search.noResults

search.searching

saml.title
dataCollection.invalidEmail
note.createNoteMessage

search.noResults

search.searching

orderForm.title

orderForm.productCode
orderForm.description
orderForm.quantity
orderForm.itemPrice
orderForm.price
orderForm.totalPrice
orderForm.deliveryDetails.billingAddress
orderForm.deliveryDetails.deliveryAddress
orderForm.noItems
THE MAGAZINE FOR THE DRAINAGE, WATER & WASTEWATER INDUSTRIES


EVENTS NEWS


Image courtesy of IKT – Institute for Underground Infrastructure


delegation – Marie Noto from the Tokyo Metropolitan Sewerage Service Corporation and Dr. Eng. Yasuaki Sugimoto from Nippon Koei Co. Ltd. – who did a fantastic job presenting despite language barriers. They shared how using fewer, more clearly defined condition classifications and a staggering amount of high-quality control data had made their AI models more reliable. It was a great example of how keeping things simple can sometimes produce the smartest results.


Another strong thread through the event was digital transformation. Digital twins, BIM, flood prediction, and remote monitoring were all hot topics. WinCan Web is already successfully using AI to detect defects, with strong results in streamlining condition assessments and reducing the need for manual review. Combined with its LiDAR modelling capabilities – used to generate highly detailed models of underground assets – this makes WinCan a powerful tool, leading the way in accurate, data-rich reporting. The detail LiDAR offers is remarkable, but it comes with challenges: large file sizes, demanding storage requirements, and transmission limitations in the field. Looking ahead, there’s potential for LiDAR itself to take on defect detection roles, particularly when integrated with AI, which could reduce reliance on traditional image-based assessments.


Image courtesy of IKT – Institute for Underground Infrastructure


with flexible sensor 'tentacles' that measure everything from gas concentration to water levels. All that info gets sent to the cloud for alerts and analysis. It's clever, useful, and shows just how imaginative this space is becoming.


Sensor tech was another big talking point. Swarm sensors like Jellox – a logger designed for harsh environments – are making real-time, continuous monitoring more realistic. Jellox is inspired by a jellyfish,


This naturally fed into wider conversations about open data and collaboration. The UK’s National Underground Asset Register (NUAR) came up as a strong example of progress – a government-backed platform to support better data sharing between organisations. It makes sense, really. If everyone’s working from the same map, you get fewer surprises and fewer costly mistakes. There’s also a welcome push towards a more standardised approach across the industry. There was broad agreement that if we want to scale innovation, we need to start speaking the same language – literally, when it comes to definitions and formats.


Of course, you can’t talk about modern sewer inspection without talking about drones. Andrews Engineers from Canada gave a great live demo of the Flyability Elios 2 drone flying through IKT’s rather dusty test sewers. It handled it with ease, thanks in no small part to the skilled operators behind the controls. That said, drones come with their own limitations: battery life, signal strength, cost, and, in the case of aerial drones (for aerial mapping), restrictions on where they can fly. Still, they’re incredibly useful tools for reaching dangerous or hard-to-access spaces, and there was no shortage of impressed faces in the demo area.


Image courtesy of IKT – Institute for Underground Infrastructure


linkedin.com/company/draintrader


One of the more entertaining and memorable moments came early on Day Two, when attendees were welcomed by “Robo-Hund” – a


May 2025 | 21


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80