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How AI/ML in Info360 Asset Can Assist in Sewer Pipeline Condition Assessment and Management

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Description

Sewer pipelines are buried and out of sight. They are underinvested in and rapidly deteriorating. There are millions of miles of sewer pipelines that require monitoring to determine the condition and performance of this critical infrastructure. The principal method that utilities use involves closed-circuit television (CCTV) mounted video cameras on crawler units. Video and photographs of the insides of pipes are interpreted by engineers trained in the art of sewer pipeline defect recognition. This time-consuming and expensive process usually carried out in the field is increasingly being challenged by the use of AI and machine learning (ML) image detection and recognition techniques. See how the integration of Autodesk's Info360 Asset SaaS asset management product and VAPAR AI/ML tools will transform the assessment of sewer pipeline conditions, leading to huge cost and efficiency savings and productivity gains. Learn how these inspections are used to identify failure modes and determine the likelihood of failure and remaining life of assets.

Key Learnings

  • Learn about how the water industry is solving problems related to aging and deteriorating sewer infrastructure assets.
  • Learn how and why Autodesk desktop asset-management products are migrating to the cloud.
  • See how AI/ML is used for the purposes of condition-based, tactical, sewer-pipeline capital improvement planning in Info360.

Speakers

  • Avatar for Tim Medearis
    Tim Medearis
    I'm passionate about water, fixing aging infrastructure, and bringing solutions to challenged utilities. I love being a solutions engineer at Autodesk because I get to work on a variety of challenges and love seeing our clients succeed. In my free time, I like to run, play basketball, read novels, enjoy the Colorado outdoors, and spend time with my wife and kids.
  • Michelle aguilar
    Michelle is a co-founder of VAPAR and has been the technical lead for the company since its inception. Michelle manages a team of software developers and data scientists, making customer focused decisions for the technical roadmap of VAPAR's products. Her career background spans multiple industries including wastewater, process automation, and business automation for Australia's largest bank.
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Transcript

TIM MEDEARIS: Hello, everyone, and welcome to this AU 2024 presentation on artificial intelligence and machine learning in Info360 Asset, and how this can help assist you in your sewer pipeline condition assessment. My name is Tim Medearis, I'm a Solutions Engineer at Autodesk, and I'm happy to be joined by Michelle Aguilar, CTO of VAPAR.

All right. So before we get started today, does anyone remember-- or did anyone watch this particular TV series? Notice how it's dating itself down at the bottom with that "2 DVD Set." This was an American reality TV series that followed Mike Rowe as he covered the jobs that, simply put, men and women just have to do, those dirty jobs.

And I can't help but think of it when I think about today's topic. Today, we're going to be talking about sanitary sewer networks and the job that operators have to do in order to inspect those. Guess how many episodes Mike Rowe had to get into this series in order to cover sanitary sewer inspections? It was episode 2 in season 1.

No doubt, sewer inspection is a dirty job, but today, we're going to talk about ways to make it a little bit cleaner, at least a little bit faster for those operators out in the field.

All right. So today's agenda. First, we're going to look at a little bit more on these dirty jobs for our sanitary sewer pipelines, why they're important, and what is the status of our sanitary sewer networks, particularly in North America. We're going to look at the standardization within the filming of those sewers and the inspections of those sewers.

Then I'm going to hand it over to Michele to show how VAPAR Solutions is help making this dirty job a little bit faster and a little bit easier to manage. Then we're going to get into the demo part of the presentation today, and Michelle will demonstrate VAPAR Solutions' interface, and I'll take you on a tour of Info360 Asset and our solution from Autodesk and how those solutions work together. And then last, are happy to answer any questions.

All right, so backing up a bit. What is the state and why is this important to our sewer pipelines today? Well, today in just in the US, there are over 800,000 miles of sewer main. That's three times the distance from here to the moon. That is a lot of aging infrastructure that we are trying to manage. Millions and millions, if not billions, of dollars of aging infrastructure.

And it's not getting any younger. The average age for sewer and water pipes here in the US is 45 years. I've personally seen pipes that are over hundreds of years old that, again, some of our cities are having to deal with.

