Kings College team looks into the heart of darkness

Researchers at Kings College, London, are making hard-to-reach sections of the UK’s ageing sewage network more accessible to inspectors by employing ultrasound sensor scanning.

The research, funded by the Engineering and Physical Sciences Research Council, is being carried out by a team in the Department of Mechanical Engineering at King’s College London, led by Dr Kaspar Althoefer and Professor Lakmal Seneviratne.

The UK sewer network is around 250,000 kilometres long, some of which is 100 years old and at least half is 50 years old. It is estimated that some 20 percent is damaged in some way, resulting in around 5,000 collapses a year and 200,000 blockages.

While some of the sewers are large enough for inspectors to walk through, most are too small.

Currently video cameras mounted on wheeled trolleys are pulled through the narrow pipes. The resulting video footage is then examined. However, if there is any water in the pipe, the video will not be able to ‘see’ below it. Also, it is time-consuming to examine the film as a one-kilometre stretch of sewer generates about three hours of video footage.

The ultrasound sensor is reportedly able to scan the pipe under water. In addition, the low power laser projects a circular pattern on the wall of the pipe, which is picked up by the camera.

If there is any distortion in this ring or a change in its intensity at any point on its circumference this could indicate a defect. The advantage of this approach is that it does not require a powerful light source to illuminate the whole scene for the camera. It also makes it easier for the camera to distinguish between genuine defects and apparent defects, which are actually reflections of light from the wall of the pipe.

‘Once we have collected the data and stored it digitally, we then subject it to analysis with intelligent computer software based on neural networks,’ said Dr Althoefer.

Here, the computer can be ‘trained’ to recognise the characteristics of a defect from the sonar or camera data. This would allow it to detect any defects automatically, pinpointing their position in the pipe, for further investigation and remedial action where necessary.