Episode 1 “The Future of Consulting Engineering”
Featuring Akira Jones
Kirsten:
Welcome to Expanding the Possible. This podcast from HH Angus explores how engineering is advancing the built environment - from innovations in building systems to the internet of things, from digital services, connecting the modern workplace to combating the impact of climate change, we delve into exciting developments in today's and tomorrow's building infrastructure. Hi, my name is Kirsten Nielsen. Today, I'm joined by Akira Jones. Akira is the director of digital services at HH Angus, and he's here to discuss the future of consulting engineering.
Akira is mechanical engineer by training and, in addition to leading our digital services team, he heads HH Angus' BIM initiatives, so he spends a lot of time thinking about how digital technologies can enhance how consulting engineers work and how they can deliver better outcomes for our clients and projects. Welcome Akira.
Akira:
Great to be here. Thanks for having me.
Kirsten:
So, by any measure, both the pace and degree of change in how we design and construct buildings today is nothing short of staggering compared to how it was evolving, even 10 years ago. So why is this change happening now and what are some of the key challenges facing consulting engineers today?
Akira:
I think there are a number of factors driving change within our industry, and I think that the most important factor is there is a generational shift occurring. There are folks in the industry that have influence, that have the ability to make changes, bringing this new technology into how we do construction. Construction has traditionally been very slow to change because the means and methods for design and building our built environment didn't need to change. They worked. I saw the stat some time ago that a lot of folks might be familiar with, that the only industry slower to change and adopt technology other than the construction industry was agriculture; another way to look at it as an industry ripe for change, or for disruptive change is often how it's referred.
I think another factor is that there are so many requirements as it relates to climate change. So that’s energy efficiency, low carbon, net zero, like net zero electricity or net zero carbon, that are becoming much more part of our social consciousness and therefore requirements for not just people building new buildings, but for people operating existing buildings. And even the tenants of those buildings are driving the change from their landlords. A lot of the low hanging fruit, in terms of energy efficiency, have already been applied, and to get that extra bit of efficiency, or even some of the new technologies in HVAC and building systems like LED lighting and heat pump systems will require much more sophisticated systems to get that extra level of efficiencies out of buildings.
And I think the third factor in the adoption of something new is the availability of viable technology.
So, you know, we have been hearing about things like AI and machine learning for a long time, but I think it is now at a stage where it is much more commercially viable than it was in the past. And so these things can be applied to our work in a meaningful way as opposed to in just a very theoretical way. Even the availability of cloud infrastructure for data acquisition, the edge devices, there are thousands and thousands of devices out there that can collect any kind of data you want, and now you have a place to put that data and leverage that data to do whatever you want.
So the availability, coupled with the folks who are willing to bring that into the construction industry and for what we do in design, and the requirements for buildings, you bring all these things together and that's why we're seeing this change now.
Kirsten:
How are the new technologies impacting what consulting engineers actually do?
Akira:
That is a very interesting question to me, because a lot of the work that our team does internally is to see how we can use technology to improve how we do our work. I would say from the design perspective, the largest fundamental shift that we're still going through is going from AutoCAD, so 2D flat drawings, into an environment where we're designing in 3D and adding a lot of data to the model that is now being required to be carried through design construction and then the operation of the building.
So, you know, when you look at the birth of an asset, let's say like a pump, it really starts in that design phase because it is digitally represented and has data associated with it, and it gets carried through the entire life cycle of the project. So, I think this is one of the biggest changes.
However, I would say that we have not been able to leverage a lot of these technologies because both our design processes and the processes by which we convey our designs haven't caught up yet. For instance, we are still required to put our 3D designs onto a 2D plan, and so there's an inherent loss of data when you do that. So this is a big challenge in the industry in terms of how we can adopt or better leverage the technologies that are available to us.
So that's kind of like the baseline. Beyond that though, because there's a lot more data, we should be able to use a lot of the techniques that are available out there in terms of data acquisition and data modeling to help inform and improve the way we do design work or get data from how buildings actually operate and how that can inform how we do our designs.
