摘 要
随着桥梁建设规模的不断扩大和服役年限的增长，确保桥梁结构安全成为亟待解决的重要问题，为此设计有效的桥梁健康监测系统并研究其数据分析方法具有重要意义。本研究旨在构建一套完整的桥梁健康监测系统，以实现对桥梁结构状态的实时、准确监测，并探索高效的数据分析方法。基于传感器网络技术搭建监测系统框架，选用高精度传感器采集桥梁在不同工况下的振动、应力等数据，利用无线传输技术将数据传送至数据中心。采用小波变换与经验模态分解相结合的方法对原始数据进行预处理，去除噪声干扰，提高数据质量。通过建立有限元模型模拟桥梁结构响应，结合实测数据进行对比分析，验证了系统的有效性。提出基于机器学习算法的状态识别模型，实现了对桥梁结构损伤的有效识别，该模型相较于传统方法具有更高的准确性和鲁棒性。研究表明，所设计的桥梁健康监测系统能够稳定运行，为桥梁的安全评估提供了可靠依据，所提出的数据分析方法提高了桥梁结构状态识别的准确性，为桥梁养护管理提供了新的思路和技术手段，对保障桥梁安全运营具有重要价值。
关键词：桥梁健康监测系统；数据分析方法；传感器网络；小波变换与经验模态分解；机器学习算法状态识别
Abstract
With the continuous expansion of bridge construction scale and the increase in service life, ensuring the safety of bridge structures has become an urgent issue that needs to be addressed. Therefore, designing an effective bridge health monitoring system and studying its data analysis methods are of great significance. This study aims to establish a comprehensive bridge health monitoring system for real-time and accurate monitoring of bridge structural conditions and to explore efficient data analysis methods. The monitoring system fr amework is constructed based on sensor network technology, employing high-precision sensors to collect vibration, stress, and other data under various operating conditions of the bridge. Wireless transmission technology is utilized to send the collected data to the data center. For preprocessing the raw data, a combined method of wavelet transform and empirical mode decomposition is adopted to remove noise interference and enhance data quality. A finite element model is established to simulate the structural response of the bridge, and the measured data is compared and analyzed to verify the effectiveness of the system. A condition recognition model based on machine learning algorithms is proposed, achieving effective identification of structural damage in bridges. Compared with traditional methods, this model exhibits higher accuracy and robustness. The research demonstrates that the designed bridge health monitoring system can operate stably, providing reliable evidence for bridge safety assessment. The proposed data analysis methods improve the accuracy of bridge structural condition recognition, offering new ideas and technical means for bridge maintenance management and contributing significantly to ensuring the safe operation of bridges.
Keywords：Bridge Health Monitoring System；Data Analysis Methods；Sensor Network；Wavelet Transform And Empirical Mode Decomposition；Machine Learning Algorithms State Recognition
目 录
摘 要 I
Abstract II
引 言 1
第一章 桥梁健康监测系统概述 2
1.1 桥梁健康监测的重要性 2
1.2 监测系统的构成要素 2
1.3 国内外研究现状分析 3
第二章 监测系统设计关键要素 5
2.1 传感器网络布局优化 5
2.2 数据采集与传输方案 5
2.3 系统可靠性保障措施 6
第三章 数据分析方法与技术 8
3.1 原始数据预处理方法 8
3.2 特征提取与模式识别 8
3.3 结构状态评估模型 9
第四章 实时监测与预警机制 11
4.1 实时数据处理流程 11
4.2 异常检测与诊断算法 11
4.3 预警阈值设定原则 12
结 论 14
参考文献 15
致 谢 16
