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Abstract. Mechanical joints, particularly fasteners such as bolted joints have a complex non-linear behaviour. The non-linearity might emerge from the material, geometry or by the contacts in the joints. However, damage to a structure can be happened either their connections or the material of components. The effect of damage can change the dynamic properties of the structure such as natural frequencies and mode shapes and structural performance and can cause premature failure to structure. This paper presents a damage detection method using a vibration based damage detection method based on the frequency response function (FRF) data. A combination of numerical model and physical bolted jointed structure of damaged and undamaged structure will be investigated. The validation is employed to detect the presence of damage in the structure based on the frequency response function (FRF) data from the parameter values used in the benchmark model and damaged model. The comparisons of the undamaged and damaged structure of the FRF have revealed the damaged structure was shifted from the undamaged structure. The effect of the FRF between undamaged and damaged structure is clearly affected by the reduction of stiffness for the damaged structure.

Nonlinear problem is always occur in slender structures that are usually characterized by large displacements and rotations but small strains. Linear design assumption could lead to premature failure if the structure behaves nonlinearly. In this paper, the static displacement of a slender beam subjected to point load is investigated numerically by incorporating the large amplitude of the displacement. Two types of numerical analyses are performed at a full-scale finite element model which is linear static and geometric nonlinear implicit static. the results of the FEA linear static analysis are compared with the results from the FEA geometric nonlinear implicit static analysis. It shows that very high different load-displacement value response. Experimental static displacement test has been performed to validate both numerical results.

This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is 'leaching'. The pronunciation of the word 'leaching' in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal's features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words.