Papers by Author: Shou Ju Li

Abstract: Three-dimensional dynamic fracture process of rock under action of disc cutter is simulated by using nonlinear finite element method. Rock constitutive relationship is characterized by Drucker-Prager model. The influences of disc angular velocity and vertical force on the penetration depth and the specific energy are systematically investigated. The simulation results show that the rock fracturing process obviously presents properties of step crushing under action of disc cutter. The proposed numerical simulation presents an improvement on existing ones in terms of providing three-dimensional visual description of rock fracturing process.

Abstract: The inverse problem of parameter identification is deal with by minimizing an objective function that contains the difference between observed and calculated dam displacements. The optimization problem of minimizing objective function is solved with genetic algorithm. The calculated dam displacements are simulated by using finite element method according to water level change acting on dam upstream. The practical dam displacements are observed on the dam crest. The investigation shows that the forecasted dam displacements agree well with observed ones. The effectiveness of proposed inversion procedure is validated.

Abstract: Model identification of dynamic systems in the vibration engineering field has been followed with interest in recent years. A number of identification techniques on this topic are now available, such as parametric or non-parametric identification methods, time domain or frequency domain estimation approaches, etc. The identification approach of nonlinear constitutive model from input-output measurements is proposed. The inverse problem of material characterization is formulated as parameter identification problem that is solved by using optimization procedure. A set of parameters corresponding to the material property can be determined by minimizing objective function which accounts for experimental data and calculated responses of the mechanical model. The performances of the proposed identification approach were evaluated with simulating data. The effectiveness of identification approach is validated by numerical simulation. The investigation results show that the proposed identification algorithm poses good robustness and high identification precision.

Abstract: The inverse problem of structure damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Based on the hybrid optimization strategy, the parameter identification algorithm was presented according to the measured data of vibrating frequency and mode shapes in the damaged structure. The proposed algorithm combines the local optimum method having fast convergence ability with the neural networks having global optimum ability. By doing this, the local minimization problem of the local optimum method can be solved, and the convergence speed of the global optimum method can be improved. The investigation shows that to identify the location and magnitude of the damaged structure by using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.