Edward Wilkinson, strategic corporate development lead, GScan looks at the future of data-driven infrastructure asset management.
The construction industry is notorious for its failure to adopt innovation. It has been slower to embrace the data and technology-driven techniques of other industries. But to progress, there is an increasing need to overcome this hurdle. The evolution of infrastructure asset management (IAM) from legacy processes to today’s technology-driven approaches will be particularly transformative, enabling more effective planning, maintenance, and optimisation of physical assets.
Physical assets are in many cases still managed through manual data collection and, in some instances, continue to be largely paper based, making it time-consuming and prone to errors. Systems are siloed, with limited integration for different asset types (e.g., roads bridges, and tunnels) and between management systems. Therefore, it is difficult to get a comprehensive view, preventing a cohesive asset management process. This in turn obstructs a dynamic approach to management and budget allocation.
Addressing these challenges requires a strategic approach, including stakeholder engagement, comprehensive planning, and continuous monitoring and improvement, as well as recognising that things can be done more efficiently and there is technology available to differentiate excellent from poor.
There are many exciting innovations in the field. Both Building Information Modelling (BIM) and Geographic Information Systems (GIS) are pivotal in capturing, processing, and visualising data for IAM. These tools help in creating detailed digital representations of physical assets, facilitating better planning and management. Yet these systems are only as good as the quality of the available data of which there is an abundance, and therefore more insightful technology is required at the data harvesting and analysis stages.
The adoption of innovative, data rich technologies will continue to transform how infrastructure assets are managed, shifting stakeholders from reactive management to predictive maintenance. At the forefront of this evolution are novel data collection methodologies like muon tomography. This advanced non-destructive testing method enables unparalleled data-driven insight into what is happening inside the structure at a depth of up to 10 metres and to an accuracy of 1mm. For the first time, we can be more proactive and preventive in terms of maintenance, prolonging the useful life of our critical infrastructure.
Advanced data collection capabilities will be complemented by highly integrated systems that facilitate seamless data flow and comprehensive asset management across different phases of the asset’s lifecycle. This will in turn allow asset operators to utilise IoT and embedded sensors to check the health of their assets in real time.
Cutting edge AI and machine learning algorithms can analyse sizable amounts of data to predict failures and optimise maintenance schedules and timely interventions. Scalable, cloud-based platforms would provide storage capacity and processing power to leverage large datasets. They can also enable the creation of asset libraries that provide comprehensive benchmarking and structural integrity trends.
Data driven approaches therefore have the potential to minimise downtime and extend the lifespan of assets. Consequently, they positively impact other integral parts of the asset lifecycle: costing, risk management, sustainability, and asset resilience. Costing involves accounting for the investments associated with an asset, from design and construction to operation and disposal. It informs financial decisions and optimises resource allocation. It also underpins risk management and mitigation strategies, defining the reliability and safety of infrastructure, as well as forming a staged system of management aligning interventions with age and status of the assets in question.
Sustainability and asset resilience have become critical considerations for IAM, ensuring structures can endure environmental and societal changes. Data driven technologies have the potential to enhance sustainable development and disaster preparedness.
The evolution of IAM reflects a broader trend towards leveraging technology to enhance efficiency, reduce costs, and improve the resilience of infrastructure systems. This transition is ongoing, with continuous advancements in technology promising even more sophisticated and effective asset management solutions in the future.