And so what we're seeing is a requirement for engineers to go beyond what is traditionally engineering work. Things like thermodynamics and fluid dynamics and those baseline things that were always, (and I'm speaking from the perspective of mechanical engineer), for electrical structural as well. There are these fundamental things that we always thought were part of engineering, but around understanding data. So, let's say, data science, even software programming, what software programming is capable of in the work that we do is becoming a much more important factor for engineers entering the industry.
Kirsten:
Let me just pick up on something you were saying about people coming into the industry, because students who are graduating and beginning their consulting engineering careers are coming with an entirely different educational background than people who came here 10-15 years ago. Are you seeing the new grad hires coming in armed with this kind of understanding? Or do we have to take even a further step back in the industry to make sure that not only the students, but clients are also able to understand these concepts and what these technologies can do for them?
Akira:
We are seeing a lot of graduate programs that engineers are taking after they've finished their undergrad degree in engineering in things like machine learning, AI data science. So we are seeing those skills coming through. We are also seeing a lot of folks who have a side interest, let's say software programming or software development, that we see through their resumes in terms of some of the clubs, or things that they've participated in or side gigs that they might have.
So those are the folks that we're really interested in finding, because that way of thinking is really important for how we could adopt the technology, right? It's not necessarily that we need somebody that can do the software programming of a specific tool for our design software or write a macro for Excel or really use Excel in a sophisticated way. It's just knowing that it's a possibility, and what type of work can be made more efficient with these types of tools.
Kirsten:
Which of these technologies that you've been talking about are you seeing making significant differences in how design work is delivered and in the range of solutions we're able to offer to clients?
Akira:
The main one I think is, and this kind of dominates our lives from a design perspective, the conversion of our work from going from a 2D space to a 3D space. And I would say that we haven't quite figured out how to leverage that technology yet. There are a lot of reasons why that is. Particularly, I think that our processes as designers are still stuck in delivering in a 2D environment, in not a data rich environment.
If you think about AutoCAD and the output into 2D drawings, now we're working in a 3D model-based environment that can contain a lot of static asset data. So, for example, engineering data about the pump or the air handling unit or the switch gear that's within the design. So we're constrained by how we're supposed to convey our designs.
That is a technology that we need to leverage better. By having good practices within that baseline environment, we open ourselves up to using some of the other technologies that might be available to us. An example for that would be if we had great modeling and design practices within our BIM models that contained good data about not just the assets, but the infrastructure that we've designed. So specifically, if I look at a duct system, everything in the model is connected and we have the right pressure drops and the right flow rates and, the system understands itself in terms of how it's designed.
Over time, if we have a lot of models that look like this, then we can start extracting data from all these models and we can start developing metrics in terms of how we've done previous designs.
So in a lot of cases, as much as we try to work from a previous design, we're using rules of funds or, or things to do the initial designs; whereas, if we have good data on, let's say, a healthcare project or a series of healthcare projects that we've done in the past, then we can, optimize that initial design phase and come closer to what will end up as the final design. So that's certainly a way that we can do that. Another way that we're potentially looking at leveraging something like machine learning, in that same sense is that if we have good data coming from the architectural models, then we are able to model the association between the space types in a clinical setting and the CSA requirements for those spaces.
And so, these are some of the things that we can do, but we have to set a fundamental baseline of good modeling practice and good design practice to make sure that we're getting good data. A lot of the challenges with leveraging data is having good data sets, and we spend a lot of time trying to find those good data sets.
Kirsten:
Something that comes to mind is integration and, with the preponderance of new systems available and, as you mentioned, people maybe coming to clients with the latest and greatest shiny new object, how important is it to have a good IMIT strategy in order to ensure interoperability?
Akira:That is probably the most important thing, right? I was chatting recently with the general manager of a new tower downtown, and one of the issues they're having is with integration, because the industry, I think, hasn't necessarily caught up in terms of standardization for integration, right?
Common languages and protocols to communicate and common practices in terms of how to manage changes are key to integration; so, application programming interfaces, how two different systems speak together or the ability of the IT network to handle all of the data. A different client that we were talking with, had an integration issue with their elevator control system with another data acquisition system in their building.
And the elevator company changed their name; it was Thyssen Krupp Elevators, but then they changed it to TKE. So, somebody renamed a folder in their system where data was being dumped from the elevator system from Thyssen Krupp elevators to TKE, and this broke the whole integration, right? That simple thing took hours to figure out! And then you also have companies that are, let's say, well-established in the industry, legacy type organizations, like the Siemens and Honeywell and whatnot.
