Abstract
The investigation of the influence of soil-structure coupling on the vibration propagation pattern is the key to ensuring the reliability of the prediction of train-induced building vibration. This study selects different metro depots with over-track buildings as the research objects based on the existing engineering examples. It analyzes the general laws of vibration propagation from the ground soil to the building columns. The study provides valuable references for predicting vibration and designing vibration isolation measures for over-track buildings. Additionally, this study proposes a vibration prediction method for over-track buildings above metro depots based on the back propagation (BP) neural network model in machine learning. This model considers the soil-structure coupling loss in a data-driven manner based on the rich data samples, which avoids the problems of complex numerical simulation calculations and parameter uncertainties to some extent. This prediction method has a good balance of efficiency and convenience and also meets the accuracy requirements. It was judged to be a promising prediction method for the preliminary design stage of the over-track buildings above metro depots.
Data availability
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
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Funding
This research was supported by the National Natural Science Foundation of China (Grant No. 51908139), the Guangdong Basic and Applied Basic Research Foundation (2021A1515012605, 2022A1515010536), and Guangzhou Basic and Applied Basic Research Foundation (202201010215).
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ZH performed the measurement and was a major contributor in writing the manuscript. CZ analyzed and reviewed the measured data and paper. LT and JW performed the measurement. All authors read and approved the final manuscript.
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Hu, Z., Tian, L., Zou, C. et al. Train-induced vibration attenuation measurements and prediction from ground soil to building column. Environ Sci Pollut Res 30, 39076–39092 (2023). https://doi.org/10.1007/s11356-022-25061-7
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DOI: https://doi.org/10.1007/s11356-022-25061-7