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预应力混凝土桥梁在使用年限增加和车辆荷载长期作用下会出现预应力损失和刚度变化,从而影响桥梁结构的健康状态。已有研究成果多数根据结构弹性模量的变化判定桥梁健康状态,该文采用小波神经网络方法进行预应力混凝土梁桥的预应力损失和刚度变化预测。预应力损失预测结果表明:成桥后1~8年,预应力预测值由初始预应力的88.54%降为87.1%,根据JTG D62-2004《公路钢筋混凝土及预应力混凝土桥涵设计规范》所给公式计算的预应力值由初始预应力的88.25%降为86.3%,最大误差为0.8%。刚度损失预测结果表明:小波神经网络方法预测出的弹性模量损失规律与清华大学李秀芬公式和北京交通大学钟铭公式计算的弹性模量损失规律一致,而且该方法可以同时考虑预应力和疲劳两个因素对桥梁弹性模量的影响,较为符合桥梁实际状态。
The prestressed concrete bridges will have the loss of prestress and the change of stiffness under the long service life and long-term load of the vehicle, which will affect the health status of the bridge structure. The existing research results mostly determine the bridge health status according to the change of structural elastic modulus. In this paper, the prediction of prestress loss and stiffness variation of prestressed concrete girder bridges is predicted by wavelet neural network. The prediction results of prestress loss show that the predicted value of prestress decreases from 88.54% of the initial prestress to 87.1% 1 to 8 years after bridge completion. According to JTG D62-2004 “Code for Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts” The formula calculates the prestress value from the initial prestress 88.25% to 86.3%, the maximum error of 0.8%. The results of stiffness loss prediction show that the law of loss of elastic modulus predicted by wavelet neural network is consistent with the law of elastic modulus loss calculated by Li Xiu-fen formula of Tsinghua University and Zhong Ming formula of Beijing Jiaotong University. Moreover, this method can consider both prestress and fatigue The influence of the factors on the elastic modulus of the bridge is more in line with the actual state of the bridge.