基于损伤定位系数和人工神经网络的钢梁横向裂缝检测

Alexandra-Teodora Aman, Cristian Tufisi, Gilbert-Rainer Gillich, Zeno Iosif Praisach
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引用次数: 0

摘要

结构健康监测对保证钢梁等工程结构的完整性和安全性起着至关重要的作用。本文提出了一种检测任意边界条件下等截面梁横向裂纹的综合方法。该方法利用梁的归一化平方模态曲率、损伤严重程度和未损伤梁的固有频率。通过分析未损伤状态和损伤状态的固有频率,得到了相对频移(RFS)值。然后,通过对RFS值进行归一化计算损伤定位系数(DLC)。然后利用这些DLC值建立已知损伤特征的综合数据库,从而在MATLAB中训练人工神经网络(ANN)。训练后的人工神经网络可以利用测量得到的DLC值来预测新场景下的损伤位置。为了验证人工神经网络的有效性,利用有限元法(FEM)进行了大量的模拟和实验测量,对钢悬臂梁进行了测试。结果表明,人工神经网络能够准确预测横向裂缝的位置,展示了其作为钢梁结构健康监测可靠工具的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of transverse cracks in steel beams using damage location coefficients and artificial neural networks
Structural health monitoring plays a crucial role in ensuring the integrity and safety of engineering structures such as steel beams. This research paper presents a comprehensive methodology for detecting transverse cracks in beams with a constant section and any boundary conditions. The proposed approach utilizes the normalized squared modal curvature of the beam, the damage severity, and the natural frequency of the undamaged beam. By analyzing the natural frequencies of both the undamaged and damaged states, Relative Frequency Shift (RFS) values are obtained. Subsequently, the Damage Location Coefficients (DLC) are calculated by normalizing the RFS values. These DLC values are then employed to establish a comprehensive database of known damage signatures, enabling the training of an artificial neural network (ANN) in MATLAB. The trained ANN can predict the locations of damages for new scenarios by utilizing DLC values obtained from measurements. To validate the effectiveness of the ANN, extensive simulations using Finite Element Method (FEM) and experimental measurements are conducted on a steel cantilever beam. The results demonstrate the ANN’s capability to accurately predict the locations of transverse cracks, showcasing its potential as a reliable tool for structural health monitoring of steel beams.
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