There is a worldwide increase in tunnel excavation especially for infrastructure development which aims to improve the amenity of urban life. When construction of these tunnels is near urban dwellings and/or rockmass conditions dictate, mechanised tunnelling methods are often favoured. In Sydney, Australia, the geotechnical conditions suit the use of roadheaders for construction in massive sandstone of multi-lane vehicular tunnels with project costs measured in the hundreds if not billions of dollars. Despite their enormous costs, these projects are highly competitive with tight budgets and of relative short duration, consequently there is little incentive to invest in the development of computerised data collection and monitoring systems.
A recent project was undertaken involving a major road tunnelling project having multiple faces and construction sites where a system was developed comprising data collection, aggregation and analysis of a fleet of roadheaders with the aim of providing information to improve construction management. Over a 36-week period, data was collected from 22 roadheaders entailing 122,000 shift activities across 9200 shifts together with changes in geotechnical conditions. The data was combined into a single database that provided useful productivity metrics to the various project management teams and other stakeholders.
The project demonstrated the benefits of a largely automated, centralised data model that provided timely and reliable information and, eliminated a significant amount of data-entry work resulting in more productive time for site engineers. The value of such a system was realized when it was implemented in a subsequent construction project and used to provide reliable data in the planning and tendering of future projects. The system enabled roadheader productivity to be optimised in the project, by for example confirming the critical path in the roof support installation stage of the excavation cycle could be reduced in adopting split-face headings rather than full-face headings.
The use of large-scale data analytics to drive business improvement is increasingly being used across many industries as the technology used to collect and interrogate data becomes more accessible, and the commercial benefits of such information become apparent. For example, as the mining industry moves towards integrating robotics and automation into its processes, an unprecedented amount of data relating to every stage of a mining process is available to organisations to drive improvements in productivity, design and planning.
