The improvement of bolted joints model via finite element model updating method

Efficient schemes to represent mathematical model of thin-sheet metal structures jointed by bolted joints for accurately predict the structure dynamic behaviour has been a significant unresolved issue in structural dynamics community. The biggest challenge is to efficiently incorporate the joints local deformation effects on the developed mathematical model via finite element (FE) method. Generally, the joints local deformation typically exerts on the joints mating area. To solve this issue, this paper proposes efficient schemes to represent mathematical model of thin-sheet metal structures jointed by bolted joints with application to accurately calculate the structure dynamic behaviour using FE model updating method. The initial FE model of the assembled structure was developed by employed Fastener Connector (CFAST) in MSC NASTRAN software to represent the bolted joints while, the inclusion of the local deformation effects at the bolted joints mating area was represented by contact elements. Then, the responses obtained from the FE model was evaluated by weight up with experimental data. FE model updating (FEMU) method then was utilised for minimising prediction discrepancies originated from the initial FE model based on the experimental data. The proposed scheme shows the accuracy of the initial prediction was improved from 25.03 % to 14.65 % while the accuracy of the predicted mode shapes via modal assurance criterion (MAC) analysis were above 0.8. Therefore, the findings offer useful schemes for improving the quality of predicted dynamic behaviour, particularly in the thin-sheet metal jointed structure and the developed model can be used with confident for any subsequence dynamic analyses.

Vision-Based Structural FE Model Updating Using Genetic Algorithm

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

The condition of joints in steel truss bridges is critical to railway operational safety. The available methods for the quantitative assessment of different types of joint damage are, however, very limited. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. A two-step identification procedure is developed to achieve damage localization and severity assessment. A series of FE models with single or multiple damages are simulated to generate the training and testing data samples and validate the effectiveness of the proposed approach. The influence of noise on the identification accuracy is also evaluated. The results show that the change rate of modal curvature (CRMC) can be used as a damage-sensitive input of the PNN and the accuracy of preliminary damage localization can exceed 90% when suitable training patterns are utilized. Damaged members can be localized in the correct substructure even with noise contamination. The FE model updating method used can effectively quantify the joint deterioration severity and is robust to noise.

Development of FE Model Updating for Three-Story Building considering Soil-Structure Interaction

Owing to the development of construction technology, structures are becoming increasingly taller. Furthermore, with the improvement in construction materials, the service life of the structures is also increasing. The increased service life of large structures has highlighted the importance of structure maintenance and performance evaluation; thus, the need for an accurate model development for performance evaluation is increasing. This study predicts the structural characteristics through finite element (FE) model updating using a genetic algorithm (GA). The GA was applied to determine whether the structural member was damaged. In particular, it is intended to improve the reliability of the FE model updating during a seismic load by considering the soil-structure interaction effect that has been overlooked in the existing model updating study. The results of this study show that the model that considers the soil-structure interaction can estimate the dynamic characteristics of the structure more accurately compared to the model that does not consider the soil-structure interaction. The accuracy of the updated parameters by the proposed method was found to be over 90%.