###### Abstract
An enhanced substructure-oriented response sensitivity method was proposed for the health monitoring of a large-scale structure for accurate estimation of damage [21]. However, directly or indirectly, all the above-mentioned techniques focus on the reduction of the number of variables or accurate estimation of the damage-sensitive parameters. Machine learning (ML) algorithms have also recently been found to be applied in the field of SHM and damage detection. The model updating technique considers a reference model and updates to represent a damaged structure to determine the damage-sensitive parameters. However, ML is a data/image-based technique which builds a model for mapping input patterns to output targets [22]. [PERSON] and [PERSON] [23] utilized the Bag of Visual Words based scalogram image classification approach to classify dis-bonds at different locations of a composite plate structure. To generate a large number of datasets, they carried out multiple experimental trials for each of the healthy and damaged cases. In order to test the learning algorithm, data were collected from a smaller size disbond level. However, their study was not able to give an idea about the disbond sizes. [PERSON] and [PERSON] [24] utilized the convolutional neural network (CNN) technique for the inspection of post-hazard ultra-low cycle fatigue-related damage in structural steel fuse members. They defined a micromechanical damage index for various levels of damage to quantitatively cluster the data. In their study, they considered five classes of damage levels. In [PERSON] et al. [25], an Acoustic Emission (AE) signal-based ML algorithm was trained for the monitoring of a carbon fiber composite panel using various statistical features and the k nearest neighbor (KNN) algorithm. A Naive Bayes data fusion scheme and CNN-based framework were proposed by [PERSON] and [PERSON] [26] for the crack detection of a metallic structure. Time-varying damage index-based features were fed into a CNN algorithm to localize the damages in a plate type of structure by [PERSON] et al. [27]. A few more applications of the ML technique in SHM are presented in [28; 29; 30; 31; 32; 33]. Again, inside Tsinghua University, post-seismic event-based damage assessment for 619 buildings was carried out based on CNN using the time-frequency domain representation of acceleration ground motion [34]. A combination of a probabilistic neural network and finite element model updating was found to assess the condition of a truss bridge structure [35]. Image-based deep learning and graphical model is used for the detection of the loosening of bolts [36]. Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames has also been found to be available in the domain of SHM under different EQ excitation. Damages were created by the loosening of bolts of the bracing members [37]. The application of ML is found to be increasing in recent times as automation of SHM is a felt need. However, studies show that for accurate prediction of damage severity and location, a large number of damage classes and corresponding training data sets are required that are somewhat impractical for civil engineering structures. In this paper, a novel vibration data-based hybrid ML and FE model updating method for the accurate estimation of joint damage and its severity is presented. The method is initially applied on an experimental small-scale single-story bolted joint planer frame model, and further extended to a four-story pseudo numerical model of a steel planer frame structure. The study is carried out to address the following objectives:  
1. The development of a combined ML and FE model updating technique for the health monitoring of joints of planer frame structures;
2. The accurate localization and quantification of joint damage with a lesser number of data sets compared to the data sets required if only either the ML-based technique or model updating technique is employed for the same purpose;
3.