But you know, there's a whole bunch of them out there. And there are some of those organizations that are somewhat new to having their data accessible on the cloud, writing application programming interfaces, so that people can interact with their cloud environment. And so you'll find that some of these organizations or even tech startups that are bootstrapping and have very few employees, you find a common problem where they'll make a change that's undocumented and all of a sudden, the integration breaks and everybody's trying to figure out why.
So this is all to say that your IMIT (Information Management Information Technology) strategy is vital, not just internally, but your practices, how you're managing data integrations, but also the vendors that you're choosing to work with and making sure that there are requirements for them in terms of how they update their technologies so that they all work together is really the key to how all of this works. The fact that all of our data was siloed in the past for building systems like BAS (Building Automation System) and lighting control systems, and your fire alarm systems - fire alarms are totally different because we can't really touch a fire alarm system because of regulatory requirements - but all of these systems just kind of worked in isolation and that was great, right? They worked most of the time.
But the integrations and the reliance on so many different verticals makes things that much more complicated, and that is still something that folks are figuring out. For sure.
Kirsten:
Akira, can you quantify how these new technologies you're talking about are providing clients with a return on their investments when it comes to creating more efficient and cost-effective facility management tools?
Akira:
That's a great question, and one of the things I want to focus on is the return on investment, because that is the most challenging part about bringing a new technology to a client. I think a lot of folks are guilty of (and I'm guilty of this too!), is the ‘shiny new thing’ syndrome. It's like,” oh, this is really cool. How can we use it?” as opposed to saying, “how can this actually drive value for the business?” You have to always think of the problem first and then what technology can solve.
One of the things that we have found really interesting at the baseline, and it forms the basis of this topic that we talk a lot about now, digital twinning, is 3D imaging - the availability, of really high fidelity and easy-to-use 3D imaging technology like the Matterport scanner, have been able to virtualize a lot of the built environment.
And this has opened up a ton of new possibilities in terms of having people do virtual visits during COVID for, let's say, contractor walkthroughs or even giving a building owner a different kind of as-built. We could do a scan prior to the walls and the ceilings being boarded, and that gives him a high-definition picture of what's actually built on site, which is often very different than the red line markup as-builts that you'll get at the end of a project. Some of the other technologies that we're seeing, like, CFD or computational fluid dynamics, are really cool because we're able to do much more efficient data center design and able to leverage CFD tools on the cloud, and deliver even more computing power than we've ever had.
This really drives value for the design, but also the end user of that data center has the most efficient and safe, or redundant system in place, based on having the right HVAC equipment in the right place and providing the cooling that the systems need. So I think those are some things that we're really interested in leveraging now. I mentioned digital twins and IOT. These I think, are a bit more challenging to derive value.
And going back to the beginning of my answer, there are so many different technologies available out there and the owners of buildings and even hospitals and post-secondary campuses or whoever, they have a hundred people knocking down their door trying to sell them something new.
That does something very cool, and again, this comes back to that shiny new thing syndrome, does it actually drive value for the client? You look at something like a healthcare facility or a post-secondary campus. These places own all of their assets and often have a lot of different buildings that have a lot of folks interacting throughout the day. So there's a lot of potential to apply, let's say, presence technology and more sophisticated HVAC and line control technology to provide a more efficient system, or a better experience for the students.
But you look at, uh, something like a commercial property where you might have a lot of different tenants and you don't own all of your assets, and there's a lot of interaction between, the tenants’ requirements, the building’s requirements; it's often a lot harder to generate an ROI and the application of these new smart buildings technologies.
The question is, how can the building drive value for tenants while also still driving value for owners by, let's say, increasing rents or making their profit margin larger, or increasing their brand. So, these are a lot of challenges that the industry is facing. I would say that for some of the most emerging technologies out there, it's still a challenge to find value. And I think, in a specific example for us for instance, we're really interested in machine learning because we feel machine learning can help us in our work.
But the challenge is what question can a machine learning model answer for us? And we spend lot of time s trying to figure out how we could potentially do that.
