e prestress loss is one of the main factors affecting the safety of prestressed concrete structure. While the detecting signals like sound and light are difficult to spread in steel strands, there is no effective method for prestress detection of the bonded prestressed steel strands in existing structures yet. In this paper, taking into consideration that the electromagnetic oscillation characteristic can make the signal propagate effectively on the bonded prestressed steel strands, a nondestructive prestress detection method based on the electromagnetic effect to detect oscillation frequency is proposed. In a detection circuit, the steel strands are simulated as an inductance component, in which an induced electromagnetic signal passes through the steel strands to form resonance. And then, a frequency meter is used to detect the oscillation frequency of the resonant circuit. e oscillation frequency is supposed to have relationship with the prestress loading on the steel strands. A section of steel strands with a length of 1.2 m is adopted to test the correlation of stress and oscillation frequency. Both the theoretical and experimental results show that the resonant frequency of the circuit decreases with the increase of the stress of the strand and is linear in a certain range.
As the damage diagnosis of bridge structure is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all of the independent variables, such as the structural and environmental properties, using conventional modeling techniques. In this study, a method was introduced for damage diagnosis of bridge structure by integration of BP artificial neural network (ANN) and information fusion based on D-S evidential theory. The basic probability assignment functions for data fusion were constructed according to the demand of the damage diagnosis and the real conditions of the bridge structure. And an application example of the proposed method was demonstrated. The results showed that the integration of the two strategy can remove the shortcoming of BP ANN with remaining of its advantages and promote the identified veracity of the whole diagnosis system.
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