Podcast: Saving America's Collapsing Infrastructure

In this episode, we explore how AI technology is revolutionizing the detection and monitoring of infrastructure defects and learn how these innovative solutions are enhancing safety, improving maintenance, and preventing failures in critical infrastructure systems

Episode Notes

(0:50) - Infrastructure Defect Detecting AI

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Transcript

What's up, folks? As you know, cracks in buildings and roads and bridges can cause some serious damage if they're not found early enough. Today, we're talking about a team from EPFL that's gonna fix this problem using AI that can help detect cracks automatically and let us know when we need to do repairs. I think it's interesting, so let's jump into it.

I'm Daniel, and I'm Farbod. And this is the NextByte Podcast. Every week, we explore interesting and impactful tech and engineering content from Wevolver.com and deliver it to you in bite sized episodes that are easy to understand, regardless of your background. 

Daniel: What's up friends? Like we said, today we're talking about the future of crack detection in infrastructure and why that's so important. We're not just talking about like cracks and buildings and roads and, sometimes it makes them look ugly or ivy can grow in them. But the problem is that they can cause serious damage if not found early enough. The traditional methods we have for inspecting and looking for these cracks are really slow, really costly, really hard to do in dangerous areas. And there's a lot of human error involved with it as well. So, we're talking about some awesome research here where they're using AI to try and help find and monitor cracks, but they're even taking kind of a unique AI approach as well that doesn't require detailed pixel by pixel labeling either. So, I'm pretty stoked about this one.

Farbod: Dude, same. I feel like infrastructure improvement has been talked about for so long. I remember being in middle school and politicians were promising the next infrastructure build and how like, our infrastructure is aging and we need to replace it. Well, before this part, I was just kind of curious. I'm like, let's pull up some stats just about the US alone, right? I don't know if you knew this, but a freeway bridge inspection can take, can cost about $5,000 and take about eight hours to do. And that doesn't take into account the detour traffic causing people pains to get to work, et cetera, et cetera. And on top of that, the way we do grading of these cracks tends to be kind of subjective. So, the person that's doing it might grade it more, I don't know, liberally than the person that did last year. So that person might've thought things were going bad. This person says everything's good. So that kind of creates a weird situation where we can't track how bad it's getting super well.

Daniel: Or can you trust the data that comes from these expectations? When you spend this much money and you spend this much time, you may not be even able to trust the data that comes out of it.

Farbod: And to just make matters worse, there's something like one out of every three bridges in the US needs repair or replacement. So, one, you don't know if things are actually bad. Two, it seems like at least one third are kind of bad, maybe more. So not super comfortable about driving over a bridge at this point, but I saw something interesting about drones. Drones have had great improvement over the past couple of years. People were saying on average it's about 40% cheaper. So that makes it more affordable for local governments to take them on. But still, I'm guessing no one is super excited about shelling out over $3,000 to inspect every bridge they got. So yeah, sticky situation to be in.

Daniel: Well, and just a quick throwback here. I would say like in the broader realm of infrastructure detection, both of our capstone projects, when we were graduating our undergrad program was related to using robotics and automation to try and detect issues with infrastructure before they progress to something that becomes a catastrophic failure. And that's not to say that we're experts in this topic at all. I think we were just at the tip of the iceberg here, but it is interesting to note that in separate graduating classes for about, and I both had this capstone project because it's such a big problem and we're still sitting here saying, maybe now we finally got some AI solution, which can help with this in a more robust manner, which is exciting for both of us, I think on a personal note, to see this field start to progress.

Farbod: Dude, absolutely. Back when we were young men, which we're not anymore, at least my team, we were toying around with the idea of using AI, but even then, there wasn't a whole lot of excitement around it. Specifically, we were looking at corrosion, but now it looks like the game has completely shifted. We're going to EPFL for this episode, but if we can transition to the secret sauce, these folks are talking about how AI can actually completely remove the human element here and in the best way possible. We've been talking about subjectivity, how that can result in variance from person to person, and if a crack is getting better or worse. These folks are saying, hey, we can just train a very simple model that can tell us if there's a crack or if there's no crack, like binary. Once it learns how to detect the crack well, then it's just a matter of saying “cool, where are you seeing the crack and how is it changing over time?” Which in the world of like, you know, computer vision is a fairly simple and straightforward analysis. And the main application they're looking at for this is railroads, right? They're saying, railroads are used very often if there's cracks that grow that can cause catastrophic failures. So how do we maintain this and like monitor it on a regular basis? Well, they're like trains are passing through this bridges and stuff. What if they were the ones that were doing the analysis completely autonomously, no added costs, no added time, no requirement for shut down.