Something that we're about to pilot on a project is, -and this is very specific for engineers out there - is how to make more efficient the associating of a clinical space type with its CSA defined requirements. Every clinical space in Canada is governed by CSA 317.2, and that defines, what the temperature should be, what the air change rate should be. But if you think about how an architect will design, they'll call something a soil utility room or they might use an abbreviation for the word ‘soiled’, or there are a variety of ways that room might be named; yet it still has that single association that it has to make with the CSA requirements.
So, we are looking at using our previous designs to build a machine learning model that that we can train, that will make those associations for us. Because we feel like that is a great way to save time. But again, we had to really think about that specific question that could be answered. And I think that often the challenge in driving a return on investment in something like this, for ourselves and for our clients, is asking the right questions.
Kirsten:
I just want to pick up on something you were mentioning just now and that is other players coming into the design landscape and kind of, cutting our grass, if you will, they're not only engineers. Where do you see this going in the future, in terms of the kinds of skillsets they bring and how consulting engineers need to, maybe change what they're doing in order to compete?
Akira:
That is something I definitely think about a lot because of the fact that where we're pushing into when it comes to, hardware technology, data analytics, and data science and whatnot, there are a lot of industries out there that have a lot more skills in that area, right? So, you're looking at professional services firms or even tech startups or big tech. You look at AWS and Azure, they have their own digital twin platforms. A lot of the other digital twin platforms out there are startup technologies, like Willow or ThoughtWire; there's a whole whack of them out there.
And even professional services firms are providing services in terms of designing smart buildings, at least in terms of the requirements. Also professional services firms that have data scientists that are looking at doing data analysis for manufacturing or consumer product goods or whatnot.
So, the emergence of using data and big data in our world has opened up our industry to these other industries that have traditionally been very focused. And that's something that we have to really take to heart because it's a bit of a threat to our work, at least in some of these emerging technologies, where we have an opportunity to play in this sandbox as well, so to speak.
Our advantage is that we understand how buildings work and our clients trust us to help them make good decisions on their behalf; that's why they come to us. So the opportunity for us is to see how we can fit into this new space, and bring these new technologies to our clients. And it's not to say that these other players won't also exist in this new ecosystem, but we can at least invest to help carve out our own space.
Kirsten:
Where do you see consulting engineering heading; what kind of skills are engineers going to have to have going forward in order to be competitive?
Akira:
I think this is largely driven by the technologies that we're going to be required to use as engineers. I truly feel that data science and software development or software programming skills will be a really key factor for the engineer of the future because we're not talking about having engineers programming large pieces of software, right? We're talking about engineers being able to understand what programming and coding is capable of, in terms of aiding the type of work that we're doing.
I think some of the examples, in terms of what I've talked about previously, are interacting with the API of a system and extracting data and putting that somewhere, like in a cloud environment. These are things that I think a lot of engineers can learn with a relatively little amount of work. I mean, relative to the engineering degree that they just did! Applying some of these technologies is not as complicated. It's just a matter of getting the practice and doing. There are a lot of other tools that we're exploring, things like parametric simulation; that's using different kinds of open-source algorithms to, do simulations by adjusting and optimizing a series of parameters, let's say, looking at optimizing the energy use intensity of a space by changing the window-to-wall ratio and the size of the shading. So, you're able to use different tools like Grasshopper and Rhino and open-source parametric modeling tools to optimize that space in a way that hasn't been done before to run a thousand simulations and get the 10 top 10 best options for you.
So that will require having some knowledge in programming and visual based programming. I think things like machine learning will require a lot of expertise in data science. And that's not to say that every engineer is going to start doing machine learning modeling, but they can at least be aware of what a machine learning model is capable of telling people. And that's the key - understanding how to apply these technologies and asking the right questions. A lot of the stuff that we're going to be relying on in the future, data acquisition from IoT, bringing that data into the cloud environment, will require folks to have skills in both.
Kirsten:
Our guest today has been Akira Jones, director of Digital Services at HH Angus. If you'd like to contact Akira, his contact information is on the podcast page. Thanks, Akira, for your insights on the future of consulting engineers. And thank you to our listeners for joining us today. Please stay tuned for future podcasts on expanding the possible coming soon.
