Digital twin bridges are virtual replicas of real physical entity bridges in computers. A digital twin bridge in the form of a finite element model can help in making sense of the structural responses monitored by the structure health monitoring system. This study proposes the structure health hybrid monitoring method, which provides a mean for synthesizing monitoring data and finite element model to reconstruct the un-monitoring structure responses, in developing a digital twin of a cable-stayed bridge. The considered structure is the orthotropic steel deck, in which the welding residual stress is an important cause of fatigue cracking. The submodel technology is employed to study the distribution characteristics of welding residual stress and the coupling effect with vehicle-induced stress and temperature-induced stress near the weld in the U-ribs to top deck joint in orthotropic steel deck. Aiming at the defect that cannot consider welding residual stress for the S-N curves based on fatigue evaluation and life prediction method, a nonlinear fatigue damage model based on the continuum damage mechanics, which has been verified by orthotropic steel deck fatigue test, is employed to evaluate the fatigue performance of U-ribs to top deck joint in orthotropic steel deck for an in-service cable-stayed bridge.
The surface defect detection of welded rebar is an important part of construction handover inspection, which is mainly done manually. To improve the efficiency and objectivity of this work, deep learning-based defect detection methods can be used, but surface defects of welded rebar have the characteristics of insignificant pixel change and small defect areas. Therefore, this paper proposes a high-frequency feature enhanced network (HFFENet) for detecting defects on the welded surface of rebar. The proposed method uses an autoencoder as the base network and inserts a high-frequency feature enhanced module (HFFEM). The module extracts high-frequency components of low-level features by utilizing discrete cosine transform (DCT) and transforming them into feature enhanced coding, which is fused into the upsampling layers by means of concatenation. The proposed method is tested with a welded rebar surface defect dataset, and the results show that the proposed method can achieve a mIoU of 86.2%. Finally, it is experimentally demonstrated that high-frequency features are more important for defect detection tasks of small areas and more widely distributed pixel histograms.
In conventional Lamb wave-based damage detection, estimation is generally performed using the difference between the measured and baseline signals. However, it is difficult to maintain consistent measurement conditions between the measured and baseline signals, likely leading to large measurement errors. This paper proposed a baseline-free Lamb wave-based detection method for locating damage to structures. The damage scattering waves are extracted according to their features in the frequency-wavenumber spectrum and the damage locations are visualized using probability imaging. To reduce the complications of full wavefield acquisition, wavefield data are acquired using piezoelectric transducer (PZT) arrays at sampling rates much lower than the Nyquist frequency, and the original wavefield is reconstructed by applying compressed sensing. Experimental results on an aluminum plate indicate that the proposed method can provide the damage probability at each plate position without requiring a reference signal, suggesting its applicability to damage diagnosis of structures. Moreover, the proposed method achieves a compression ratio of 86.7% compared with the use of Nyquist sampling.
This paper provides a novel and effective self-adaptive real-time clustering (SARTC) strategy for clustering real-world large datasets real time, and a novel feature selection method (LS-MI) was proposed to enhance the clustering efficiency. The effectiveness of the novel methods was validated by theoretical
An accurate and reasonable finite element model is essential for bridge structural health monitoring and safety assessment. To improve the accuracy and efficiency of the finite element model updating, this paper proposes a finite element model updating method for bridge structures based on an improved response surface method. By introducing the radial basis function as the augmentation term of the polynomial function, a response surface model based on the augmentation polynomial is established, and the fitting accuracy of the global response surface model is improved. The convergence speed and accuracy of the response surface model optimization solution are improved by improving the regression step and annealing strategy in the simulated annealing algorithm. The method is validated using the numerical case of a simply supported beam and the finite element model of the main bridge of the Tonghe Songhua River Highway Bridge (Tonghe Bridge), and the safety condition of the main bridge of the Tonghe Bridge is evaluated using the updated finite element model. The results show that the maximum relative error of the updated parameters of the simply supported beam decreased from 13.011% before improvement to 0.719% after improvement, and the maximum relative error of the natural frequencies decreased from 0.728% before improvement to 0.225% after improvement; the maximum relative error of the natural frequencies of the finite element model of the Tonghe Bridge main bridge decreased from 21.68% before improvement to 4.23% after improvement. In April, May, and June of 2021, the main bridge of the Tonghe Bridge operated well and had a good security reserve.
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