And, to top this off, there's only-- well, for all of these pipelines, there is actually a 4% increase in operation maintenance. Those activities that are helping to preserve the lifespan of these pipes annually. So annually, the cost to take care of these pipes is going up. So the bottom line is, this critical infrastructure is aging, and there's no way to wholly replace it. There's just too much of it, so it's very important for us to maintain it well.

The American Society of Civil Engineers has rated specifically the sewer infrastructure here in the US as a D-plus. And it's improving slightly due to that increase in funding, but it's critical that we maintain it to keep our sewers running and our cities actually functioning.

All right, so what is involved in some of this O&M operation and maintenance activities within our sewer system? So on average, about 4% of an average sanitary sewer network is inspected annually, and then only a portion of that is then repaired or replaced. And again, very small fraction of a percent here, so it's important we inspect as much of it as possible so that we can replace the most critical pieces as soon as possible and not waste time replacing pipes that still have life left within them.

But even as we're doing this, we're projected to have an $18 billion gap by the year 2039. Again, that stat comes from the American Society of Civil Engineers. These inspections are done sometimes by the utility, but sometimes by a third-party contractor. Regardless, though, different technologies are being used to inspect these, again, miles and miles of pipeline.

So there's some that are cutting-edge. Focused electrode leak location is an advanced technology tried to find little pinholes within our pipes. Smoke testing is a common one. We actually pump smoke into these sewer mains and see where that smoke is coming out to see where you might have holes.

You can do visual inspection. That was actually what was featured in one of those episodes of Dirty Jobs, and you saw Mike and some of these inspectors actually getting into the pipe with the cockroaches and the rats and seeing where there might be deformations or where these pipes plants might be in need of repairs. But most commonly, the most common technology used to inspect these sanitary sewer pipelines are CCTV video inspections.

And what are CCTV video inspections? As our product manager sometimes likes to sarcastically say, it's kind of like a poo tube. This is a camera that operators are actually putting into the pipe, it's connected to a long line, it's on wheels, and they're actually driving these down from one manhole to another.

It's a very standardized way of doing these inspections. It's very rarely ever done on pressurized pipe. The pipe would have to be completely drained in order to do it on a pressurized pipe. So most commonly, it's always sanitary sewer pipeline. Maybe stormwater networks are a little cleaner, but for the most part, this is done on sanitary sewer and combined sewer pipelines.

NASSCO is the main governing agency within North America for standardizing how these inspections are done to make sure they're all done, first, I guess, safely, but secondly and still importantly, in compliance so that they can be compared apples to apples. These are always done manhole to manhole. And generally, these can run as far as 1,500 feet, but a lot of times, they're much shorter than that because we're just going from one manhole to another. All right.

And so as a camera is running along the sewer pipeline, it's the operator in that cab, in that truck, is recording defects and stopping to record defects on that pipeline. So each type of defect has a different code on these. So can you guess some of these particular codes? There's a lot of them. There's a lot of different codes. I know that one on the left there, H stands for hole. XP, that's a full collapse there, that's a major defect. IG is Infiltration Gusher.

DAGS, I believe, is deposits of grease on that pipeline. You can find all kinds of things within the sewer pipeline. So everything has a particular code to it so that operators can understand the true nature of that pipeline and prioritize it effectively for whether it needs to be replaced, repaired, et cetera.

All right, so again, there are these codes, but each of these codes can then be associated to a particular grade. So in NASSCO, in the NASSCO standard, the highest grade is a 5, lowest grade is a 0-- or I guess a 1, maybe. And these codes can be combined in different ways to, again, make sense of that pipeline and compare it to others that you might want to fix or rehab.

So this is a very commonly-used code here. This is called a quick score where you take the highest grade on that pipe-- in this case, I've got a 5. And I've got three of them, so I've got 53. And then the second highest grade is a 3, and I've got one of those. And that gives you your quick score. You can use that to compare pipe A to pipe B, et cetera, and you can have different variations of this quick score.

And that's just one of the different ways that you can look at all these defects on that pipe. Ultimately, a lot of times, these codes, these reports, these inspections are put into PDFs. Ideally, we'd love to see it when they're linked to the videos and the actual media from that inspection. And in best cases, it's best to see these linked to maybe that utilities GIS data, their geographic information systems where they can see and link that to different attributes and show it on the map like I was showing earlier.

