Multiclass Supervised Machine Learning Algorithms Applied to Damage and Assessment Using Beam Dynamic Response

Purpose Structural damage can significantly alter a system's local flexibility, leading to undesirable displacements and vibra-
tions. Analysing the dynamic structure feature through statistical analysis enables us to discriminate the current structural
condition and predict its short- or long-term lifespan. By directly affecting the system's vibration, cracks and discontinui-
ties can be detected, and their severity quantified using the DI. Two damage indexes (DI) are used to build a dataset from
the beam's natural frequency and frequency response function (FRF) under both undamaged and damaged conditions, and
numerical and experimental tests provided the data-driven.
Methods In this paper, we present the methodology based on machine learning (ML) to monitor the structural integrity of a
beam-like structure. The performance of six ML algorithms, including k-nearest neighbors (kNN), Support Vector Machine
(SVM), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) are investigated.
Results The paper discusses the challenges of implementing each technique and assesses their performance in accurately
classifying the dataset and indicating the beam's integrity.
Conclusion The structural monitoring performed with the ML algorithm achieved excellent metrics when inputting the
simulation-generated dataset, up to 100%, and up to 95% having as input dataset provided from experimental tests. Demon-
strating that the ML algorithm could correctly classify the health condition of the structure.