A machine learning enhanced structural response prediction strategy due to seismic excitation

Abstract Operators of offshore floating drilling units have limited time to decide on whether a drilling operation can continue as planned or if it needs to be postponed or aborted due to oncoming bad weather. With day-rates of several hundred thousand USD, small delays in the original schedule might amass to considerable costs. On the other hand, pushing the limits of the load capacity of the riser-stack and wellhead may compromise the integrity of the well itself, and such a failure is not an option. Advanced simulation techniques may reduce uncertainty about how different weather scenarios influence the system’s integrity, and thus increase the acceptable weather window considerably. However, real-time simulations are often not feasible and the stochastic behavior of wave-loads make it difficult to simulate all relevant weather scenarios prior to the operation. This paper outlines and demonstrates an approach which utilizes probabilistic machine learning techniques to effectively reduce uncertainty. More specifically we use Gaussian process regression to enable fast approximation of the relevant structural response from complex simulations. The probabilistic nature of the method adds the benefit of an estimated uncertainty in the prediction which can be utilized to optimize how the initial set of relevant simulation scenarios should be selected, and to predict real-time estimates of the utilization and its uncertainty when combined with current weather forecasts. This enables operators to have an up-to-date forecast of the system’s utilization, as well as sufficient time to trigger additional scenario-specific simulation(s) to reduce the uncertainty of the current situation. As a result, it reduces unnecessary conservatism and gives clear decision support for critical situations.

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Seismic Performance of Curved Bridges with Longitudinal Slope

Applied Sciences ◽

10.3390/app9040771 ◽

2019 ◽

Vol 9 (4) ◽

pp. 771

Author(s):

Peng Su ◽

Yanjiang Chen ◽

Zhongwei Zhao ◽

Weiming Yan

Keyword(s):

Ground Motion ◽

Structural Response ◽

Seismic Excitation ◽

Tangential Displacement ◽

Site Conditions ◽

Rotation Effect ◽

Curved Bridges ◽

Curved Bridge ◽

Displacement Response ◽

Main Girder

A curved bridge test model with a scale ratio of 1:10 was constructed to investigate the influence of site conditions on curved bridges with longitudinal slopes based on a similar theory. The natural ground motions of five different groups, namely, Sites A–E, were selected from the Pacific Earthquake Engineering Center (PEER) seismic database, and the shaking table model test was conducted under horizontal unidirectional and bidirectional excitations. Results showed that the structural response of the curved bridge is sensitive to the ground motion of different site conditions. Spatial characteristics are observed in the main girder structural response of the curved bridge. When the curved bridge is parallel to the direction of the principal ground motion, the rotation effect of the main girder is greater than that perpendicular to the direction of the principal ground motion. The rotation effect of the main girder leads to evident beam end and bearing displacements at the low pier. The seismic excitation direction and pier height notably affect the displacement response of the pier, and the tangential displacement response of the fixed pier is sensitive to seismic excitation.

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Machine learning model for predicting structural response of RC slabs exposed to blast loading

Engineering Structures ◽

10.1016/j.engstruct.2020.111109 ◽

2020 ◽

Vol 221 ◽

pp. 111109

Author(s):

M.K. Almustafa ◽

M.L. Nehdi

Keyword(s):

Machine Learning ◽

Structural Response ◽

Blast Loading ◽

Learning Model ◽

Machine Learning Model ◽

Rc Slabs

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