AI-Enabled Structural Vibration Prediction for Enhanced Stealth Performance

Abstract: Increasing engineering complexity and the need for dependable structural health monitoring have created a demand for accurate and computationally efficient vibration-prediction methods. Conventional approaches—including physics-based models, extensive experimental testing across operating conditions, and computationally intensive simulations—can be difficult to apply when structures and service environments vary widely and decisions must be made under uncertainty. This study presents an AI-based framework for predicting structural vibration responses using regression algorithms. Five approaches—artificial neural network (ANN), k-nearest neighbours (KNN), decision tree (DT), gradient boosting regression (GBR), and random forest regression (RFR)—were evaluated using historical and simulated data representing material characteristics, stiffened geometries, and applied loading conditions. The comparison indicates that ensemble methods, particularly RFR and GBR, provide stronger predictive accuracy and generalisation than the other evaluated models. GBR delivered the best test performance, while RFR achieved a closely comparable result. ANN could represent nonlinear relationships but required additional computational effort and tuning, whereas KNN and DT were more sensitive to variation in the data. The findings indicate that AI-based regression can support faster preliminary assessment, reduce design-cycle time, and contribute to structural monitoring. This work demonstrates the potential of ensemble learning as a scalable approach to vibration prediction and as a foundation for data-driven infrastructure monitoring, proactive maintenance, anomaly detection, and improved structural safety.

Keywords: Artificial intelligence, vibration prediction, structural health monitoring, predictive models, data-driven methods, machine learning.