Welcome to Expanding the Possible. This podcast from HH Angus explores how engineering is advancing the built environment - from innovations in building systems to the internet of things, from digital services, connecting the modern workplace to combating the impact of climate change, we delve into exciting developments in today's and tomorrow's building infrastructure. Hi, my name is Kirsten Nielsen. Today, I'm joined by Akira Jones. Akira is the director of digital services at HH Angus, and he's here to discuss the future of consulting engineering.
Akira is mechanical engineer by training and, in addition to leading our digital services team, he heads HH Angus' BIM initiatives, so he spends a lot of time thinking about how digital technologies can enhance how consulting engineers work and how they can deliver better outcomes for our clients and projects. Welcome Akira.
Akira:
Great to be here. Thanks for having me.
Kirsten:
So, by any measure, both the pace and degree of change in how we design and construct buildings today is nothing short of staggering compared to how it was evolving, even 10 years ago. So why is this change happening now and what are some of the key challenges facing consulting engineers today?
Akira:
I think there are a number of factors driving change within our industry, and I think that the most important factor is there is a generational shift occurring. There are folks in the industry that have influence, that have the ability to make changes, bringing this new technology into how we do construction. Construction has traditionally been very slow to change because the means and methods for design and building our built environment didn't need to change. They worked. I saw the stat some time ago that a lot of folks might be familiar with, that the only industry slower to change and adopt technology other than the construction industry was agriculture; another way to look at it as an industry ripe for change, or for disruptive change is often how it's referred.
I think another factor is that there are so many requirements as it relates to climate change. So that’s energy efficiency, low carbon, net zero, like net zero electricity or net zero carbon, that are becoming much more part of our social consciousness and therefore requirements for not just people building new buildings, but for people operating existing buildings. And even the tenants of those buildings are driving the change from their landlords. A lot of the low hanging fruit, in terms of energy efficiency, have already been applied, and to get that extra bit of efficiency, or even some of the new technologies in HVAC and building systems like LED lighting and heat pump systems will require much more sophisticated systems to get that extra level of efficiencies out of buildings.
And I think the third factor in the adoption of something new is the availability of viable technology.
So, you know, we have been hearing about things like AI and machine learning for a long time, but I think it is now at a stage where it is much more commercially viable than it was in the past. And so these things can be applied to our work in a meaningful way as opposed to in just a very theoretical way. Even the availability of cloud infrastructure for data acquisition, the edge devices, there are thousands and thousands of devices out there that can collect any kind of data you want, and now you have a place to put that data and leverage that data to do whatever you want.
So the availability, coupled with the folks who are willing to bring that into the construction industry and for what we do in design, and the requirements for buildings, you bring all these things together and that's why we're seeing this change now.
Kirsten:
How are the new technologies impacting what consulting engineers actually do?
Akira:
That is a very interesting question to me, because a lot of the work that our team does internally is to see how we can use technology to improve how we do our work. I would say from the design perspective, the largest fundamental shift that we're still going through is going from AutoCAD, so 2D flat drawings, into an environment where we're designing in 3D and adding a lot of data to the model that is now being required to be carried through design construction and then the operation of the building.
So, you know, when you look at the birth of an asset, let's say like a pump, it really starts in that design phase because it is digitally represented and has data associated with it, and it gets carried through the entire life cycle of the project. So, I think this is one of the biggest changes.
However, I would say that we have not been able to leverage a lot of these technologies because both our design processes and the processes by which we convey our designs haven't caught up yet. For instance, we are still required to put our 3D designs onto a 2D plan, and so there's an inherent loss of data when you do that. So this is a big challenge in the industry in terms of how we can adopt or better leverage the technologies that are available to us.
So that's kind of like the baseline. Beyond that though, because there's a lot more data, we should be able to use a lot of the techniques that are available out there in terms of data acquisition and data modeling to help inform and improve the way we do design work or get data from how buildings actually operate and how that can inform how we do our designs.
And so what we're seeing is a requirement for engineers to go beyond what is traditionally engineering work. Things like thermodynamics and fluid dynamics and those baseline things that were always, (and I'm speaking from the perspective of mechanical engineer), for electrical structural as well. There are these fundamental things that we always thought were part of engineering, but around understanding data. So, let's say, data science, even software programming, what software programming is capable of in the work that we do is becoming a much more important factor for engineers entering the industry.
