Design and implementation of machine learning-based anomaly detection in iron structures using synthetic data

This manuscript introduces a novel methodology for detecting anomalies in iron structures using synthetic data and machine learning algorithms. Synthetic datasets representing normal and anomalous conditions were generated through simulated gamma-ray interactions with iron. Decision tree and support vector machine (SVM)-based classifiers were employed to train a model capable of distinguishing between intact and defective materials. This data-driven approach provides a scalable and efficient platform for non-destructive testing across industries such as construction, transportation, and manufacturing.

In the future, we plan to integrate IoT devices into this framework to enhance its practical applicability. The manuscript presents the design and proposed methodology for machine learning-based anomaly detection in iron structures using synthetic data.