Toward AI-Assisted Conceptual Design of Subsea Inline Structures: Classification and Strain Behaviour of Inline Components during S-Lay Installation
S-lay installation remains the dominant method for offshore pipeline deployment, with inline components — valves, flanges, Inline Tee (ILT) assemblies, and Pipeline End Terminations (PLETs) — increasingly integrated into the continuous pipeline string. As these structures traverse the stinger, the displacement-controlled overbend condition induces strains in the adjacent pipeline that exceed those in plain pipe, a complex and highly localized design condition unique to such subsea assemblies. For a given piping layout, many structural configurations are possible, and identifying the one that minimizes installation strain is a non-trivial search problem that fast-track project schedules rarely allow engineers to conduct systematically; designs are instead adapted from past projects using individual pattern recognition rather than a structured body of design knowledge. This paper is the first in a series developing an AI-assisted conceptual engineering framework for subsea inline structures, using the S-Lay ILT design problem as its case study. It introduces a dual classification system — a physical taxonomy (IW/EA) describing how components connect to the pipeline, and a mechanical taxonomy (Types A, B, C) describing their strain-amplification behaviour — and characterizes each behaviour type through parametric finite element analyses incorporating stinger–pipeline contact modelling. Results identify offset depth and component length as the dominant strain drivers for elevation- and stiffness-type components respectively, quantify the DNV-compliant range for taper transitions, and show that very long components can paradoxically reduce peak strain through dual-roller contact. Together, these findings constitute the design intuition — Steps 1 and 2 of the proposed seven-step framework — needed to support AI-assisted conceptual design of subsea structural assemblies