摘 要
随着城市化进程的加快，大型公共建筑的数量与规模持续增长，其结构安全问题日益受到关注，而传统的检测手段已难以满足高效、精准的需求。为此，本研究聚焦于结构健康监测技术在大型公共建筑中的应用，旨在通过先进的传感与数据分析方法提升建筑全生命周期的安全管理水平。研究采用多源传感器融合技术，结合机器学习算法对建筑结构的动态响应进行实时监测与评估，并提出了一种基于深度学习的异常识别模型，以提高故障诊断的准确性与效率。通过对某实际大型公共建筑的长期监测数据进行分析，验证了所提方法的有效性，结果表明该技术能够准确捕捉结构性能退化特征并及时预警潜在风险。本研究的创新点在于将深度学习引入结构健康监测领域，显著提升了复杂环境下数据处理的能力，为大型公共建筑的安全运维提供了新思路和技术支撑，具有重要的工程应用价值和推广前景。
关键词：结构健康监测; 深度学习; 多源传感器融合; 异常识别; 大型公共建筑
Abstract
With the acceleration of urbanization, the number and scale of large public buildings continue to grow, and concerns about their structural safety are increasing. Traditional inspection methods are no longer sufficient to meet the demands for efficiency and precision. In response, this study focuses on the application of structural health monitoring technology in large public buildings, aiming to enhance safety management throughout the building lifecycle through advanced sensing and data analysis techniques. A multi-source sensor fusion approach is employed, combined with machine learning algorithms for real-time monitoring and evaluation of the dynamic responses of building structures. Furthermore, a deep-learning-based anomaly detection model is proposed to improve the accuracy and efficiency of fault diagnosis. The effectiveness of the proposed method is validated through the analysis of long-term monitoring data from an actual large public building. Results indicate that the technology can accurately capture characteristics of structural performance degradation and provide timely warnings of potential risks. The innovation of this study lies in the integration of deep learning into the field of structural health monitoring, which significantly enhances data processing capabilities in complex environments. This provides new insights and technical support for the safe operation and maintenance of large public buildings, demonstrating important engineering application value and broad prospects for promotion.
Key words:Structural Health Monitoring; Deep Learning; Multi-Source Sensor Fusion; Anomaly Detection; Large Public Buildings
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、结构健康监测技术概述 2
1.1、监测技术基本原理 2
1.2、关键技术与方法 2
1.3、技术发展历程 3
第2章、大型公共建筑的结构特性分析 4
2.1、建筑结构类型与特点 4
2.2、结构损伤的主要形式 4
2.3、监测需求与挑战 4
第3章、监测技术在大型公共建筑中的应用实践 6
3.1、数据采集与处理方法 6
3.2、实时监测系统设计 6
3.3、典型案例分析 6
第4章、监测技术的优化与未来发展 8
4.1、技术瓶颈与改进策略 8
4.2、新兴技术的应用前景 8
4.3、未来发展方向探讨 8
结 论 10
参考文献 11
随着城市化进程的加快，大型公共建筑的数量与规模持续增长，其结构安全问题日益受到关注，而传统的检测手段已难以满足高效、精准的需求。为此，本研究聚焦于结构健康监测技术在大型公共建筑中的应用，旨在通过先进的传感与数据分析方法提升建筑全生命周期的安全管理水平。研究采用多源传感器融合技术，结合机器学习算法对建筑结构的动态响应进行实时监测与评估，并提出了一种基于深度学习的异常识别模型，以提高故障诊断的准确性与效率。通过对某实际大型公共建筑的长期监测数据进行分析，验证了所提方法的有效性，结果表明该技术能够准确捕捉结构性能退化特征并及时预警潜在风险。本研究的创新点在于将深度学习引入结构健康监测领域，显著提升了复杂环境下数据处理的能力，为大型公共建筑的安全运维提供了新思路和技术支撑，具有重要的工程应用价值和推广前景。
关键词：结构健康监测; 深度学习; 多源传感器融合; 异常识别; 大型公共建筑
Abstract
With the acceleration of urbanization, the number and scale of large public buildings continue to grow, and concerns about their structural safety are increasing. Traditional inspection methods are no longer sufficient to meet the demands for efficiency and precision. In response, this study focuses on the application of structural health monitoring technology in large public buildings, aiming to enhance safety management throughout the building lifecycle through advanced sensing and data analysis techniques. A multi-source sensor fusion approach is employed, combined with machine learning algorithms for real-time monitoring and evaluation of the dynamic responses of building structures. Furthermore, a deep-learning-based anomaly detection model is proposed to improve the accuracy and efficiency of fault diagnosis. The effectiveness of the proposed method is validated through the analysis of long-term monitoring data from an actual large public building. Results indicate that the technology can accurately capture characteristics of structural performance degradation and provide timely warnings of potential risks. The innovation of this study lies in the integration of deep learning into the field of structural health monitoring, which significantly enhances data processing capabilities in complex environments. This provides new insights and technical support for the safe operation and maintenance of large public buildings, demonstrating important engineering application value and broad prospects for promotion.
Key words:Structural Health Monitoring; Deep Learning; Multi-Source Sensor Fusion; Anomaly Detection; Large Public Buildings
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、结构健康监测技术概述 2
1.1、监测技术基本原理 2
1.2、关键技术与方法 2
1.3、技术发展历程 3
第2章、大型公共建筑的结构特性分析 4
2.1、建筑结构类型与特点 4
2.2、结构损伤的主要形式 4
2.3、监测需求与挑战 4
第3章、监测技术在大型公共建筑中的应用实践 6
3.1、数据采集与处理方法 6
3.2、实时监测系统设计 6
3.3、典型案例分析 6
第4章、监测技术的优化与未来发展 8
4.1、技术瓶颈与改进策略 8
4.2、新兴技术的应用前景 8
4.3、未来发展方向探讨 8
结 论 10
参考文献 11
