Road and rail maitenance is the next frontier in complete automation.
The technical capability to automate more activities in road and rail maintenance has been available for some time. Manufacturers have developed technologies to automate everything from data collection to paving, but uptake has been slow.
But that could be changing, thanks to pressure from two different directions: the UK government’s building information modelling (BIM) requirements for publicly funded projects; and the move to condition-based – and ultimately to preventative – maintenance.
“Over the last three or four years we have seen a massive ramping up of BIM-awareness,” says Tarmac continuous improvement manager Rachael Bennison. Tarmac is rolling out an automated data collection system for its surfacing activities. “We also had a client that we were tendering a maintenance contract for who was very passionate about this and wanted to get better understanding of their asset over the long term,” she says.
Tarmac has spent three and a half years looking at and trialling technologies to improve its surfacing work. As a result, the company has invested in a system that encompasses hardware and software that will automate data collection on site, improve quality control, and provide a permanent record of the asphalt laying process.
“Currently, the truck comes in and delivers asphalt, and we record the temperature manually, as well as the location, the weather conditions and where the material was supplied from,” Bennison explains. “This technology has sensors on the paver that records all this information automatically.”
The location is determined using GPS on the paving machine, while an on-board weather station records the temperature and conditions. Temperature sensors identify the exact temperature at which the asphalt goes through the paving machine, and sensors on the paver read radio frequency identification (RFID) tags on the rear of each truck to record information about the delivery, including when and where it was mixed and the length of time between mixing and laying. “As the truck reverses back, a sensor reads this time and location tag as it starts tipping,” explains Bennison.
This system of sensors on the paving machine is part of a wider network of data collection and measurement that extends to compaction equipment. Once asphalt has been laid, a sensor on the roller records the temperature of the material during the first pass. The roller is also equipped with software that records the number of passes over each section of road in a 150mm square grid and compares them with the number required.
The information is displayed in the cab, so the operator can see where to roll. “At the moment, it doesn’t control the roller – it still relies on the operator to have the experience to see where they need to go,” says Bennison, who describes the system as “roll by numbers”.
Technology also exists to automate paving and compaction. For example, Leica Geosystems launched its first 3D paving control system more than a decade ago. It uses a total station or a GNSS sensor to track the paving machine’s exact position and elevation; software compares this information with the design information, and then links with the paver’s control system to adjust it to the required position to lay the pavement as required.
Over the last three or four years we have seen a massive ramping up of BIM-awareness
Rachael Bennison, Tarmac
The system can be used for laying rail trackbed as well as road pavement.
On the compaction side, specialist manufacturer Bomag already offers a fully automated option for its asphalt rollers capable of identifying the level of compaction required, applying the correct amplitude and stopping when the asphalt reaches the right density or stiffness. The company calls it “intelligent compaction”.
“You can set a target value,” explains Bomag vice president, marketing Jonathan Stringham. “The amplitude varies, and when you get to the target value, it will stop.”
This ensures the right density without is achieved without over-compacting, as can happen when rolling a prescribed number of times.
In Germany, where Bomag is based, the road authorities are happy to accept data from this automated system as a record of the compaction achieved. But in the UK, it is still standard to specify the number of passes, with this number being calculated based on the designer’s understanding of the materials and ground conditions. It is one reason why Tarmac opted for a system based on the number of passes – although it has been trialling the more sophisticated systems.
Bennison says there are two reasons for automating data collection: to ensure the site team is “getting it right on the day”, and to create an accurate record of what is laid where. “We’re interested in making sure we improve the quality of our materials,” she explains. “For our clients, it’s about understanding their asset.”
You may be taking a person away from doing some data monitoring on site, but you need more resources for dealing with the data back in the office
Rachael Bennison, Tarmac
Clients can combine the data that is collected on the paver and roller with other information they might be recording, such as vehicle numbers, to build up a detailed model of the road that, in turn, should help them to understand why roads fail when they do and – hopefully – to know when to carry out preventative maintenance.
Deciding which new technologies to invest in is crucial to their success, says Bennison: “We have taken technology that was being used for other things and used it for what we’re doing. There are all sorts of other things we could do – we could add video into this or infra-red cameras. But at the moment it’s about learning from what we’re already doing.”
It is also important that the technology is appropriate to the working conditions, Bennison adds: “We are working in harsh conditions with very hot asphalt going across the sensors. And equipment is out in all weathers.
Simple, robust, user-friendly
“You have to keep these things very practical; you need to build a high level of robustness into the kit you use; and you have to make sure it’s user-friendly. Anything more complicated is going to fall by the wayside.”
She also points out that automation does not necessarily mean you need fewer people to do the work: “You may be taking a person away from doing some data monitoring on site, but you need more resources for dealing with the data back in the office. If you can’t store it, process it and move it around with your clients, it’s not worth having.”
The advent of “smart roads” that provide drivers with journey time information, or warn them about hazards or delays ahead, could soon also be helping to predict future maintenance requirements. Once vehicles are linked into the smart road infrastructure, cars could be collecting and relaying information on the condition of the road while driven.
This is already starting to happen on the rail network, where the condition of the track can be monitored using measurement of vibration within the train wheels. Vibration specialist Perpetuum initially developed sensors to identify when train wheel bearings needed replacing. It is now working with Network Rail to look at using them on normal passenger trains to record information about the state of the track.
The rail maintenance sector is also taking a lead in the use of artificial intelligence – not for operating machinery but for scheduling work.
Hong Kong’s Mass Transit Railway Corporation has been using AI to plan, schedule and optimise engineering work on all the Hong Kong rail lines since 2013, with 10,000 people carrying out over 2,600 individual maintenance jobs every week.
The AI engine automatically generates a weekly schedule of all maintenance work, and is optimised to enable MTR to do more work with the same resources.
The system also checks that any proposed work does not clash with work already in the schedule, and checks everything against safety regulations and operational guidelines.
As MTR operates services in London, it is possible that we may see AI being rolled out here.