Short-term tunnel-settlement prediction based on Bayesian wavelet: a probability analysis method

Journal of Zhejiang University-SCIENCE A Pub Date : 2023-11-01 DOI:10.1631/jzus.A2200599

Yang Ding, Xiaowei Ye, Zhi Ding, Gang Wei, Yunliang Cui, Zhen Han, Tao Jin

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Abstract

As urbanization accelerates, the metro has become an important means of transportation. Considering the safety problems caused by metro construction, ground settlement needs to be monitored and predicted regularly, especially when a new metro line crosses an existing one. In this paper, we propose a settlement-probability prediction model with a Bayesian emulator (BE) based on the Gaussian prior (GP), that is, a GPBE. In addition, considering the distortion characteristics of monitoring data, the data is denoised using wavelet decomposition (WD), so the final prediction model is WD-GPBE. In particular, the effects of different prediction ratios and moving windows on prediction performance are explored, and the optimal number of moving windows is determined. In addition, the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data. One year of settlement-monitoring data collected by a structural health monitoring (SHM) system installed on the Nanjing Metro is used to demonstrate the effectiveness of WD-GPBE and GPBE for predicting settlement.

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基于贝叶斯小波的短期隧道沉降预测：一种概率分析方法

随着城市化进程的加快，地铁已成为一种重要的交通工具。考虑到地铁建设带来的安全问题，需要对地面沉降进行定期监测和预测，尤其是当新地铁线与现有地铁线交叉时。本文提出了一种基于高斯先验（GP）的贝叶斯仿真器（BE）沉降概率预测模型，即 GPBE。此外，考虑到监测数据的失真特性，使用小波分解（WD）对数据进行去噪处理，因此最终的预测模型为 WD-GPBE。其中，探讨了不同预测比率和移动窗口对预测性能的影响，并确定了最佳移动窗口数。此外，还比较了基于原始数据的 GPBE 预测值和基于去噪数据的 WD-GPBE 预测值。南京地铁上安装的结构健康监测（SHM）系统收集了一年的沉降监测数据，用来证明 WD-GPBE 和 GPBE 预测沉降的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Journal of Zhejiang University-SCIENCE A

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