Kirsten:
Let me just pick up on something you were saying about people coming into the industry, because students who are graduating and beginning their consulting engineering careers are coming with an entirely different educational background than people who came here 10-15 years ago. Are you seeing the new grad hires coming in armed with this kind of understanding? Or do we have to take even a further step back in the industry to make sure that not only the students, but clients are also able to understand these concepts and what these technologies can do for them?
Akira:
We are seeing a lot of graduate programs that engineers are taking after they've finished their undergrad degree in engineering in things like machine learning, AI data science. So we are seeing those skills coming through. We are also seeing a lot of folks who have a side interest, let's say software programming or software development, that we see through their resumes in terms of some of the clubs, or things that they've participated in or side gigs that they might have.
So those are the folks that we're really interested in finding, because that way of thinking is really important for how we could adopt the technology, right? It's not necessarily that we need somebody that can do the software programming of a specific tool for our design software or write a macro for Excel or really use Excel in a sophisticated way. It's just knowing that it's a possibility, and what type of work can be made more efficient with these types of tools.
Kirsten:
Which of these technologies that you've been talking about are you seeing making significant differences in how design work is delivered and in the range of solutions we're able to offer to clients?
Akira:
The main one I think is, and this kind of dominates our lives from a design perspective, the conversion of our work from going from a 2D space to a 3D space. And I would say that we haven't quite figured out how to leverage that technology yet. There are a lot of reasons why that is. Particularly, I think that our processes as designers are still stuck in delivering in a 2D environment, in not a data rich environment.
If you think about AutoCAD and the output into 2D drawings, now we're working in a 3D model-based environment that can contain a lot of static asset data. So, for example, engineering data about the pump or the air handling unit or the switch gear that's within the design. So we're constrained by how we're supposed to convey our designs.
That is a technology that we need to leverage better. By having good practices within that baseline environment, we open ourselves up to using some of the other technologies that might be available to us. An example for that would be if we had great modeling and design practices within our BIM models that contained good data about not just the assets, but the infrastructure that we've designed. So specifically, if I look at a duct system, everything in the model is connected and we have the right pressure drops and the right flow rates and, the system understands itself in terms of how it's designed.
Over time, if we have a lot of models that look like this, then we can start extracting data from all these models and we can start developing metrics in terms of how we've done previous designs.
So in a lot of cases, as much as we try to work from a previous design, we're using rules of funds or, or things to do the initial designs; whereas, if we have good data on, let's say, a healthcare project or a series of healthcare projects that we've done in the past, then we can, optimize that initial design phase and come closer to what will end up as the final design. So that's certainly a way that we can do that. Another way that we're potentially looking at leveraging something like machine learning, in that same sense is that if we have good data coming from the architectural models, then we are able to model the association between the space types in a clinical setting and the CSA requirements for those spaces.
And so, these are some of the things that we can do, but we have to set a fundamental baseline of good modeling practice and good design practice to make sure that we're getting good data. A lot of the challenges with leveraging data is having good data sets, and we spend a lot of time trying to find those good data sets.
Kirsten:
Something that comes to mind is integration and, with the preponderance of new systems available and, as you mentioned, people maybe coming to clients with the latest and greatest shiny new object, how important is it to have a good IMIT strategy in order to ensure interoperability?
Akira:That is probably the most important thing, right? I was chatting recently with the general manager of a new tower downtown, and one of the issues they're having is with integration, because the industry, I think, hasn't necessarily caught up in terms of standardization for integration, right?
Common languages and protocols to communicate and common practices in terms of how to manage changes are key to integration; so, application programming interfaces, how two different systems speak together or the ability of the IT network to handle all of the data. A different client that we were talking with, had an integration issue with their elevator control system with another data acquisition system in their building.
And the elevator company changed their name; it was Thyssen Krupp Elevators, but then they changed it to TKE. So, somebody renamed a folder in their system where data was being dumped from the elevator system from Thyssen Krupp elevators to TKE, and this broke the whole integration, right? That simple thing took hours to figure out! And then you also have companies that are, let's say, well-established in the industry, legacy type organizations, like the Siemens and Honeywell and whatnot.
