Papers by Author: Zi Chang Shangguan

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.

Abstract: The prediction procedures of the residual stresses in welding process were presented by using finite element techniques. Owing to localized heating by the welding process and subsequent rapid cooling, the residual stresses can arise in the weld itself and in the base metals. The bilinear elastic-plastic material model based on Von Mises yield criterion was developed. The material nonlinearity of weldment and welding fluid was dealt with using an incremental technique. Inside each step, the Newton-Raphson iteration method was utilized. A fully coupled thermo-mechanical twodimensional analysis was performed with finite element method. The model applied in this study adopts the technique of element birth and death to simulate the weld filler variation with time in multi-pass welded joints. The effects of welding speed on residual stresses are discussed.

Abstract: Freezing and thawing damage is one of the major problems of concrete dams in cold climate. Cracking and splitting are the most common results of freezing and thawing deterioration in concrete dam. The cracking problem owing to freezing and thawing was investigated by making sue of finite element methods. The interpretation of the mechanism of failure was also given. In order to compute the thermal stress fields of concrete dam caused by freezing and thawing, the temperature changes versus seasons is determined according to measured data. The temperature fields of concrete dam versus seasons are simulated by using finite element method. Basing on the computational results of the temperature fields of concrete dam, the thermal stress fields are calculated numerically. The researches show that the first principal stress of concrete dam at downstream surface can exceed the tensile strength of concrete material. The numerical simulation results of fractured regions of concrete dam agreed with practical observed data.

Abstract: The inverse problem of rock damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Convergence measurements of displacements at a few of positions are used to determine the location and magnitude of the damaged rock in the excavation disturbed zones. Unlike the classical optimum methods, ANN is able to globally converge. However, the most frequently used Back-Propagation neural networks have a set of problems: dependence on initial parameters, long training time, lack of problemindependent way to choose appropriate network topology and incomprehensive nature of ANNs. To identify the location and magnitude of the damaged rock 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.