However, that doesn't always happen. I've run into utilities that actually have CCTV data still on VHS tapes, even older than that DVD set from the beginning of the presentation. But again, these can be combined in various ways, and it's important in order to compare it and better allocate resources, since we can only really inspect 4% of a network every year.

Again, just a fraction of the data. There's a lot more that goes into an overall utilities asset management plan. So the more efficiently we can get this done, the better we can move to more holistic analysis of these types of inspections and better allocate millions, and really, billions of dollars of infrastructure resources. So with that, I'm going to turn it over to Michele who's going to talk a little bit on how AI can help speed up these inspections.

MICHELLE AGUILAR: Thanks, Tim. By way of quick introduction, I'm Michelle Aguilar, one of the co-founders of VAPAR and the CTO. As a mechatronic engineer, my professional background has been largely in the space of automation in various flavors. But it did also dip me into the world of sewage before we started VAPAR.

If you know anything about beaches in Australia, which is where I'm from, you've probably heard of Bondi Beach, but what you probably didn't know is that there's a wastewater treatment plant just around the corner of that beach servicing the entire Sydney city.

My first professional introduction to sewage systems was rewriting the automation that controls that wastewater treatment plant, which made it easier to do my transition into VAPAR and then the dirty jobs we're doing here.

So carrying on from where Tim left off, the level of detail inside the pipe inspection reports is definitely challenging for a human to do. So let's have a look at the use case for automation here.

Generally, automation works best in a high-volume consistent process. That's definitely the case for pipe inspection. While each utility may only be seeing 4% of their network, as Tim said, each year on average, that still adds up to over a billion meters a year globally. That's a lot of dirty videos being watched.

So the defect reports are being put together onsite, like Tim went through, and typically, miss or misrepresent a fair amount of defects within the pipe. This then puts the onus back onto the pipe owner to rewatch the videos manually to make sure they understand the condition of their networks and make the right decisions for their budgets.

We started VAPAR because my co-founder, Amanda, used to be one of the engineers sitting as a pipe owner watching all the videos. And she said, there's got to be a better way. So pipe owners are really trying to achieve the goal of fixing pipes before they fail. Act too early and you're likely overspending. Act too late and you'll spend almost four times more on reactive repairs than if you've managed the condition proactively.

So to prevent this, we need to help pipe owners to understand what's gone wrong, what do I need to do about it, when do I need to do it, and how much is it going to cost? The budget pools and the engineering resources that pipe owners can leverage are centered around the inspections and the repair works.

So inspections make up the smallest part of the budget, but require a large amount of an engineer's time, as Tim's been through. The quality and efficiency of the planned repairs programs relies heavily on the quality and coverage of these inspections. Pipe owners can end up spending more money than they need or neglecting more impactful repairs if they have bad data.

And unplanned repair works or reactive programs, as they're more widely known, occur after an incidents already happened and something has to be done immediately. That's because there's sewage flowing onto a road or filling up someone's basement. It can be hard to get ahead of these issues with the current labor-intensive process, and so that's where we come in.

VAPAR's product vision is to be the fastest way to the right asset investment decision. VAPAR has developed artificial intelligence models that watch pipe inspection videos for you. So instead of watching a whole pipe inspection video, you can just review the coded defect highlights in a significantly shorter time.

And then based on the detected defects, VAPAR's system also generates automatic repair suggestions that are defect-specific and configurable to meet the pipe owner's needs. But let's talk more about what we're all here for, which is the AI.

For a sense of scale that this tech is at, we've worked with Sydney City Council, one of Australia's largest municipal councils. So this council serves over 130,000 local residents and manages over 250 kilometers of stormwater infrastructure. The number of features digitized that we were able to process for them within a six-month period was over 6,000. We were able to save them over 400 hours of manual review. So they went from assessing 5 kilometers of stormwater network per year to 15 kilometers in six months.

So the strain of AI that's used for defect detection in pipe inspections is image recognition. Computers read images as pixels on the screen. AI can be used to learn what clusters of pixels represent. Image recognition models are trained by showing different variants of the same thing until the model understands what the patterns in the image mean.

