There is a fast-paced digital transformation taking place in many industries, driven by the emergence and convergence of digital technologies that include machine learning, artificial intelligence (AI), blockchain, cloud/edge computing, Internet of Things, 5G communication, automation, digital twin and the metaverse. In parallel, Industry 4.0 is now complemented by Industry 5.0, which sees enterprises shift from benefits for shareholders (profits) to benefits for stakeholders (people).
It is widely recognised that civil engineering practice, including underground construction, is lagging behind in the digital transformation. There is no widespread adoption of Building Information Modelling, much less digital twin and the metaverse. But digital transformation has the potential to transform the entire built environment, bringing forth new modes of working and living and new products, services and systems to support these modes. While the metaverse may appear a far-fetched concept for underground space at this moment, its impact on the future of infrastructure is widely discussed.
The purpose of this special issue is to advance research in data-centric geotechnics with a focus on underground infrastructure. The short-term goal is to support decision making in complex underground infrastructure from design, construction and operation, to delivering services to end-users that will drive our profession towards whole-life decisions and recognising the value of the entire data estate. The long-term goal is to realise the underground metaverse.
These include, but are not limited to:
- Data-driven site investigation
- Machine learning and big data techniquesin tunnel and underground engineering
- Building Information Modelling in tunnel and underground engineering
- Reliability-based design in tunnel and underground engineering
- Construction risk assessmentand management
- Data mining and machine learningto support decision making in construction and maintenance of tunnels and underground structures
- Integration of data-driven and physics-based methods inunderground engineering
- Machine learningand computer vision in underground structural health monitoring
- Submissions close: 30 June 2023
- Final decision: 31 December 2023