But you know, there's a whole bunch of them out there. And there are some of those organizations that are somewhat new to having their data accessible on the cloud, writing application programming interfaces, so that people can interact with their cloud environment. And so you'll find that some of these organizations or even tech startups that are bootstrapping and have very few employees, you find a common problem where they'll make a change that's undocumented and all of a sudden, the integration breaks and everybody's trying to figure out why.
So this is all to say that your IMIT (Information Management Information Technology) strategy is vital, not just internally, but your practices, how you're managing data integrations, but also the vendors that you're choosing to work with and making sure that there are requirements for them in terms of how they update their technologies so that they all work together is really the key to how all of this works. The fact that all of our data was siloed in the past for building systems like BAS (Building Automation System) and lighting control systems, and your fire alarm systems - fire alarms are totally different because we can't really touch a fire alarm system because of regulatory requirements - but all of these systems just kind of worked in isolation and that was great, right? They worked most of the time.
But the integrations and the reliance on so many different verticals makes things that much more complicated, and that is still something that folks are figuring out. For sure.
Kirsten:
Akira, can you quantify how these new technologies you're talking about are providing clients with a return on their investments when it comes to creating more efficient and cost-effective facility management tools?
Akira:
That's a great question, and one of the things I want to focus on is the return on investment, because that is the most challenging part about bringing a new technology to a client. I think a lot of folks are guilty of (and I'm guilty of this too!), is the ‘shiny new thing’ syndrome. It's like,” oh, this is really cool. How can we use it?” as opposed to saying, “how can this actually drive value for the business?” You have to always think of the problem first and then what technology can solve.
One of the things that we have found really interesting at the baseline, and it forms the basis of this topic that we talk a lot about now, digital twinning, is 3D imaging - the availability, of really high fidelity and easy-to-use 3D imaging technology like the Matterport scanner, have been able to virtualize a lot of the built environment.
And this has opened up a ton of new possibilities in terms of having people do virtual visits during COVID for, let's say, contractor walkthroughs or even giving a building owner a different kind of as-built. We could do a scan prior to the walls and the ceilings being boarded, and that gives him a high-definition picture of what's actually built on site, which is often very different than the red line markup as-builts that you'll get at the end of a project. Some of the other technologies that we're seeing, like, CFD or computational fluid dynamics, are really cool because we're able to do much more efficient data center design and able to leverage CFD tools on the cloud, and deliver even more computing power than we've ever had.
This really drives value for the design, but also the end user of that data center has the most efficient and safe, or redundant system in place, based on having the right HVAC equipment in the right place and providing the cooling that the systems need. So I think those are some things that we're really interested in leveraging now. I mentioned digital twins and IOT. These I think, are a bit more challenging to derive value.
And going back to the beginning of my answer, there are so many different technologies available out there and the owners of buildings and even hospitals and post-secondary campuses or whoever, they have a hundred people knocking down their door trying to sell them something new.
That does something very cool, and again, this comes back to that shiny new thing syndrome, does it actually drive value for the client? You look at something like a healthcare facility or a post-secondary campus. These places own all of their assets and often have a lot of different buildings that have a lot of folks interacting throughout the day. So there's a lot of potential to apply, let's say, presence technology and more sophisticated HVAC and line control technology to provide a more efficient system, or a better experience for the students.
But you look at, uh, something like a commercial property where you might have a lot of different tenants and you don't own all of your assets, and there's a lot of interaction between, the tenants’ requirements, the building’s requirements; it's often a lot harder to generate an ROI and the application of these new smart buildings technologies.
The question is, how can the building drive value for tenants while also still driving value for owners by, let's say, increasing rents or making their profit margin larger, or increasing their brand. So, these are a lot of challenges that the industry is facing. I would say that for some of the most emerging technologies out there, it's still a challenge to find value. And I think, in a specific example for us for instance, we're really interested in machine learning because we feel machine learning can help us in our work.
But the challenge is what question can a machine learning model answer for us? And we spend lot of time s trying to figure out how we could potentially do that.
Something that we're about to pilot on a project is, -and this is very specific for engineers out there - is how to make more efficient the associating of a clinical space type with its CSA defined requirements. Every clinical space in Canada is governed by CSA 317.2, and that defines, what the temperature should be, what the air change rate should be. But if you think about how an architect will design, they'll call something a soil utility room or they might use an abbreviation for the word ‘soiled’, or there are a variety of ways that room might be named; yet it still has that single association that it has to make with the CSA requirements.
