Open AccessModel Updating of Frame Structure with Bolted Joints
Author(s) -
M.H.N. Izham,
M. S. M. Sani
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1262/1/012023
Subject(s) - finite element method , bolted joint , structural engineering , frame (networking) , modal analysis , sensitivity (control systems) , modal , joint (building) , experimental data , process (computing) , static analysis , engineering , computer science , mathematics , mechanical engineering , materials science , statistics , electronic engineering , polymer chemistry , operating system
Experimental analysis is often viewed as an important source of reference compared to finite element due to the accuracy of giving reliable data. Currently, finite element analysis is widely used as an early adoption in development, hence trusting the finite element data is crucial for the user. Finite element analysis often shows discrepancies to the test result. The complexity of the joints (bolted) might cause the discrepancies to occur. This study aims to reduce the discrepancies between the experimental and numerical analysis on a frame structure with bolted joints by model updating. Model updating is a process of making adjustment to certain parameters of finite element model to reduce discrepancy between analytical predictions of finite element analysis (FEA) and experimental results. Modal properties (natural frequencies, mode shapes, and damping ratio) of a frame structure with bolted joints are determined using both experimental modal analysis (EMA) and finite element analysis (FEA). Both data obtained is correlated before optimising the properties with sensitivity analysis. Joint strategy of this paper is focusing on RBE2, CBAR and CELAS element. CELAS was selected to represent the bolted modelling due to its lowest percentage average of 2.03% compares to CBAR 6.55% or RBE2 3.56%. Selected parameters were identified by performing a sensitivity analysis and the discrepancies was reduced by performing model updating procedure.