隧道监测中基于条件扩散的缺失数据补全方法

IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Wentao Zhu, Junchen Ye, Jinyan Feng, Tao Zou, Xuyan Tan, Haiquan Wang, Weizhong Chen
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引用次数: 0

摘要

在隧道结构健康监测(SHM)系统中,数据的完整性和准确性对于损伤检测和预警等任务至关重要。然而,环境干扰和传感器故障往往会导致大量的数据丢失,使得有效的数据输入成为关键的预处理步骤。传统的统计方法难以捕捉复杂的非线性时间和跨特征依赖关系,而自回归模型(如递归神经网络)则存在误差积累和难以适应实际隧道中动态变化的应变分布的问题。为了解决这些问题,本研究提出了一种基于扩散模型的非自回归插值框架,有效地减轻了误差积累。该模型有效地利用观测数据的信息内容来指导缺失值的建模和重建。引入门控时间特征自关注融合模块,准确捕捉结构响应的复杂时空依赖关系。此外,将温度和水位等外部环境变量集成到结构响应和运行条件的联合模型中,确保即使在恶劣的环境条件下,估算也保持稳健。在南京和武汉两个隧道的真实SHM数据集上验证了该方法的有效性。实验结果表明,该方法在不同缺失率下均具有良好的鲁棒性和性能,即使在严重的数据丢失情况下也能保持较高的准确性,证明了该方法在实际SHM应用中的有效性和实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Conditional Diffusion-Based Method for Missing Data Imputation in Tunnel Monitoring

A Conditional Diffusion-Based Method for Missing Data Imputation in Tunnel Monitoring

In tunnel structural health monitoring (SHM) systems, data completeness and accuracy are essential for tasks such as damage detection and early warning. However, environmental disturbances and sensor faults often cause significant missing data, making effective imputation a critical preprocessing step. Traditional statistical methods struggle to capture complex nonlinear temporal and cross-feature dependencies, while autoregressive models, such as recurrent neural networks, suffer from error accumulation and difficulty adapting to dynamically varying strain distributions in real tunnels. To address these challenges, this work proposes a novel nonautoregressive imputation framework based on diffusion models, which effectively mitigate error accumulation. The model effectively exploits the informative content of observed data to guide the modeling and reconstruction of missing values. A gated temporal-feature self-attention fusion module is introduced to accurately capture the complex temporal and spatial dependencies of structural responses. Additionally, external environmental variables such as temperature and water level are integrated to jointly model structural responses and operating conditions, ensuring that the imputation remains robust even under harsh environmental conditions. The method is validated on two real-world SHM datasets from tunnels in Nanjing and Wuhan with various missing data patterns. Experimental results show consistently robust and superior performance across different missing rates, maintaining high accuracy even under severe data loss, demonstrating its effectiveness and practical value in real SHM applications.

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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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