You can imagine this as trying to teach a five-year-old how to identify issues in a pipe. You would show them lots of images of cracks and lots of images of roots and so on until they were able to identify cracks in roots in new images, and that's the baseline technology that VAPAR's built upon.

So VAPAR has been in operation for over six years and processed over 25 million images of pipe inspections in this time. World-wide usage of VAPAR's as technology has refined the outputs to closely mirror the expectations that pipe owners are looking for as a basis for decision-making on their pipe infrastructure.

So basically what happens when you come into VAPAR, you need to have just an inspection video. The video is split into a series of still frames which the AI can then process. The AI analyzes each of the frames, and based on all of its training data, over 100,000 images of training data, it checks each frame for the presence of a pipe defect, classifies what that defect is, quantifies the defect and the severity of that defect, and then categorizes the results to align with the regional scoring standards.

Unfortunately, this AI is not very sexy. We don't display bounding boxes like you can see here to the user, otherwise, they'd be having to watch the video again. Everything happens behind the scenes, and the raw results of our AI are already at an acceptable first-pass review level. So our users interact directly with those results to finalize their reports.

So we process about 1 million images each month from video inspection uploads from Australia, New Zealand, the UK, and the USA. So this process that you're seeing on the screen is step 1 of processing to get the image recognition results. The next step is to consolidate the classified issues just down to what a user would want to see in a report.

There are several factors to take into consideration, such as, is this a defect that I've already seen before? Is there a better image for this issue? How sure am that this is what I said it is? So when the user gets a chance to interact with the raw results and make adjustments, we can then help to refine the answers to those questions and improve the AI.

So now, we're going to jump into the product demo. Basically, the steps we're going to go through is uploading an inspection video, seeing the instant processing, and the results that are delivered in minutes. We'll go through the review process, making repair decisions, and then outputting the final report. That's a typical user flow on the platform. And then we've partnered with Autodesk to make it even easier and more centralized to be able to access your data and then make those flow on decisions for your big picture.

All that you need to use VAPAR Solutions, like I mentioned, is an inspection video. So you can upload the inspection video to a job folder to help categorize the inspections, but the majority of the information that's present on the inspection video gets read and stored about either the pipe asset or the specific inspection.

So here, you can see, we're uploading an inspection video. And the stages of processing are going from Uploading into Waiting to Process where it's queued within the system to be analyzed. The next step is called Analyzing Video, and that's where the AI runs. So the AI is there splitting the video down, processing the images against its training set, and coming up with the new defects for this specific video.

Once the AI has completed, we're going to the next step, which is called processing, and that's where the raw AI results are moved into useful information. So that is the standardized information. It's the consolidated information about one defect gets reported once and not over multiple images. And then finally, it disappears from the list once it's complete, and it appears, then, in the bottom table, which is the recently processed table. Once it's gone from the processing list, it's available on the Inspections page.

The most recent inspections appear by default at the top of the Inspections table. This table can be sorted and filtered on Folders, Asset, or Inspection attributes. The initial view will show you all of the classified frames, which is extensive. The report frames, though, shows you just the snapshots of the video that the chosen for your review.

Here, you can see the simplicity of cleaning up these results to get into a final report position. So you can see we're reviewing each frame that the AI has analyzed, and we're making some changes, and those changes will then get Fed back into the system to improve the quality of the AI for future video processing.

So here, you can see, we're changing some of the codes. And there are sometimes we're hiding the frames If we disagree that there's any defect present there. And that also feeds back into our system and we improve the outputs going forwards as well from that.

Now once the review is complete, the inspection status can then be changed to Reviewed, and the initial repair suggestions are generated for review. Any defect changes that impact these can be considered by regenerating the suggestions as well. So here, you can see we've suggested lining of the whole pipe, and this pipe owner said, if there are three or more patches required, then we are going to just realign the pipe because it's the same money.

So here, we're going to see if we actually reduce that down to two patches required, then the repair recommendation regeneration says we just need patches at this 13 meters location-- or 13 feet location, and also this 42 feet location as well for this circumferential fracture. And patches can also cover more than one defect as well.

As you can see, it hasn't suggested two patches there for the two circumferential fractures reported. But if we change this defect to a service defect that requires cleaning instead of patching, and we regenerate the repair recommendations, we can see now, we've suggested a desilt clean. So basically, cleaning the pipe rather than patching the pipe at that location.