Daniel: It's not sending people on harnesses to rappel from these. Honestly, you should just check on the link in the show notes, click for no other reason than to see the beautiful photo of this train on a railroad bridge going across a canyon in Switzerland where, and I wanted to confirm this, I wasn't 100% sure. The Matterhorn-Gothard bond network. I'm pretty sure Nellie and I took this train on our honeymoon, which is pretty cool.

Farbod: Wow. That's special.

Daniel: If not something very similar because all of Switzerland is full of beautiful trains and bridges and mountains, but I'm pretty sure we went on this exact railroad section where they're starting testing, but yeah, if for no other reason, check out this photo, but for me to get back to my main point, this is a bridge suspended several stories above the bottom of a canyon. It would cost a lot and you'd have to pay someone a lot to be able to visually inspect all of that manually, like as a person by hand. And what they're trying to do now is instead send a drone, but not in the form of the quadcopter drone that you think of probably when we say drone. The solution is, send a drone rail car, right? An instrumented rail car down this railroad and try and collect a bunch of data on, where are their cracks? Are they propagating? Are they staying the same? Which ones need to be addressed? Continuing to understand and map the ever evolving, let's say ecosystem around all these bridges and this infrastructure and understand when there's issues and indicate whether they need further inspection or whether they need immediate repair, et cetera.

Farbod: Dude, totally agree. And the solution seems so simple when you say it out loud, but it really is, I don't know, I guess the best solutions are the simple ones. And what it made me think of is obviously this is great for the railroad system. But then we've been talking about like as a commuter that's going in a tunnel. That one tunnel in Boston I always hate it gives me the creeps when I'm going through it or when I'm going through bridges to Maryland and stuff like that. It would be great if there was something that could do the analysis there and in my mind, I'm like we're definitely a long way away from having autonomous vehicles doing these trips for us. But Uber and Lyft, they're drivers that are operating every single day that are doing these normal routes that people take to go to work and whatnot. I wonder if there was an opportunity there to say, hey, while you're doing this, mount this device on the back of your car so that whenever your GPS detects you're going from the beginning of the tunnel to the back of the tunnel, you do a scan and then that data uploads to a cloud somewhere and we can do analysis over time to see if we need to patch any cracks or what?

