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Estimation of Cable Tension with Unknown Parameters Using Artificial Neural Networksas applied to identify tensions in cables of an existing bridge as a case study. Results showed thatthe suggested that suggested methodology is highly capable of identifying cable tension with unknown cable bending stiffness and uncertain boundary conditions.

Initial

亞當(dāng)心理陰影

Application of Artificial Neural Network for Recovering GPS—RTK Data in the Monitoring of Cable-Staye were extracted for study. The proposed method results are compared with actual monitoring data to evaluate the accuracy. These results indicate that GPS—RTK data supplemented by the ANN method completely ensure accuracy and reliability.

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Multilayer Perceptron Neural Network for Damage Identification Based on Dynamic Analysisealth of various structures. These methods are considered as efficient and reliable non-destructive techniques for damage detection of structures. In this study, a novel model is developed to predict damage severity in beam-like structures based on the finite element method (FEM) and multilayer perc

VERT

Optimization of Processing Parameters of Primary Phase Particle Size of Cooling Slope Process for Seriments have been carried out following central composite design. Three-key process variables at five different levels (pouring temperature, slope angle, and length of travel of the melt) have been considered for the present experimentation. Regression analysis and analysis of variance (ANOVA) have

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不如樂死去

Estimation of Cable Tension with Unknown Parameters Using Artificial Neural Networksble materials and complexity at the cable supports. This paper presents a method for vibration-based cable tension estimation using artificial neural networks (ANNs) regardless of the uncertainties of cable boundary conditions and unknown cable bending stiffness. Finite difference formulation of a d