Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Concepts and Applications of Finite Element Analysis
Concepts and Applications of Finite Element Analysis
Suitability of different neural networks in daily flow forecasting
Applied Soft Computing
Crack detection in beam-like structures using genetic algorithms
Applied Soft Computing
Locating the critical failure surface in a slope stability analysis by genetic algorithm
Applied Soft Computing
On the application of bees algorithm to the problem of crack detection of beam-type structures
Computers and Structures
Damage detection based on improved particle swarm optimization using vibration data
Applied Soft Computing
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Techniques for detecting elemental level damage using the traditional methods receive the setback because of the difficulties in formulating the problems mathematically, specially in case of inverse problems. Artificial neural networks (ANN) have been proved to be an effective alternative for solving the inverse problems because of the pattern-matching capability. But there is no specific recommendation on suitable design of network for different structures and generally the parameters are selected by trial and error, which restricts the approach context dependent. A hybrid neuro-genetic algorithm is proposed in order to automate the design of neural network for different type of structures. The neural network is trained considering the frequency and strain as input parameter and the location and amount of damage as output parameter. The performance is demonstrated using two test problems: (i) clamped-free beam and (ii) plane frame. The outcomes of the results are quite encouraging and prove the robustness of the proposed damage assessment algorithm.