next 10 to 20 years.

However, it isn’t solely up to the industry to make that shift. Academia must start playing a more significant role toward creating the future workforce. Innovative thinking—whether at the process, contractual, or financial level—must be woven into the fabric of design and construction. 

BK:  How can you best foster a culture of innovation? How do you promote this in day-to-day life? 

TZ: Innovation in our industry must capitalize on the legacy data that’s at our disposal. We must radically transform how we transfer knowledge from the older to younger generations via technology.

At Clayco, we invested in adoption and construction of a new, all-encompassing knowledge platform. We’re looking into the legacy of information associated with our projects over the past 30 years to deploy data-mining techniques and analytical practices.

We want to extract those useful bits of information, recreate and re-examine lessons learned, and weave them into our new knowledge-management processes. By becoming “knowledge stewards,” we'll be better positioned to innovate at an accelerated rate and in more relevant ways - with hopefully lasting consequences for our industry.

BK:  What emerging technologies represent the best opportunities for our industry?  

TZ:  Machine learning—and artificial intelligence associated with machine learning—will hopefully accelerate our decision-making process and eliminate errors. But this is predicated on our ability to extract legacy information with relative ease.  

I’m also quite hopeful about the proliferation of robotics in construction.

Arrays of automated, closed-network devices capable of making logic-based decisions are already here. Some elements of robotics technology will help alleviate risks, injuries, and potential hazards on construction sites.  

Clayco's unmanned aerial vehicle (UAV) adoption was our first step toward implementing the principles of automated / robotic data acquisition, eliminating human error, and ensuring that the project’s visual intelligence is aggregated in a consistent way.

These technologies won’t have as much of an impact as they should unless we start thinking about re-engineering our overall project delivery processes. We must think about the construction industry from a product / process-based perspective instead of from a transactional project-based approach.

One of the key components of an innovative practice involves the transformation of industry titles—such as project engineers or project managers—into process engineers and process managers.  

BK: Technology is catching up to industry processes, but we must think about how we deliver it. What use cases do you see in terms of machine learning and AI?

TZ: It starts with the creativity of design. For the most part, we all take pride in delivering iconic buildings and iconic pieces of infrastructure.

That said, there are huge opportunities to weave in artificial intelligence or machine learning in the design process. We’re talking about intelligent and sustainable structures and understanding how we optimize precious material and energy resources.

How do we reverse the clock through a truly sustainable approach?

Rapidly processing vast amounts of information

Processing large amounts of information in near real-time is an additional benefit of these evolving technologies. Fifteen to 20 years ago, we marveled at our ability to generate a whopping 20 to 30 megabytes of project data to complete a large hospital project.

Now our projects are easily exceeding terabytes of useful—and not so useful—information. Every day we take hundreds of images, laser scans, and videos at each job site.

It’s humanly impossible to analyze this data in a timely manner and make any decisions in the time required.

Machine learning—and subsequently artificial intelligence—are hugely beneficial to processing large amounts of information to stakeholders in a (near) real-time framework. Conducting this type of analysis helps us to recognize potential risks and hazards on a jobsite prior to breaking ground.

Leveraging historical data

For example, we leveraged 25 years of historic data to help us analyze equipment utilization and conduct predictive analytics based on past performance. This analysis resulted in higher-quality deliverables in a shorter period.

The manufacturing industry has been utilizing this approach for the past 20 years while the construction industry is just scratching the surface. Each organization must define how they want to use these specific tools—especially in terms of AI.

Any AI-based decision process will only be as good as the information that we’ve aggregated. At Clayco, we take pride in being one of the safest—if not the safest—construction company.

We’ve witnessed significant benefits from utilizing machine learning algorithms towards further improving our safety record.

Reaping the rewards of technology

I’m hopeful that we’re on the cusp of enjoying the benefits of our technology vision.  Predictive schedules, risk analysis, risk mitigation, production in a controlled environment, real time QA/QC feedback loop, industry-wide adoption of Lean, more deliberate business integration, etc.

Anything that provides a greater dose of certainty ahead of time makes us better equipped to transform this industry from project-based to product-based. Hopefully we’ll be in closer alignment when the fourth industrial revolution is upon us.  

Read our eBook: "Innovation in Construction, Perspectives from AEC Innovation Leaders"

Explore innovation in action at the Oracle Construction and Engineering Innovation Lab, a simulated worksite with integrated technologies.

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Corie Cheeseman

Corie Cheeseman is a senior content marketing manager for Oracle Construction and Engineering.

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