Chapter 9. Digital Transformation by the Implementation of the True Digital Twin Concept and Big Data Technology for Structural Integrity Management

For re-assessment of ‘traditional’ industry asset management methods the key technology is Structural Health Monitoring (SHM) combined with the recent development within novel Big Data technologies. The technologies support the digital transformation of the industry with the purpose of cost reduction and increase of structural safety level. Today’s State-of-the-Art methods encompass novel advanced analysis methods ranging from linear and nonlinear system identification, virtual sensing, Bayesian FEM updating, load calibration, quantification and propagation of uncertainties and predictive maintenance. Challenges approachable with the new methods cover structural re-assessment analysis, Risk- and Reliability-Based Inspection Planning (RBI), and new ground-breaking methods for damage detection; many of which exploit recent advances in Machine Learning and AI and the concept of the ‘True’ Digital Twin. In this paper, a selection of the new disruptive technologies is presented along with a summary of the limitations of current approaches, leading to suggestions as to where tomorrows’ new methods will emerge. New frameworks are suggested for the way forward for future R&D activities based on an Ontological Approach, founded on a shared communication purpose and the systemising/standardisation of the methods for performing SHM. The Ontology Approach can be embedded in, or made compatible with, organising (and decision-supporting) frameworks based on Population-based SHM methods and extended Probabilistic Risk Analysis. The new ideas also offer the potential benefit of gaining information/learning from a large pool of structures (the population) over time and by transfer learning, transferring missing information to individual structures where less (or no) specific data are available.