Advanced digital technology is being deployed across the transport industry to improve the efficiency of road construction, maintenance and operation, revolutionising the sector.
This is one of the trends emerging at the Southern African Transport Conference (SATC) currently underway here, where several papers have unpacked the impact of technology on the transport industry.
Digital twinning for sustainable roadbuilding
An SATC paper by N Miravalls and H Pley-LeClerq described how innovative digital twins are being developed to provide advanced impact assessments of planned projects, and to then recommend the most financially and environmentally sustainable construction approach.
The researchers conducted a case study of a road upgrade in Uzbekistan, which was used to pilot a multiple-scenario digital analysis of its pavement design.
The rehabilitation of the rural, 4km 4R180 road was assessed by creating a digital twin using data on environment characteristics, local material sources, traffic, and weather conditions.
Various pavement design options were assessed according to costs, carbon emissions, resilience, resource consumption, maintenance, safety and water drainage.
Through a modelisation set up on the ORIS platform, the project team performed a pavement design analysis comparing the Base Case (asphalt road with granular base) with alternative designs. This analysis identified opportunities to lower costs by 51% ($1.8 million), spare 14 700 tons of natural resources and enable the use of 24 100 tons of recycled materials.
By challenging the identified alternatives with low-carbon and circular solutions, the project’s environmental footprint was also significantly improved, with 6 690 tons of CO2eq avoided (16.8 % compared to the Base Case).
Drones for bridge inspections
Closer to home, L Kemp, R Matchett, MP Roux and L De Klerk delivered a conference paper analysing opportunities for drone assessments in the bridge-inspections sector in South Africa.
At present, bridge inspections for strategic bridges require an under-bridge inspection unit (UBIU) – a heavy piece of machinery mounted on a large truck. Strategic bridges are often difficult to access due to the height and size of the structures, or because they are located over rivers.
The researchers found that there were potential cost- and time-saving benefits for bridge inspections to be conducted using drones or unmanned aerial vehicles (UAVs).
Their report stated that inspections using images and point cloud models could be performed faster compared to the traditional in-person TMH19 inspections – as long as inspectors were comfortable and accustomed to the new inspection methodology.
The new inspection methodology would not require a technical assistant and inspections could be performed off-site. Bridge inspectors would then not have to travel between structures and could inspect more structures per day.
The new drone technology would allow for capturing of images using a drone, and the inspection of structures using the “point cloud model”, by senior bridge inspectors.
“The new methodology could replace the use of an UBIU and capture images with a UAV, reducing the cost of strategic inspections and ensure the safety of bridge inspectors,” the researchers said.
AI for efficient overloading measurement
Another promising application of new-generation technology involves using artificial intelligence (AI) to monitor and control the overloading of vehicles.
Research by A De Koning and AJ Hoffman proposed an intelligent weigh-in-motion (IWIM) algorithm to decrease unnecessary static weighing of vehicles through data sharing between Traffic Control Centres (TCCs) and intelligent data interpretation.
At present, TCCs protect the country’s road infrastructure via overload-control weighbridges along major freight corridors. However, these systems lack the information to support intelligent decision-making. This means that 75% to 85% of the vehicles weighed are found to be legally loaded – an unnecessary waste of time and fuel.
The paper found significant inefficiencies in existing overload control operations, as the system does not share information between TCCs, so most legally loaded vehicles are subjected to repeated static weighing.
The authors proposed a novel overload control method that shares data between stations to allow more intelligent decisions to be made about which vehicles need to be statically weighed.
The AI would use inputs such as weight measurements at the previous static scale and the current WIM scale, as well as travel time from the previous scale.
It was found that a Random Forest Tree (RFT) AI model produced the best performance to differentiate between overloaded and legal vehicles, achieving an average improvement of 65.83% in terms of vehicles to be statically weighed when compared to the current rule-based system.
The paper suggested that implementation of the IWIM concept could have a significant positive impact for all stakeholders involved in the freight movement process.
The researchers said that since 79% of South African goods are moved by road, it was essential to use all available technology to ensure high-quality road infrastructure.
