Dubai’s Roads and Transport Authority (RTA) has upgraded the automated system for assessing the condition of roads, managing their maintenance and pavement to achieve the relevant strategic objectives.
The system constitutes a comprehensive programme for automating and governing the assessment of the performance of the functional and construction pavement condition of the road network through software applications such as the automated systems for pavement management system.
“This system is a digital version of roads with artificial intelligence techniques in implementing the strategies and policies of RTA and maintenance department to assess the road network and optimum selection of the appropriate type of maintenance within the allocated budgets. This development is subject to great challenges given the continued expansion of the road network and these challenges are represented by the exposure of this network to various factors that lead to the emergence of some damages due to its obsolescence caused by environmental factors or operational payloads,” said Hamad Al Shehi, Director of Roads and Facilities Maintenance, RTA.
“To achieve the transformation to the digital version, the software mechanism has been developed to identify and schedule the implementation of the annual maintenance needs based on the planning that is carried out through these modern systems, to maximise the satisfaction of road users first and rationalize operating expenses secondly. The system has been developed to distribute the budget to the network maintenance activities to ensure that it maintains quality though optimal utilisation of the allocated budget,” added Al Shehi.
“The automated smart system for assessing the condition of roads and managing their maintenance uses laser scanning techniques to configure the digital copy of roads and verify the life cycle of paving assets and their maintenance in the Road Maintenance and Facilities Department. The life cycle of RTA’s Agile PMS is characterized by the accuracy of data and a reference compatible with reality. All parts of the road network within the system have a fixed digital label in the system database, which represents the smallest part that can be used in planning maintenance works.”
He added, “The road network has been divided into parts not exceeding 100m/lane to raise data accuracy to 99%. It requires raising the reliability index of data accuracy and matching it with sites to more than 97%, accurately locating damages on the network through the automatic detection of damages, determining the optimal type of maintenance on the network and rationalizing the operational expense. It requires performing maintenance on priority sites while making the most of the allocated budget. Accordingly, the system can list roads length based on their functional importance such as highways, main roads and internal roads, as the digital version of roads contributes to automating the process.
Al Shehi concluded, “The economic feasibility of developing the digital version of the roads and smart maintenance management systems to maintain the network has multiple benefits that include: keeping pace with the industrial revolution in generating the digital version of the roads and optimising the spending on maintenance as maintenance of the road network means fundamental investment in the value of road assets.
Through the digital version of roads developed using artificial intelligence to evaluate the paving of roads to ensure their sustainability and maintain their operational condition, this smart system has achieved savings in operating expenses equivalent to 78% of the annual maintenance works, thanks to the use of smart software to ensure the optimal planning of maintenance works of the new system compared with the traditional method.
