In principal, a proper analysis of the dynamic response of a structure can provide general indicators of its operational conditions. When the dynamic response changes due to a variation of the physical properties of a structure, then one may conclude that some kind of damages has occurred. This paper presents investigation of the robustness and comparison of four simple methodologies to both identify and quantify the damages in structures, based on the use of Frequency Response Functions (FRF) signals, Principal Component Analysis technique (PCA) and Transmissibility. A steel beam with constant rectangular crosssection is used to compare the proposed approaches. At first, nine damaged scenarios are created and for each of them numerical examples are discussed; a database of FRFs is measured using modal testing. Then, PCA theory is applied to the FRF matrix and global damage detection and quantification indices are defined by using the first 3 Principal Components; Hotelling's T-squared distribution is also applied and by using transmissibility two other indicators, Transmissibility Damage Indicator and Weighted Damage Indicator, are computed for the assessment of damage. The reported examples show that all proposed methods are able to detect and quantify damages at the initial stage.
In the present research, two stress-based failure criteria were proposed to predict brittle fracture in components containing V-notches with end holes (VO-notches) under mixed mode I/II loading. The first criterion, called VO-MTS, was an extension of the maximum tangential stress (MTS) criterion, and the second one, called VO-MS, was developed based on the mean stress (MS) failure concept. Two different groups of critical distances were utilized in the predictions. The first group was equal to the critical distances for sharp crack, and the second one was computed by using the mode I fracture test results on notched specimens. To verify the criteria, the theoretical fracture curves were compared with numerous experimental results gathered from 108 new brittle fracture tests performed on the Brazilian disk specimens weakened by central V-notches with end holes and made of PMMA under mode I and mixed mode I/II loadings. It was found that both the criteria provide very good predictions to the experimental results for different mode mixity ratios. Also, found in this research was that the curves are almost independent of the critical distance groups, meaning that one can simply utilize the critical distances of sharp crack in both the VO-MTS and the VO-MS criteria without requiring performing mode I VO-notch fracture experiments.
List of symbolsCTSN Compact-tension-shear-notch CZM Cohesive zone model d Total slit length in the VO-BD specimen D Diameter of the VO-BD specimen d c Critical distance of the MS criterion for sharp cracks d c,vo Critical distance of the MS criterion for VO-notch E Young's modulus ERNFT Effective relative notch fracture toughness FE Finite element FFM Finite fracture mechanics FNR Fictitious notch radius K vo eff Effective relative notch fracture toughness K IC Plane-strain fracture toughness of material K vo I Mode I notch stress intensity factor
One of the challenging problems in the case of aircraft failure is to determine the new altered dynamics of the impaired aircraft. Among various methods, neural networks and neuro-fuzzy systems can be used for high-fidelity modeling of the aircraft nonlinear dynamics with the aim of onboard applications in real time. However, the method with better generalization capability is more preferred specifically in the case of unpredicted aircraft failures. Generalization of a network is mainly dependent on the network's parameters, the employed training algorithm, and the amount of training data. In this paper, several neural networks and local model networks are trained using different training algorithms and different amounts of training data to model the nonlinear dynamics of an impaired aircraft with the damaged rudder. These networks are compared based on their generalizations to the new cases of rudder failure. The effect of using different amounts of training data on the generalization capability and performance of the networks has also been investigated. The results of this paper show that both network types have good performance but neural networks generalize better to the new failure cases than local model networks. Also based on the obtained results, a significant reduction in the number of training samples could be accomplished without a considerable decrease in the network's performance and generalization. Finally, a neural network-based sensitivity analysis method is proposed which utilizes the network's regression equation as an emulator for fast model evaluations and can be used as an advisory tool for choosing safer path planning strategies.
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