Once the review of these suggestions is considered, there's a place for the pipe owner to make their final decisions as well. They can enter lining. In this case, there's a big hole. There's a lot of cracks along the whole pipe, so we're making the decision that we want to line the whole pipe. There's also a location for likelihood of failure and consequence of failure, and that can then feed into an internal risk score for pipe owners, and also feed into the frequency of inspections for this pipe.

OK. So moving on to exports. Once all data is finalized in the system, reports can be exported. And so this is a common export format that pipe owners will receive. This report is often handed off to maintenance contractors to assist with the repair works. So the inspection defects and the inspection video is also available to be played directly from this PDF report. So if required, this inspection video is available outside of VAPAR and can be shared and reshared outside here as well.

There are a number of other export formats as well to assist with data ingestion. So there are multiple other systems that this information is useful for, but we can also provide the shareable PDF and video links so that they can-- any other system is able to access this information via link. We also provide standard validation checks to make sure that the exports are valid and all required information is being captured on the inspection report.

So now this finalized report is the data that can then be plugged into Info360 for GIS insights, hydraulic modeling, and the big-picture planning. So now I'll hand back over to Tim to show you how that integration works.

TIM MEDEARIS: Thanks, Michelle. Yeah, absolutely. That's CCTV inspection done with VAPAR's artificial intelligence machine learning. It's a huge input into the holistic rehab and risk planning that's done within the Info360 Asset Management Platform and Solution.

So to access this same type of information within Info360 Asset and utilize our partnership with VAPAR, log into Info360 Asset just as I have here using that same standard Autodesk account and login just as you would for any other Autodesk solution. Come down to Tasks. And within Tasks, PACP Inspections, that's the NASSCO format. Is what we want to be working with in bringing in those particular inspections that VAPAR has coated for us.

So you can see, you can put these in the same exact spot that you're maybe doing CCTV inspections manually, but the VAPAR way of doing it is so much faster. Normally I'd have to drop a database in here and then upload the media files. When I have this connection with VAPAR, I can skip right to the second step here, drag and drop just that simple MP4 onto the screen here, and VAPAR is going to code this and spit out the results within Info360 Asset here.

So once that's complete, I can then start to link that MP4 to a pipe within that's been uploaded to Info360 Asset, but again, you can dictate in here, I want to bring in this MP4 inspection, code it in VAPAR using PACP Code 7, that NASSCO Code 7, and do this along with all your other inspections. So you can see that inspection there at the top. It's coding there at the top. And it's in the same spot as the rest of my CCTV inspections there.

All right, once you give that a couple of minutes to code, if you come back to your PACP Inspections, you can see that inspection has now been coded. So once it's that blue color there on the left and inspection ID is clickable, that means that AI has processed that inspection. And now look at all those codes that have been created by VAPAR, by the AI machine learning process and that are now within your Info360 Asset database here.

So you can see all those PACP codes there, manholes to joint offsets, deposits, grease. We're looking at something that's been identified as attached deposits grease there, but the cool thing you can actually edit that defect, just as Michelle was showing, in VAPAR, you can actually do that from the Info360 Asset space as well.

So if I look at this and I say, ah, I don't think that's a grease, I think that's rags, and I can change my percentages, my clock position for exactly where do I see that defect on that pipe. So here, I'm saying it's between the 4 o'clock and 6 o'clock positions. I can make those changes within Info360 Asset. You can see those there at 29.5 feet. You see, I've made that change. And you can do that and review everything that was automatically done with that machine learning and AI within Info360 Asset.

The next thing you might want to do is it's great that I brought in an MP4 here, but that MP4 doesn't necessarily although know-- though it can, actually-- where that video is coming from. Unfortunately, I have a mismatching MP4 and GIS data, so I'm actually going to plug in exactly what is my upstream manhole and my downstream manhole here.

If these were matching, this was real data, these would actually automatically come in and match, VAPAR would pull that into Info360 Asset, I believe, and you would see that coded there. But since they're not matching, I'm dragging in what is the upstream manhole that this video corresponds to, what's the downstream manhole that this video was taking into account? And then it is going to map to an actual pipeline in my network.

