Dynamic Responses of Continuous Girder Bridges with Uniform Cross-Section under Moving Vehicular Loads

To address the drawback of traditional method of investigating dynamic responses of the continuous girder bridge with uniform cross-section under moving vehicular loads, the orthogonal experimental design method is proposed in this paper. Firstly, some empirical formulas of natural frequencies are obtained by theoretical derivation and numerical simulation. The effects of different parameters on dynamic responses of the vehicle-bridge coupled vibration system are discussed using our own program. Finally, the orthogonal experimental design method is proposed for the dynamic responses analysis. The results show that the effects of factors on dynamic responses are dependent on both the selected position and the type of the responses. In addition, the interaction effects between different factors cannot be ignored. To efficiently reduce experimental runs, the conventional orthogonal design is divided into two phases. It has been proved that the proposed method of the orthogonal experimental design greatly reduces calculation cost, and it is efficient and rational enough to study multifactor problems. Furthermore, it provides a good way to obtain more rational empirical formulas of the DLA and other dynamic responses, which may be adopted in the codes of design and evaluation

Dynamic Responses of Simply Supported Girder Bridges to Moving Vehicular Loads Based on Mathematical Methods

For dynamic responses of highway bridges to moving vehicles, most of studies focused on single-factor analysis or multifactor analysis based on full factorial design. The defect of the former one is that it has no consideration of interaction effects, while that of the latter one is that it has large calculation. To avoid these defects, simplified theoretical derivations are presented at first; then some numerical simulations based on the proposed method of the orthogonal experimental design in batches have been done by our own program VBCVA. According to simplified theoretical derivations, three factors (κ, γ, and α) are proved as the most important factors to determine dynamic responses. Based on the modal synthesis method, the program VBCVA has been introduced in detail. Then on the basis of the orthogonal experimental design, both main effects and interaction effects are studied. The results show that, for different indices of dynamic responses, the influences of each factor are not the same. Additionally, the interaction effects have proved to be so small that they can be neglected. In the end, this method provides a good way to obtain more rational empirical formulas of the DLA and other dynamic responses, which may be adopted in the revision of codes for design and evaluation

Numerical Investigation on the Dynamic Performance of Steel–Concrete Composite Continuous Rigid Bridges Subjected to Moving Vehicles

Assembly construction is the main feature of industrialized bridges, and π-shaped section steel–concrete composites that are continuously rigid have been widely used in engineering fields in recent years; however, their dynamic responses and corresponding impact coefficients in positive and negative moment regions need to be further studied. First, considering the interface slip model, we established a finite element model for the π-shaped continuous region section of the steel–concrete composite on the Sutai Expressway Tongfu No. 3 viaduct. Second, the bridge deck unevenness parameters were generated by preparing a MATLAB program with random calculations and were added to the bridge deck as the excitation load along with the vehicle load. Such parameters are defined on the basis of considering the vertical degrees of freedom of the four wheels and of one vehicle rigid body. Finally, we analyzed the displacement or stress impact coefficients as the dynamic response index of the bridge by adjusting the vehicle travel speeds, vehicle weights, interface slip stiffness values, and deck unevenness values. The results show that the change in vehicle travel speed and the change in vehicle load weight have some influence on the change in the dynamic effect of the combined beam, but this change is not significant. Moreover, the unevenness and interface slip strength changes have a large effect on the dynamic effect of the combination beam, which can significantly change the impact coefficient of the combination beam bridge. The worse the unevenness of the bridge deck is, the lower the grade of interface slip for the steel–concrete composite bridges and the higher the impact coefficient. We calculated the recommended impact coefficient values of the steel–concrete composite bridge based on the specifications for various countries, and they range from 1.16 to 1.4; such values are similar to the finite element calculation results

Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis

Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases

Numerical Investigation on the Dynamic Performance of Steel–Concrete Composite Continuous Rigid Bridges Subjected to Moving Vehicles

Assembly construction is the main feature of industrialized bridges, and π-shaped section steel–concrete composites that are continuously rigid have been widely used in engineering fields in recent years; however, their dynamic responses and corresponding impact coefficients in positive and negative moment regions need to be further studied. First, considering the interface slip model, we established a finite element model for the π-shaped continuous region section of the steel–concrete composite on the Sutai Expressway Tongfu No. 3 viaduct. Second, the bridge deck unevenness parameters were generated by preparing a MATLAB program with random calculations and were added to the bridge deck as the excitation load along with the vehicle load. Such parameters are defined on the basis of considering the vertical degrees of freedom of the four wheels and of one vehicle rigid body. Finally, we analyzed the displacement or stress impact coefficients as the dynamic response index of the bridge by adjusting the vehicle travel speeds, vehicle weights, interface slip stiffness values, and deck unevenness values. The results show that the change in vehicle travel speed and the change in vehicle load weight have some influence on the change in the dynamic effect of the combined beam, but this change is not significant. Moreover, the unevenness and interface slip strength changes have a large effect on the dynamic effect of the combination beam, which can significantly change the impact coefficient of the combination beam bridge. The worse the unevenness of the bridge deck is, the lower the grade of interface slip for the steel–concrete composite bridges and the higher the impact coefficient. We calculated the recommended impact coefficient values of the steel–concrete composite bridge based on the specifications for various countries, and they range from 1.16 to 1.4; such values are similar to the finite element calculation results

Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis

Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions

Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions