使用机器学习进行桥梁损伤识别：开发及其在KW51桥梁上的应用
Damage identification for bridges using machine learning: Development and application to KW51 bridge
AI总结：
本文提出结合模态分析与动态分析的CMLDI机器学习方法，利用kNN、Stacked GRU和CNN等模型处理KW51桥梁监测数据与FEM仿真数据，实现了高效、高精度的桥梁损伤存在、位置及程度识别。
摘要：
土木工程结构中现有的损伤识别工具已知计算成本高昂且数据需求量大。本文提出了一种综合基于机器学习的损伤识别（CMLDI）方法，该方法整合了模态分析和动态分析策略。所提出的方法被应用于一个真实结构——位于鲁汶的KW51铁路桥。CMLDI精心结合了信号处理、机器学习（ML）和结构分析技术，以实现依赖最少监测数据的快速损伤识别求解器。CMLDI考虑模态分析输入和从加速度响应中提取的特征，以基于长期和短期监测数据为损伤识别提供信息。通过分析长期监测数据得到的运行模态分析结果，使用预训练的k近邻（kNN）分类器进行分析，以识别损伤的存在、位置和程度。精心设计的信号处理和ML方法组合用于分析加速度时程。堆叠门控循环单元（Stacked GRU）网络用于识别损伤存在，kNN分类器用于识别损伤程度，卷积神经网络（CNN）用于识别损伤位置。KW51桥的损伤识别结果证明了该方法的高准确性、效率和鲁棒性。在这项工作中，训练数据从KW51桥的传感器以及数值有限元模型（FEM）中检索。所提出的方法提供了一条使用经过验证的FEM生成训练数据的系统路径。数据生成依赖于对桥梁沿线损伤位置和程度组合的建模。
英文摘要：
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive machine learning based damage identification (CMLDI) method that integrates modal analysis and dynamic analysis strategies. The proposed approach is applied to a real structure - KW51 railway bridge in Leuven. CMLDI diligently combines signal processing, machine learning (ML), and structural analysis techniques to achieve a fast damage identification solver that relies on minimal monitoring data. CMLDI considers modal analysis inputs and extracted features from acceleration responses to inform the damage identification based on the long-term and short-term monitoring data. Results of operational modal analysis, through the analysis of long-term monitoring data, are analyzed using pre-trained k-nearest neighbor (kNN) classifiers to identify damage existence, location, and magnitude. A well-crafted assembly of signal processing and ML methods is used to analyze acceleration time histories. Stacked gated recurrent unit (Stacked GRU) networks are used to identify damage existence, kNN classifiers are used to identify damage magnitude, and convolutions neural networks (CNN) are used to identify damage location. The damage identification results for the KW51 bridge demonstrate this approach's high accuracy, efficiency, and robustness. In this work, the training data is retrieved from the sensor of the KW51 bridge as well as the numerical finite element model (FEM). The proposed approach presents a systematic path to the generation of training data using a validated FEM. The data generation relies on modeling combinations of damage locations and magnitudes along the bridge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
