Condition Monitoring of Spud in Cutter Suction Dredger Using Physics Based Machine Learning

Dredging operation is happening at an increased rate due to the impetus gained towards inland navigation and land reclamation. The spud system is integral component of a Cutter suction dredger (CSD) which anchors the hull at the dredging location. The spud embedded on soil maintains the position of the dredger and offers resistance to the motion of the CSD. In this present study the spud was modeled as a Euler Bernoulli beam using finite element analysis. The resistance offered by the soil was evaluated experimentally. Soil stiffness is modeled using two spring elements which restraints the motion of the spud in the transverse and rotational direction. The spud system was subjected to external force due to wave loading. The wave load was determined along the length of the spud using Morison equation. Heave and pitch are the degrees of freedom of the dredge hull which were restraint by the spud. The ship rigid body dynamics was identified using the experimental study. The identified dredge hull was coupled to the spud. Numerical model of the spud system was subjected to random wave loading. A case study was carried out using Monte-Carlo simulation by varying the soil stiffness. The maximum response of the system was evaluated at the top of the spud. A meta model for the system was developed based on the maximum response at spud and soil stiffness using Gaussian process emulator (GPE). During the validation study it was observed that the meta-model was predicting the soil stiffness accurately. This indicates that the condition monitoring of the embedment of the spud can be assessed by the meta-model developed using GPE.