I could also add the pipe ID here. It's actually in the Locations section of the properties here. So there it is, Pipe Segment Reference. I can edit, drop that in there. And then you can see in the notes, again, Info360 Asset is all about data integrity. What have you changed within this database to help refine it and have everyone access the database and be up to date with your asset management plan there?

So once I do that and I do a little refresh here, you're going to see that now, that inspection isn't just brought in and not intelligently linked to an asset. Now it is linked to an asset so that if I were to click on this and look at this within Info360 Asset, I can jump right to that actual asset-- not just the CCTV inspection, but the actual pipe asset that maybe I brought in from GIS data.

I can look at that on my map, look at it in relation to maybe the service lines that it's near. I could add notes to this particular pipeline. I can see it in relation to all that pipe GIS data, the risk analysis that's been done, the rehab work that's been suggested for it within Info360 Asset, and get a holistic picture for the status of that pipe according to that latest inspection there. So, again, that's the name of the game within Info360 Asset, is how can we bring all these different data sources together to answer that question of what comes next here?

So that inspection has been coded, tied to an asset here. And the last thing that Info360, as it can do with that inspection, is actually approve that inspection and make it ready so that we actually generate, again, those quick scores. You see the Ratings Index here that aren't populated yet. We're actually going to generate those codes when we make this status of this inspection ready, and then approve it.

So this is one of the things that there's still a lot of people want to validate and make sure the CCTV data is good because it's being used for million-dollar decisions, so Info360 Asset gives you this robust QA/QC process where you're not just relying on someone that's getting tired of watching hours of video, you're not just relying on the machine, but you're relying on both of those together in order to make a robust asset management plan and have approved data to make those inspections.

All right, so zooming out a little bit, that is what Info360 Asset does with its integration with VAPAR, but in terms of what Info360 Asset does as a whole, we need to actually go back two years ago to when the Autodesk CEO was on stage and talking about the future of Autodesk.

Autodesk has all these different solutions, he said, all these different point solutions, but it's not going to work to solve the larger problems of the 21st century until these really come together and use the power of the cloud. And that's what we've done with Info360 Asset and followed along in this vision. Announced these AEC and media and entertainment and manufacturing different platforms. And this is continued to be the vision of Autodesk.

This was from this year, and he'll probably talk about it at this conference, if he hasn't already, about how these platforms are bringing together these hundreds of different Autodesk point solutions into these digital twins. So as much as these continue to develop and we're proud of how they've continued to develop, what I find hilarious is that our CEO's wardrobe has not developed at all in the last two years. I wonder if he's going to be wearing the same thing this year, that maroon shirt, jeans, and those Autodesk Vans, we will see.

But anyways, this is exactly what we've done here at Autodesk. I used to use this graphic for a very similar tool, that Innovyze was acquired by Autodesk. Used to sell-- that did a very similar asset management task. But it was a desktop tool, and you can see with VAPAR and Info360 Asset, we're able to integrate and do more in the cloud than we could have ever done on a desktop-based tool.

But at the same time, the mission of what we're trying to accomplish and what the area we're trying to help when it comes to, again, these large challenges that sewer utilities have in terms of asset management, is how can we bring together all these different data sources into one spot to make a more informed operation and maintenance or CIP decision?

So we've talked a lot about pipe inspections and CCTV, but really briefly, the rest of the presentation, we're going to talk about how Info360 Asset brings all these other data sources together as well into one cloud space that's very accessible and transparent, again, to help you better make what really are, again, million-dollar and billion-dollar decisions which are important to one of the most precious resources we have, which is water.

So real briefly, 10-minute overview of everything else within Info360 Asset. So this is the Info360 Asset interface. We spent a lot of time on that Tasks section, and that's really where the CCTV data and the VAPAR integration really comes in, but there's a lot of other parts to Info360 Asset as well.

You can see here on the map, not only do we support sanitary networks, but also water distribution networks. You can view all this in a web display, on your map, look at background GIS data, turn that off or on, change your different base maps as you need to.

But the real point of Info360 Asset is to have a spot where all of this information comes together. And then there, you see-- it can go back and forth. It's not just a sanitary sewer tool, but it's for water distribution as well. And can be stored at an asset level and-- our product manager likes to call it the asset bibliography. This is all the information you need in order to make that more informed decision.

