The new system can detect smoke in this 1.4 km long, 40 metres wide, undersea link in a matter of seconds, say D-Tec, unlike conventional solutions which struggle in the confines of a tunnel.
The box-like Palm Jumeirah tunnel is a major engineering feat, having required 185,000 m3 of concrete and 30,000 tons of reinforcing steel and has been designed and constructed to hold three individual tubes with the outer two tubes carrying three lanes of traffic and pedestrian walkways in each direction. The inner tube is used as a service tunnel and also in the event of an incident for emergency evacuation.
For the Dubai tunnel solution, the FireVu units are connected to 28 fixed CCTV cameras positioned strategically throughout the tunnel’s two outer tubes – 14 in each. The cameras used for Video Smoke Detection are the same as those for security and other surveillance tasks such as traffic management in the tunnel, thus maximising the return on investment.
The FireVu approach works by using CCTV images, in real time, from a number of cameras simultaneously that are then analysed by specialised image processing software. VSD seeks out the particular pattern that smoke produces through the application of extensive detection and known false alarm algorithms.
By programming the software to look for anticipated motion patterns of smoke over a specified area within the camera image, an analysing pixel changes, VSD has the potential to react to an incident in a matter of seconds.
Alarm and associated video images can be relayed back to a central control room and can be reviewed using NetVu ObserVer video management software. Multiple cameras provide all important situational awareness so that drivers and their passengers can be directed safely away from danger.
This is especially beneficial in tunnels with a gradient where as smoke rises it will tend to gather more on one side of the fire. As all alarm events are recorded on the system’s NetVu Connected Digital Video Recorder (DVR), these can be readily accessed for pre and post-event analysis, allowing the operator to view what, or who, caused the incident.