Crack modeling and identification in curved beams using differential evolution

Natural frequencies of parabolic arches with a single crack on opposite cross-section sides

“…We express these as in Eroglu and Tufekci (2017), and account also for the location of the crack on opposite sides of the cross-section (top or bottom with respect to the center of curvature).…”

“…The same problem was studied also by Capecchi et al (2016), based on natural frequencies, mode shapes, and curvatures, following previous proposals (Ciambella and Vestroni, 2015;Ciambella et al, 2017). Eroglu and Tufekci (2017) used the coupling of bending and axial strains due to a small local crack to check the effects of its location in the cross-section on the natural frequencies of plane arches and to identify this location, in addition to its position on the beam axis and its magnitude. Crack identification in parabolic arches via static behavior is reported by Greco and Pau (2011): they point out that the dynamic response provides better structural damage identification than the static response.…”

Vibration of locally cracked pre-loaded parabolic arches

“…Zare [38] performed an experimental modal analysis on cracked circular specimens for comparison with the results of the differential quadrature method, in which the crack is modelled by a rotational spring. Evolutionary algorithms are used by Greco et al [39], and Eroglu and Tüfekci [40] for damage identification in curved beams. Most researchers performed such studies dividing the beam in two regular chunks joined by a rotation spring, the stiffness of which is determined either directly by reduction of inertia of the cross-section or by the concepts of fracture mechanics.…”

“…Eroglu and Tüfekci [40] highlighted that it may be possible to find crack location on the cross-section by introducing a non-material parameter linked to couplings. These were further investigated by the same approach in Eroglu et al [41] for parabolic arches; it is found that neglecting the coupling due to crack may be misleading in identification problems, especially for shallow arches.…”

Multi‐objective comprehensive teaching algorithm for multi‐objective optimisation design of permanent magnet synchronous motor

“…In combination with natural optimisation algorithms (OAs) in the optimisation process, it can effectively improve the optimisation efficiency, which has become one of the current research directions of MO. Using natural OA [7–12] for MO design of PMSM can improve the efficiency of optimisation design and complete complex optimisation processes, and solve the PMSM MO problem better. For example, Taguchi method [5, 6], genetic algorithm (GA) [13–17], particle swarm algorithm (PSO) [18–20], controlled random search algorithm [21], artificial bee colony (ABC) [22], differential evolution (DE) algorithm [23, 24] and non‐dominated sorting GA‐II (NSGA‐II) [25].…”