Engineering

Geotechnical engineering

Subsea

Computer science

Geology

Oceanography

Group method of data handling

Electronic engineering

Seabed

Soil science

Sensitivity (control systems)

Artificial neural network

Machine learning

Cold Regions Science and Technology

Ice-induced scour is a serious challenge for the subsea pipelines in the Arctic shallow waters. Estimation of the maximum pipeline deformation and its minimum burial depth can guarantee the operational integrity of these structures in the ice-prone regions. The pipeline buried below the ice keel is still threatened by subgouge soil deformation that is extended down the ice tip due to the shear resistance of the seabed soil. Determining the subgouge soil deformations is a challenging process that needs costly experimental and numerical simulations. In this paper, an alternative and cost-effective methodology has been proposed using a robust neural network-based method titled “generalized structure of group method of data handling” (GS-GMDH) for the first time to simulate the horizontal and vertical subgouge soil deformation profiles in clay. Using the parameters governing the subgouge soil deformations, nine GS-GMDH models were defined. The premium GS-GMDH models and the most influencing input parameters comprising the soil depth and the gouge geometry were introduced by performing a sensitivity analysis. Subsequently, results of the best GS-GMDH models were compared with the classical group method of data handling (GMDH), the artificial neural network (ANN), and the empirical approaches. An uncertainty analysis showed that the GS-GMDH slightly overestimated the horizontal and underestimates the vertical subgouge soil deformations. A partial derivative sensitivity analysis (PDSA) was also performed to assess the influence of the input parameters on the subgouge soil deformations. Lastly, a set of GS-GMDH-based equations were proposed for fast estimation of the subgouge soil deformations in clay.

Ice-seabed interaction modeling in clay by using evolutionary design of generalized group method of data handling