Daniel: I'm like wondering if you read my mind here. I went to buses, because I'm like, the department of transportation in a city may also own the buses, right? So, they're in charge of the bridges. They're also in charge of the buses. Wonder if they can just put a bunch of instruments on top of a bus. In my mind, it's like the closest analogy to a rail car. Put a bunch of this infrastructure on top of a bus and this instrumentation, see if you can analyze the infrastructure around us using that same method as well. But I also want to jump a little bit into the specific AI methods they're using. And I'm not an expert here by any means, but I took some notes from the paper that I think are interesting. That there have previously tried to do AI classification of images, but it's too computationally intensive to justify the cost. So, if you were to use AI to try and evaluate millions of photos of an entire bridge. It may actually cost more than actually sending a human down there to do it. Because one of the things that I had to do is classify each pixel by pixel, trying to understand whether it was light or if it's dark and look at contrast to understand what is a crack, then it classifies a crack and that took a lot of computational energy. One of the things that they did is they, they train their AI ahead of time with images labeled as crack or no crack. And then it uses explainable AI is what they say is their unique method here to try and look at a new photo and circle, basically highlight a cracked area and create a map showing where cracks are and where they aren't. And essentially what it's doing is it's just taking photo after photo after photo and trying to trace the area around where it thinks the crack is. And over a period of time, it's trying to look at the same photo from the same location the last time this train came through and look at whether this crack got bigger or if it stayed the same. And if cracks are serially getting bigger and bigger and bigger from these images, it warrants sending someone down there, an expert down there to take a look at it. And one of the things that they mentioned is this cut the processing and labeling effort, as opposed to previous AI methods by about 90%, which makes it cheaper and faster and worthwhile to implement in an autonomous manner. It's not quite as accurate as these full AI models, but they said it can still help directionally track crack growth. And they think that they can hone it essentially and get rid of more of the area that they're experiencing right now. But essentially the game plan I guess is to go test this out in the field, get more learnings, understand more but in practice even if it has the same performance that it has today you can use this as kind of like a non-invasive constant monitoring where it looks at the rails, it looks at the ties, it looks at the retaining walls and tries to understand if there's anything wrong with the railroad. And you could have this on every train or one of every 10 trains passing through this railroad. Even though I think right now it's got 35% error, which again, it sounds high compared to most AI models, but if you're telling me, you can tell me 65% of the time, whether a crack's growing or not. If I've got two or three trains that pass through over a series of a week and show that this crack indicates that this crack may be growing, the playbook here is not to say, all right, let's shut down the bridge and tear it down. The playbook here could be, let's go send one of those expert inspectors that we used to have doing this job full time and had to shut down the railroad to do now that we know that there's something that's worth further evaluation, let's send one of those crack experts down there to understand what's going on and see whether it needs repair.

Farbod: And this is a great example, at least in my opinion of complimentary automation, right? Like here's a task that was bad for people to do manually. And some places are like in America, it's just straight up not done at times. Now you have automation that's taking care of the tedious part. And then you have the human that's taking on the challenging part, which is, hey, we're like 90% sure there is a crack, please go out there and now characterize it and give us feedback on if it's catastrophic or if, you know, it's a simple crack that needs to be fixed, etc, etc. So, I feel like this is the best use of every technology or every resource, think of the human expert as a resource. And this is kind of going against the AI is going to get rid of all jobs mentality.

Daniel: Well, and I think what we, the intended end here is to be able to do continuous monitoring, collect data all the time to understand which regions are susceptible to cracks, and then use that as a heat map essentially to show someone who's again, got expertise in this field to go, this is the area that you need to inspect. This is the area that you need to monitor. This is the area that may need potential repair as opposed to the other portions of track that maybe didn't need as much attention and they were getting it before because you had to go check every single mile of this thing manually. And maybe you used to only check one, every single mile of this thing manually, one out of every five years. And now we could have a train passing on it five times a day that tells us how the railroad's doing. So, I appreciate here, like you said, it's a complimentary solution between automation and humans. But it also gets us an immediate benefit, which is a lot more data, a lot more consistently on how our infrastructure is doing and when it needs to be repaired.

Farbod: Totally agree, man. You want to do us a favor and wrap up the episode?

Daniel: I will. What if we could stop big cracks in buildings and roads before they cause big problems, I'm thinking of bridges collapsing or bridges needing to be shut down because they need repair or rip, cars getting derailed. These are big problems and we're talking today about a new AI method that helps find and track these cracks automatically without needing a ton of expensive detailed data. Essentially, they're getting the job done with 90% less effort. That's what their new tech promises. And they use this system called explainable AI that spots cracks with simple image labels, saving time and money, keeping an eye on crack growth to let experts know where they need to address cracks and where we might need repairs in the future. I think it's a lot like, I don't know, Google Maps navigation or some sort of traffic heat map, but it tells us where most areas are prone to cracks and where we need repairs so that folks know where they need to focus their efforts.

Farbod: Money.

Daniel: Thanks, my dude.

Farbod: You killed it, dude. All right, folks, thank you so much for listening. And as always, we'll catch you in the next one.

Daniel: Peace.

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The Next Byte: We're two engineers on a mission to simplify complex science & technology, making it easy to understand. In each episode of our show, we dive into world-changing tech (such as AI, robotics, 3D printing, IoT, & much more), all while keeping it entertaining & engaging along the way.