Risk-based integrity model for offshore pipelines subjected to impact loads from falling objects

Jiang, Fengyuan; Dong, Sheng

doi:10.1177/1475090220960562

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…39,42 Collision damage probability and spill volume to chemical tankers have been estimated by a logistic regression metamodel proposed in Sormunen et al 43 Metamodels can help decision-makers to consider risk associated with failure probability. 44 Among various metamodels, Deep Neural Network (DNN), also known as Artificial Neural Network, is a quite promising approach for the current objectives. Papanikolaou 47 proposed a DNN to predict collision damage.…”

Section: Metamodelingmentioning

confidence: 99%

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An optimized metamodel for predicting damage and oil outflow in tanker collision accidents

Das

Goerlandt

Tabri

2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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Society is concerned about maritime accidents since pollution, such as oil spills from ship accidents, adversely affects the marine environment. Operational and strategic pollution preparedness and response risk management are essential activities to mitigate such adverse impacts. Quantitative risk models and decision support systems (DSS) have been proposed to support these risk management activities. However, there currently is a lack of computationally fast and accurate models to estimate oil spill consequences. While resource-intensive simulation models are available to make accurate predictions, these are slow and cannot easily be integrated into quantitative risk models or DSS. Hence, there is a need to develop solutions to accelerate the computational process. A fast and accurate metamodel is developed in this work to predict damage and oil outflow in tanker collision accidents. To achieve this, multiobjective optimization is applied to three metamodeling approaches: Deep Neural Network, Polynomial Regression, and Gradient Boosting Regression Tree. The data used in these learning algorithms are generated using state-of-the-art engineering models for accidental damage and oil outflow dynamics. The multiobjective optimization approach leads to a computationally efficient and accurate model chosen from a set of optimized models. The results demonstrate the metamodel’s robust capacity to provide accurate and computationally efficient estimates of damage extents and volume of oil outflow. This model can be used in maritime risk analysis contexts, particularly in strategic pollution preparedness and response management. The models can also be linked to operational response DSS when fast, and reasonably accurate estimates of spill sizes are critical.

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Section: Metamodelingmentioning

confidence: 99%