So at the top here, you see some basic property information that largely comes from GIS. As we work down, we've got ways to analyze that data to score it from 0 to 100. Which one should I care about first, pipe A or pipe B? Different ways to summarize that.

Below that, you've got rehab details, again, that take your data and what's important to your particular network as a utility who knows your network best, how do you want to combine that to come up with potential rehab actions and what might that cost you to implement that rehab work there. So again, that's just a little bit on the map there.

Working down the interface there on the left, tasks, again, we talked a lot about PACP inspections, but NASSCO also supports MACP inspections. So these are the pipeline equivalent. The M stands for manholes, believe it or not, instead of pipelines, but you can do very similar process there with manhole inspections. We also support international types of inspections. MSCC and WSA. Other tasks, other types of inspections can also be brought into Info360, other types of work.

So here, we've got a list of manhole repairs that have been imported that, again, in order to know what you want to do next, you need to know the history of that particular asset so you can store all these different types of repairs or inspections and whatever tabular format you might have within this cloud solution that--

Again, the game-changer here is that you want to share this within your organization, you just copy and paste that link. Before, if this were a desktop tool, this would have been an export process, and that export would have been old by tomorrow. But the game-changing thing about Info360 Asset and our partner solutions like VAPAR is that all of this is much more shareable to those managers and planners that need this up-to-date asset information very quickly in order to make these decisions here.

So, next, if we work down the list, again, other types of data that are part of Info360 Asset. Again, you can bring in modeling results. You see results from InfoWater Pro, count of pipe cleans. You saw spatial data there, you saw simulation data you can bring in, all these other types of data sources there.

And once all this is in Info360 Asset, then you can start to use these tools in Info360 Asset toolbox is a good way to explain it. So this is risk analysis where we're basically saying, OK, again, it's your data. We don't want to be a black box solution. How do you want to combine your data in terms of likelihoods of failures and consequence of failures in order to come up with a risk score and rank your pipes or your manholes or any type of asset from 0 to 100, from low to extreme risk so that, again, you can be very transparent in how you're making these types of decisions here?

So here, you can see these are very variable. You can see which pipes have that CCTV and condition data, what pipes don't. You could export this out if you need to create offline reports as necessary. But the nice thing about, again, the cloud interface is that gave us a chance to make this, again, even easier to use, a little bit more transparent.

How you rank things in your different categories is as easily as sliding that bar to the left or to the right. As long as everything adds up to 100%, then your risk analysis will run and everything will check out there. So, again, very flexible in terms of how you can create these categories. Our goal is to give you a framework, but it's up to you. You know your model best, your consultants who work with you know model best and your data best.

And it's very flexible to create different components from simulation data, task information, GIS, proximity information, CCTV observations in ways to combine this to rank your pipes on a 0-to-10 scale, 0-to-100 scale-- whatever you like, really, in order to come up with that list of, here's where I need to be spending the most time and most dollars, and here's where my pipes are in pretty good shape, and this is a low, maybe, consequence or likelihood of failure.

So on the consequence of failure side, again, very similar. You've got these same slider bars, same streamlined interface that is very easy to adjust according to your criteria. It always amazes me how every utility delivers and collects the exact same thing, but the way you all do that and the data that's available really demands a flexible tool such as Info360 in order to help manage and prioritize, again, what comes next for your assets.

Again, likelihood and consequence of failure are combined in the risk setup here. So you can see those likelihoods of failures and the risks setup there on the risk setup side, and then how those are combined, how those are weighed with the different slider bars for likelihood and consequence of failure, you see those on the other side there. A lot of different ways for doing this.

You can put these into a matrix. You can adjust this so that you're actually looking at-- you're adding those scores together, you're multiplying those scores together. Regardless, it's made to be this flexible tool to adjust to the type of risk analysis you want to see and that makes sense for your particular project there.

So again, can normalize these, as I mentioned. You can normalize 0 to 10, 0 to 100. Separate those risk grades. You want only a few pipes to show up as extreme risk or do you want a lot of them. If you have a lot of budget, maybe you want all your pipes to show up as extreme risk because you're able to fix a lot that particular year or something like that. But again, the purpose is to make this flexible so that it can be certainly not a black box solution, but a very transparent solution to help with those, again, large-scale decisions and planning.

