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A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension
AbstractIn the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle loads, a spatio‐temporal correlation model of the girder strain and tension forces of all suspenders is constructed based on the combined application of stacked denoising autoencoder and convolutional neural networks‐long short‐term memory model, so as to realize the preliminary identification of suspenders tension. On this basis, by using the actual monitoring data of suspenders tension and the strain monitoring data obtained through the distributed optical fiber sensors, the delicate identification of tension forces of all suspenders is realized based on the error interpolation of preliminary identification results. The results of the example bridge show that the method in this paper can effectively identify tension forces of all suspenders of the suspension bridge, and identification results are more accurate than the method using only the monitoring data of suspenders.
A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension
AbstractIn the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle loads, a spatio‐temporal correlation model of the girder strain and tension forces of all suspenders is constructed based on the combined application of stacked denoising autoencoder and convolutional neural networks‐long short‐term memory model, so as to realize the preliminary identification of suspenders tension. On this basis, by using the actual monitoring data of suspenders tension and the strain monitoring data obtained through the distributed optical fiber sensors, the delicate identification of tension forces of all suspenders is realized based on the error interpolation of preliminary identification results. The results of the example bridge show that the method in this paper can effectively identify tension forces of all suspenders of the suspension bridge, and identification results are more accurate than the method using only the monitoring data of suspenders.
A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension
Computer aided Civil Eng
Xu, Qianen (author) / Gao, Qingfei (author) / Liu, Yang (author)
Computer-Aided Civil and Infrastructure Engineering ; 39 ; 1641-1658
2024-06-01
Article (Journal)
Electronic Resource
English
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