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Introduction: In recent years, the seismic vulnerability of structures in Malaysia has attracted the attention of researchers mainly because the majority of existing structures have not been designed for seismic actions. In this study, seismic vulnerability of tall concrete wall buildings has been investigated through the development of seismic fragility curves. Methods: Two 25-story tall buildings with similar plans but with the different number of parking levels were analyzed through the incremental dynamic analysis. The tall buildings were excited by 15 far-field earthquakes, and their inter-story drift demands and capacities were estimated. Nonlinear response of beams and columns was simulated through the lumped plasticity model. The inelastic response of concrete walls was taken into account through the use of distributed plasticity fibre-based elements. Results and Conclusion: The obtained results indicated that the probability of exceeding minor damage to the tall concrete wall buildings located in the Kuala Lumpur city was around 55%. However, the probability of collapse of these structures in the same city was less than 15%.

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In this study, a neuro-wavelet technique was proposed for damage identification of cantilever structure. At first, damage localisation was accomplished through mode shape decomposition using discrete wavelet transforms. Subsequently, a damage indicator was defined based on the detail coefficients of the decomposed signals. It was found that distinct patterns relate the damage indicators to damage locations. Considering this property, a neural network ensemble was developed for damage quantification. Damage indicators and damage locations were selected as input parameters for the neural networks. Three individual neural networks were trained by input samples obtained from different combinations of decomposed mode shapes. Then, the outcomes of the individual neural networks were fed to the ensemble neural network for damage quantification. The proposed method was tested on a cantilever structure both experimentally and numerically. Six different damage scenarios including three different damage locations and three different damage severities were introduced to the structure. The results revealed that the proposed method was able to quantify different damage levels with a good precision.

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