In this paper, a two stage crack detection method has been proposed. In the first stage, extreme learning machine used to identify crack using three fist modes frequencies and mode shapes as input data to train machine. In the second stage, the obtained results in the f More
In this paper, a two stage crack detection method has been proposed. In the first stage, extreme learning machine used to identify crack using three fist modes frequencies and mode shapes as input data to train machine. In the second stage, the obtained results in the first stage, used as initial population in optimization procedure to detect crack locations and severities accurately. To demonstrate the potential of the proposed analysis over existing ones, a validation study has been done. To evaluate the performance of the presented method, a simply supported beam and a cantilever beam. The obtained results indicated that this method can provide a reliable tool to accurately identify cracks in beam structures
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In this paper a new method for crack detection in structures based on first three mode frequencies and modal strain energies using least square support vector machine has been proposed. Since the mode shape vectors are equivalent to nodal displacements of a vibrating st More
In this paper a new method for crack detection in structures based on first three mode frequencies and modal strain energies using least square support vector machine has been proposed. Since the mode shape vectors are equivalent to nodal displacements of a vibrating structure, therefore in each element of the structure strain energy is stored. The strain energy of a structure due to mode shape vector are usually referred to as modal strain energy (MSE) and can be considered as a valuable parameter for crack identification. Also, change of natural frequencies is effective, inexpensive, and fast tool for non-destructive testing. So, the proposed method uses the first three natural frequencies and modal strain energies as the input parameters and crack states as output to train the least squares support vector machine model.
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