A ReCon-BCALoD clustering algorithm for field monitoring data of marine structures

Abstract

Monitoring of the extreme marine environmental loads and hazardous dynamic responses of marine structures is critical to maintaining safe field operations. Field monitoring data from marine structures are high-dimensional with high aggregation and weak modes. Furthermore, monitoring data accumulates rapidly over time, which makes it difficult for conventional clustering algorithms to accurately identify the extreme environmental loads and hazardous response conditions experienced by marine structures during their service life. Cluster analysis is an unsupervised learning algorithm widely used in the fields of machine learning and information recognition. Currently, clustering algorithms are mainly used to calculate and analyze large data clusters, but few studies have focused on small clusters. Therefore, this study proposes a ReCon-BCALoD clustering algorithm suitable for long-term monitoring data of marine structures. The ReCon-BCALoD algorithm consists of up and down processes in which the data are clustered based on the ReCon local densities of data points. Applied to field monitoring data, the ReCon-BCALoD clustering algorithm proved capable of automatically determining cluster quantity and effectively identifying small data clusters. This showed that the ReCon-BCALoD clustering algorithm can be used to identify extreme current profile environment conditions and hazardous response conditions of a soft yoke mooring system. These clustering results can be used to improve the safety of in-service structures and provide valid references for future designs of marine structures.