Muhammad Umair

The interactions between subsea pipelines and the seabed generate complex vibratory responses that can compromise structural integrity, yet these interactions remain difficult to characterize with sufficient fidelity for design purposes. This study examines a numerical workflow for incorporating hydrodynamic forces and displacement-dependent pipe-soil interaction into the finite element analysis of a nominally 400 m subsea pipeline. The hydrodynamic formulation includes irregular wave kinematics, directional spreading, current effects, and force corrections related to pipeline movement, while the geotechnical representation uses prescribed pipe-soil contact and lateral resistance relationships. The workflow links local resistance characterization with the global structural response and investigates the sensitivity of calculated lateral displacement to applied stabilization weight and seabed strength assumptions. This approach enables computationally efficient structural analysis without sacrificing the fidelity needed to assess whole-system performance, directly addressing the central modeling challenge posed by the pipe-soil interface. Over a 100-year simulated service life, the maximum lateral displacement recorded across the 400 m pipeline reached 5.43 m, while displacement peaked at approximately 3.98 m under a chain weight of 18.37 kg. These results demonstrate the model’s sensitivity to key design parameters governing pipeline stability. The analysis provides an implementation-based case study for assessing the consequences of alternative seabed-resistance assumptions rather than establishing a new soil constitutive model or an independently validated through-life design procedure, and it provides a robust foundation for future subsea pipeline engineering practices.

– The supermarket industry loses potential sales because of a lack of insight into customer demands and sales. Modern technologies allow supermarkets to improve their sales performance. Approaches such as customer traffic flow, customer-product engagement, optimized layout, and real-time analysis and recommendations help supermarkets cover lost sales. Existing studies fall short in providing a comprehensive framework that helps supermarkets boost their sales. This study introduces a novel framework that employs Deep Learning, Computer Vision, Generative AI, and LLMs to deliver an end-to-end solution. These technologies help supermarkets in customer detection and tracking, identify heat grids for customer flow, and customer-product engagements by utilizing generated heat maps, real-time analysis, and recommendations, and planograms for optimized layout, all of which contribute to helping supermarkets improve their sales performance. In addition, the framework opens the door for upcoming advancements by including additional features like multi-store optimization in further enhancements to increase supermarket sales.