And while it's great to see your pipes lined up from most extreme risk to negligible risk, 0 to 100, it's maybe more important to know, OK, based on all this different data I've brought together, what should come next based on my particular logic? And so that's where our decision trees come in. Again, you can have multiple scenarios, just as you can for risk, for how these decision trees work.

But what you're doing here is you're asking your network a series of questions. Has this pipe been inspected, yes or no? And you can see the numbers there, true or false, if it has. If it has been inspected, how recently? In the last 10 years? Hopefully. If so, then what was the structural score? What was one of those CCTV scores it had?

If it was really significant, maybe we need to replace or line that pipe. If it wasn't that significant, maybe we just need to inspect it at some point. If it hasn't been inspected in the last 10 years and it's extreme risk, maybe we need to get there immediately, or if it looks low risk, maybe we don't need to do anything-- no action until we run this decision tree again.

These decision trees are hugely popular in our desktop tool, and they remain a staple of our cloud offering here within Info360 Asset. Again, our goal here is to be very configurable. Every network is different. What data you want to pull in here is really up to you. As long as that's tabular data, GIS data, CCTV data, there's some way you can bring this in here and start to ask questions of your network to more intelligently or more transparently guide your CIP and O&M action moving forward.

So once you run that decision tree, every pipe or asset gets an endpoint there. Suggested Rehab Action, you can see the resulting table here. So I've got my asset IDs there on the left, I've got rehab actions. The suggested costs for those, and again, that's user-defined. You can define how you come up with that cost information. You can begin to compare. And again, everything here can be shared with a link.

So if you need to really highlight a particular pipe that you think is critical, you can copy and paste that link, send it to your asset manager, send it to your director of that utility and bring attention to this. And you can export this if you want a snapshot in time to something like a CSV. You can filter this data so you only look at pipes that need to be CCTV'd immediately or something like that.

But ultimately, this is the end goal of Info360 Asset, is you've brought all this information together, you've analyzed it intelligently, hopefully even with adding VAPAR and machine learning to it as well, but you're now coming up with, OK, based on all this data, what happens next? What do I do with it? And you've worked down the steps in Info360 Asset in a very transparent way that makes it clear to all stakeholders how you got from noisy data in point A to the endpoint B there and a suggested rehab.

All right, so with that, that's the high-level overview of quite a bit, I think we covered, in this session. We started with the basics of CCTV analysis and how it is this perfect example of a dirty job. No one wants to be in that sanitary sewer main and doing the poo tube with the rats, and usually not as cute of creatures as I had, and some of those example videos like the fox and the dog.

But it's just a dirty job, no matter if you're doing the inspection or, if like Michelle's coworker, you're viewing hundreds of hours of CCTV data. That is just mind-numbing work. But there's technology out there like VAPAR's AI to help assist and make this dirty job at least a little less time-consuming. A little bit easier, a little bit more accurate, a little less error-prone, and just overall faster.

But at the end of the day, it's important to realize that CCTV is really only part of the asset management equation. As always, people, process, and technology come together, and VAPAR and Info360 Asset that are really only part of the solution.

And we realize, it's really only part of the data, too. There's a lot of other data inputs from GIS to hydraulic modeling, past work history, any legacy tables you've had in the past that you want to bring together in order to better answer that question, what comes next for this critical infrastructure that is aging and that is in need of innovative technologies to make up for that funding gap that we're facing?

So, potentially maybe we solved a little bit of this dirty job. At least we made it a little bit faster, I think. Thank you all for watching, and we look forward to any questions you might have.

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Terminus
We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
StackAdapt
We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
The Trade Desk
We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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We can access your data only if you select "yes" for the categories on the previous screen. This lets us tailor our marketing so that it's more relevant for you. You can change your settings at any time by visiting our privacy statement

Your experience. Your choice.

We care about your privacy. The data we collect helps us understand how you use our products, what information you might be interested in, and what we can improve to make your engagement with Autodesk more rewarding.

May we collect and use your data to tailor your experience?

Explore the benefits of a customized experience by managing your privacy settings for this site or visit our Privacy Statement to learn more about your options.