###### Abstract  
This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model is trained with the discriminative features obtained from time history data, and those features are used to distinguish between damaged and undamaged joints. An FE model of the planer frame is considered where the fixity factor (FF) of a joint is modeled with rational springs and the FF of the spring is assumed as the severity level of loosening bolts. The Cat Swarm Optimization technique is further applied to update the FE model to calculate the fixity factors of damaged joints. Initially, the method is applied to a laboratory-based experimental model of a single-story planer frame structure and later extended to a pseudo-numerical four-story planer frame structure. The results show that the method successfully localizes the damaged joints and estimates their fixity factors. structural health monitoring; machine learning; model updating; steel frame; loosening of bolts; Cat Swarm Optimization Article  
A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data. 1  
[PERSON]; [PERSON]; [PERSON]; [PERSON] A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data. Applid  
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the health monitoring of a space frame structure to the set-up of the GA parameters and operators. Various objective functions were examined for accuracy [12]. A multi-stage and multi-objective optimization was also found to be applied in the field of SHM of civil engineering structures [13; 14]. A vision-based FE model updating technique was also applied to the SHM of a three-storey building frame structure, which was found to reduce cost and time [15]. However, the success of the technique depends on multiple factors, namely, the accuracy of the finite element (FE) model, the quality of experimental data, the optimization algorithm, and the definition of the optimization algorithm. In FE-based model updating, FE models of the physical systems are developed and updated to achieve a good agreement with experimental data. In the process, a detailed FE model with a large number of elements and several updating parameters is required to recognize the effect of localized damage. However, these may increase the computational cost as well as may make the solution ill-conditioned. [PERSON] et al. [16] presented a parameterization method, namely, a damage function for reducing the number of updating variables. The technique assumes correction factors for updating parameters that change continuously over the FE modeling. The technique was applied to an RCC beam [16]. A multi-stage damage detection technique was proposed by [PERSON] and [PERSON] [17] for the monitoring of a large-scale structure. In the primary stage, expected damage points were approximated and in the next stage, the exact location of the damage and severity was estimated. [PERSON] and [PERSON] [18] also proposed modal strain energy in combination with FE model updating for the health monitoring of joints of a planer frame structure to identify accurate damage location and quantity. [PERSON] and [PERSON] [19] proposed a two-step FE model updating. The technique was applied to the ASC benchmark building, where, in the first stage, connection stiffness and young's modulus were identified and then applying the FE model updating, the estimation of damaged braces was carried out [19]. [PERSON] and [PERSON] [20] developed a progressive damage identification strategy depending on a multiple-scale wavelet FE model that was successfully examined on a numerical model of a beam and a frame.