Addressing High-Risk Projects with Construction Technology
On February 26, 2021 by Oracle Construction & Engineering
In our latest “Trailblazers” profile, we speak with Tim Mumford, senior project manager, Office of Projects Victoria. In Part I Mumford discussed his role as well as how the Victorian government of Australia is taking a ‘bottoms up’ approach to infrastructure as part of the AEC revolution.
In Part II of our interview, Mumford reviews the most promising AEC technologies and which will have the most impact in the short and long term. Dr. Burcin Kaplanoglu, executive director, innovation officer at Oracle Construction and Engineering, leads the interview.
BK: Which technology has the best opportunities, including the changes we’re looking for?
TM: In terms of technologies that have made the biggest difference to-date, it’s around information management. It’s not really a technology per se, but object-based information systems, digital engineering, etc., is going to help empower individuals to make the right decision and not duplicate efforts.
Once that becomes business as usual, then the other things follow. It becomes a natural extension to talk about AI, machine learning, etc., once you’ve got that information management process dialed in.
For example, in rail corridors it’s very hard to train people. It’s challenging to train future operators and staff because it’s a dangerous environment. It also becomes a very expensive exercise to train someone. This might be for the most basic task, like cleaning graffiti.
Through VR and AR, we can now train someone before the project is finished without putting them at risk. This is a step change in the way that we think about our assets and how we train our future workforce. You can have a workforce ready to go from day one and get over that O&M handover hump. It’s a seamless transfer.
The last technology I’ll highlight are object-based libraries so we’re not doing the same things repeatedly. If we’re in that same rail corridor we might do a gantry over that rail corridor. There’s no reason why we should be “bottoms-up engineering” that thing repeatedly.
We’re devoting our energy repeatedly engineering the same scope. There’s so much upside on getting the balance of 80/20 right. Let’s re-use the 80% and customise the 20% that makes a difference.
BK: If we were to pick AI and machine learning, where do you see the biggest impact in the short and long-term?
TM: The biggest opportunity for these two is understanding where the risks are in projects and opportunities in cost estimating, scheduling, and assurance. If we routinely develop projects, then theoretically, we should understand the way risks play out.
Whether they’re priced or unpriced, it doesn’t matter—it’s a less risky venture. This is a feedback loop which could be trained by machine learning. You could understand what we did in the past: what was hazardous, risky, expensive, etc., and then start to integrate that feedback loop back into a future project.
AI and machine learning could vet designs and inform us what’s going to cost a lot of money and what’s risky from an operational, construction, and design perspective.
Technology such as AI and machine learning could also tell us how our facility or asset will perform. Is it the most efficient it can be? How resilient is the facility or asset? How does it integrate with other assets? Understanding the way that we can use assets is a real opportunity for AI and machine learning.
Read Part I of the interview here.
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Complex public infrastructure development programs are high-risk endeavours marked by cost overruns, delays, financing difficulties, and other challenges. Changing strategies at the top of the organization often aren’t communicated to individual projects, and risks found in one part of the project life cycle can be translated to other parts. With many projects underway at once, problems with one part of the program can quickly spread, wreaking havoc with the overall budget. Discover how implementing a program risk model can help improve project success.