The last few weeks has seen RAAC (Reinforced Autoclaved Aerated Concrete) dominate not just the built environment news but the national news, with many public and private sector organisations putting RAAC on or higher up their property and estates agenda.
Although much discussion has already taken place on the management, remediation, or estate optimisation side of the RAAC issue, little has been said about the role that digital, data and AI can play in supporting those who think they might have RAAC in their buildings.
For most estate managers there is probably a plethora of data available to them about their estates – historical reports, research papers and structural assessments across different departments inhouse or held by external suppliers. Working with digital specialists, using Artificial Intelligence (AI) can take all these records and apply knowledge mining techniques to extract valuable insights from extensive data sources related to RAAC, looking for keywords or phrases that might indicate whether RAAC is likely and if present, indicate where it sits within the estate. Image recognition AI can also be run through records, searching for material properties that might have been identified as potential weaknesses in relation to RAAC or match images of builds that since have been identified as being built with RAAC with those in existing data sets.
These knowledge discovery methods can produce a wealth of information that analysed through a team of experts can create data-driven risk mitigation strategies tailored to clients’ specific needs. We know from our experience at RLB that different sectors are at different points in their understanding of RAAC in their estates, and their treatment of it. For example, the NHS has a mitigation plan in place since 2019 for hospital buildings with confirmed RAAC that is backed with a £698 million investment for trusts to put in place necessary remediation and failsafe measures. They have also stated they are looking to have eradicated RAAC in the NHS estate entirely by 2035. However other sectors might be looking at different strategies where it is more cost effective to demolish the building and build new.
Leveraging a data-driven strategy bolstered by digital capabilities enables the consolidation of various datasets. This ensures informed decision-making tailored to each specific scenario, with priorities set in line with budgetary considerations. Furthermore, bespoke AI models can be developed to utilise the accumulated data, aiding in predicting the degradation rate of a RAAC structure. This includes evaluating factors such as moisture impact and contributing to a mitigation strategy. Digital insights and reporting can subsequently be designed to showcase the future roadmap, emphasise strategic priorities, and provide real-time updates on RAAC management in alignment with the strategic plan. And of course, digital can ensure that this RAAC plan can be integrated into other maintenance and remedial plans, so that clients can see where money spent on RAAC might help save money spent on other proposed remedial work.
This approach is a collaborative one, with cost, project, and programme management experts and those in digital teams working closely with client, structural engineers and relevant stakeholders from the knowledge mining stage, through strategy, planning and mitigation stage, bringing a measured and planned approach to how we deal with RAAC in our built environment.
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