Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement

A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity.

Automated summary

A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed, which accurately calculates the modal responses and identifies the damage ratios of offshore jacket platforms. The framework consists of an OSP model and a damage identification model. The OSP model optimizes the sensor number/location using the multi-objective Lichtenberg algorithm (MOLA) to balance the sensor cost and modal calculation accuracy. The damage identification model uses the Markov Chain Monte Carlo (MCMC)-Bayesian method to calculate the structural damage ratios based on modal information obtained from sensory measurements, considering the uncertainties of structural parameters. The method is validated using an offshore jacket platform, demonstrating efficient identification of structural damage location and severity.