So, we are looking at using our previous designs to build a machine learning model that that we can train, that will make those associations for us. Because we feel like that is a great way to save time. But again, we had to really think about that specific question that could be answered. And I think that often the challenge in driving a return on investment in something like this, for ourselves and for our clients, is asking the right questions.
Kirsten:
I just want to pick up on something you were mentioning just now and that is other players coming into the design landscape and kind of, cutting our grass, if you will, they're not only engineers. Where do you see this going in the future, in terms of the kinds of skillsets they bring and how consulting engineers need to, maybe change what they're doing in order to compete?
Akira:
That is something I definitely think about a lot because of the fact that where we're pushing into when it comes to, hardware technology, data analytics, and data science and whatnot, there are a lot of industries out there that have a lot more skills in that area, right? So, you're looking at professional services firms or even tech startups or big tech. You look at AWS and Azure, they have their own digital twin platforms. A lot of the other digital twin platforms out there are startup technologies, like Willow or ThoughtWire; there's a whole whack of them out there.
And even professional services firms are providing services in terms of designing smart buildings, at least in terms of the requirements. Also professional services firms that have data scientists that are looking at doing data analysis for manufacturing or consumer product goods or whatnot.
So, the emergence of using data and big data in our world has opened up our industry to these other industries that have traditionally been very focused. And that's something that we have to really take to heart because it's a bit of a threat to our work, at least in some of these emerging technologies, where we have an opportunity to play in this sandbox as well, so to speak.
Our advantage is that we understand how buildings work and our clients trust us to help them make good decisions on their behalf; that's why they come to us. So the opportunity for us is to see how we can fit into this new space, and bring these new technologies to our clients. And it's not to say that these other players won't also exist in this new ecosystem, but we can at least invest to help carve out our own space.
Kirsten:
Where do you see consulting engineering heading; what kind of skills are engineers going to have to have going forward in order to be competitive?
Akira:
I think this is largely driven by the technologies that we're going to be required to use as engineers. I truly feel that data science and software development or software programming skills will be a really key factor for the engineer of the future because we're not talking about having engineers programming large pieces of software, right? We're talking about engineers being able to understand what programming and coding is capable of, in terms of aiding the type of work that we're doing.
I think some of the examples, in terms of what I've talked about previously, are interacting with the API of a system and extracting data and putting that somewhere, like in a cloud environment. These are things that I think a lot of engineers can learn with a relatively little amount of work. I mean, relative to the engineering degree that they just did! Applying some of these technologies is not as complicated. It's just a matter of getting the practice and doing. There are a lot of other tools that we're exploring, things like parametric simulation; that's using different kinds of open-source algorithms to, do simulations by adjusting and optimizing a series of parameters, let's say, looking at optimizing the energy use intensity of a space by changing the window-to-wall ratio and the size of the shading. So, you're able to use different tools like Grasshopper and Rhino and open-source parametric modeling tools to optimize that space in a way that hasn't been done before to run a thousand simulations and get the 10 top 10 best options for you.
So that will require having some knowledge in programming and visual based programming. I think things like machine learning will require a lot of expertise in data science. And that's not to say that every engineer is going to start doing machine learning modeling, but they can at least be aware of what a machine learning model is capable of telling people. And that's the key - understanding how to apply these technologies and asking the right questions. A lot of the stuff that we're going to be relying on in the future, data acquisition from IoT, bringing that data into the cloud environment, will require folks to have skills in both.
Kirsten:
Our guest today has been Akira Jones, director of Digital Services at HH Angus. If you'd like to contact Akira, his contact information is on the podcast page. Thanks, Akira, for your insights on the future of consulting engineers. And thank you to our listeners for joining us today. Please stay tuned for future podcasts on expanding the possible coming soon.
The pace and the degree of change taking place in the engineering profession today is nothing short of staggering, compared to how it was evolving even just a decade ago. Join Akira Jones as he discusses where consulting engineering is heading. Akira is the Director of Digital Services and a Principal at HH Angus, co-chairs its Smart Buildings Committee and is a LEED-certified mechanical engineer by training.