Scour Propagation Rates around Offshore Pipelines Exposed to Currents by Applying Data-Driven Models

Offshore pipelines are occasionally exposed to scouring processes; detrimental impacts on their safety inevitable. The process of propagation around offshore is naturally complex and mainly due currents and/or waves. There a considerable demand for the safe design phenomena. Therefore, patterns must be focused on. In present research, machine learning (ML) models applied achieve equations prediction rate currents. approaching flow Froude number, ratio embedment depth pipeline diameter, Shields parameter, current angle attack were considered main dimensionless factors from reliable literature. ML developed based various setting parameters optimization strategies coming evolutionary classification contents. Moreover, explicit yielded used demonstrate how proposed approaches in harmony with experimental observations. performance was assessed utilizing statistical benchmarks. results revealed that given by provided physically consistent predictions rates regarding comparison tests.