One of the primary challenges associated with bolts, frequently used in assembly structures and systems, is the issue of torque loosening. This problem can arise due to shock and vibration, potentially leading to significant damage and structural failure. The difficulty in identifying and monitoring torque loosening arises from the variability and nonlinear effects present in bolted joints. In this paper, we proposed a machine-learning algorithm architecture designed for pattern recognition, detection, and quantification of torque loosening in bolted joints. This approach combines unsupervised and supervised machine learning algorithms to address the challenges of assessing the bolt torque loosening issue. Our algorithm utilises a damage index, calculated from the frequency response of the jointed system using the Frequency Response Assurance Criterion, as input data for the unsupervised–supervised classification algorithm. This classification ML algorithm effectively identifies and categorises instances of torque loss by analysing indirect vibration measurements, even in situations where the bolted system’s state is unknown. Additionally, we introduce a regression algorithm to quantify torque loosening levels. The results obtained from our proposed machine learning algorithm to overcome torque loosening in bolted joints show that the inherent uncertainties of a data-driven approach intrinsically influence torque-related issues. This assessment is based on experimental raw data collected under diverse test conditions for the bolted structure. We employ a range of validation and cross-validation metrics to evaluate the effectiveness and accuracy of these ML algorithms in detecting and diagnosing torque-related issues. These metrics play a crucial role in assessing the algorithms’ efficiency and precision in determining the state of the bolted connection and the corresponding torque levels with associated uncertainty quantification.

Purpose: Structural damage can significantly alter a system's local flexibility, leading to undesirable displacements and vibrations. 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 discontinuities 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. Demonstrating that the ML algorithm could correctly classify the health condition of the structure.