Vibration Characteristics of Alumina–Steel Axially Functionally Graded Fluid-Conveying Pipes: A Physics-Based GITT and MLP Surrogate Study

The vibration characteristics of clamped–clamped Alumina–Steel axially functionally graded (AFG) fluid-conveying Timoshenko pipes are investigated using a physics-based generalized integral transform technique (GITT) benchmark and a multi-layer perceptron (MLP) surrogate trained on GITT data. Parametric GITT sweeps over the power-law gradation index k, dimensionless flow velocity u, and aspect ratio L/D quantify how axial material gradation controls the first two natural frequencies (ω1, ω2) and the maximum vibration deflection (yM): increasing k reduces ω1 and ω2; on u-sweeps at L/D=50, larger k also increases yM and lowers the critical flow velocity, whereas on L/D-sweeps at u=3.0, yM decreases with k. A feedforward MLP surrogate fitted to Ns=336 GITT samples via an interior block-wise train–test split and three independent networks with output-specific preprocessing achieves R2>0.99 on held-out data, with maximum relative errors below 9%, and reproduces representative GITT parametric curves in overlay validation. After one-time offline training, MLP inference is orders of magnitude faster than online GITT runs, enabling large-scale global sensitivity analysis based on Sobol indices, SHAP values, and partial dependence plots; these identify u as the dominant influence on the modal responses, while SHAP ranks k first for ω2. The physics-based GITT and MLP surrogate workflow combines high-fidelity material–structure benchmarking with efficient metamodeling for design optimization, reliability assessment, and sensitivity-driven screening of Alumina–Steel AFG fluid-conveying pipes.