Displacement response estimation of a cable-stayed bridge subjected to various loading conditions with one-dimensional residual convolutional autoencoder method

Displacement is an essential indicator of the functioning and safety of long-span cable-supported bridges under operational conditions. However, displacement estimation is challenging as these bridges are simultaneously subjected to various loading conditions such as temperature, wind, and vehicles. This article investigates an approach for estimating bridge displacement responses under multiple loads using a residual autoencoder model. Monitoring data of a cable-stayed bridge are collected to validate the proposed approach, including comprehensive measurements of the various loads and the displacement responses. Characteristics of temperature, wind, and vehicle loads are taken as the input, and the displacement responses at the mid-span of the main girder and top of the two pylons are taken as the output. The results showed the effectiveness of the proposed approach with an accuracy higher than 95%, which clearly outperformed other models such as long short-term memory networks in accuracy and efficiency. The effects of different types of loads are also investigated, and the wind load is found to be the most influential. Furthermore, multistep ahead prediction is carried out using the proposed approach, and good accuracy is achieved even 5 min ahead. The proposed approach can shed light on early warning of the malfunction of the bridge.

Automated summary

This article investigates an approach for estimating bridge displacement responses under multiple loads using a residual autoencoder model. The proposed approach showed a high accuracy of over 95% and outperformed other models in accuracy and efficiency. Wind load was found to be the most influential factor.