The novel Digital Twins paradigm encompasses the entire life cycle of built assets (buildings, bridges, and other large-scale structures), namely, (i) planning and design, (ii) construction, (iii) inspection, maintenance, operation (iv) rehabilitation or replacement and (v) demolition and decommissioning or reuse. A digital twin comprises the creation of an as-is historical asset information model interconnected with its physical counterpart through the implementation of a structural health monitoring system, model-based (multi-scale finite element models), and physics-informed data-driven (artificial intelligence anomaly detection algorithms) components. While the data-driven surrogate models allow real-time damage detection and early warning alerts, the detailed finite element models of the asset are used for physics-informed synthetic data generation purposes, damage prognosis, and the simulation of what-if scenarios that, in combination with probabilistic and reliability analysis, aid asset managers to take informed decisions about the optimal maintenance, retrofitting and repairing of the physical asset. This new paradigm results in extended life, operation costs reduction, as well as increased resilience and sustainability of the built environment.

Relevant publications:

Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). Bridge Management through Digital Twin-based Anomaly Detection Systems: A Systematic Review. Frontiers in Built Environment. DOI: https://doi.org/10.3389/fbuil.2023.1176621

• Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). DTADD Systematic Review Preprint. Zenodo. DOI: 10.5281/zenodo.7673718.
• Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). DTADD Systematic Review Protocol (amended). Zenodo. DOI: 10.5281/zenodo.7546576.
• Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). DTADD Systematic Review Search Strategy (amended). Zenodo. DOI: 10.5281/zenodo.7546557.
• Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Zenodo. DOI: 10.5281/zenodo.7548017.

Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). Uncertainties in the synthetic data generation for the creation of bridge digital twins. Presented at the 5th International Conference on Uncertainty Quantification in Computational Science and Engineering, Athens, Greece. URL: https://2023.uncecomp.org/proceedings/pdf/20020.pdf

Jiménez Rios, A., Plevris, V. and Nogal, M. (2023). Synthetic data generation for the creation of bridge digital twins what-if scenarios. Presented at the 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Athens, Greece. URL: https://2023.compdyn.org/proceedings/pdf/21262.pdf

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