The shortlisted candidates for the Application of Digital Technology Award in no particular order are;
- Scanprobe Techniques Ltd
- Wessex Water Ltd / Headlight AI Limited / Bright Innovations Group Ltd
- Plowman Craven Ltd / IWJS
Good luck to you all!
Scanprobe Techniques Ltd – Mina Survey
Mina Survey is a free mobile app available on iOS & Android, which enhances the trenchless survey capabilities for the drainage and utilities industries. Connecting wirelessly to your Scanprobe push-rod camera system, it enables the user to create and deliver fully formatted reports with in-pipe photos & digital drawings to the customer on site, in minutes, at no extra cost. Saving the engineer time and effort, and providing the customers with a visual and trustworthy report.
Plowman Craven Ltd / IWJS – Macclesfield Gyro
Plowman Craven’s utilities team was asked to find the line and level of 2x 90m culverts that ran under a road so that connections could be made from a new housing development in Macclesfield.
Using a state-of-the-art gyroscopic mapping system our surveyors were able to successfully locate both the 600mm and 800mm culverts at a depth of 15m – despite the many challenges of dense vegetation, dangerous access and utility congestion.
Wessex Water Ltd / Headlight AI Ltd / Bright Innovations Ltd – ‘Telesto’ 3D LiDAR Modelling of Tunnels in Semi-Turbulent Flow
Telesto is a multi-sensor system with intelligent software that attaches to a floating platform that traverses underground assets for 3D modelling in semi-turbulent flow. It enhances the health and safety for workers involved in the surveying of subterranean assets by preventing confined spaces entry, thereby removing the associated hazards. It goes beyond CCTV and laser profiling solutions on the market and provides a more cost-effective route towards asset digitalisation compared with traditional surveying techniques. The customer obtains the 3D information and the position of defects and anomalies, which are automatically extracted and reported using traditional and machine learning approaches.