http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Guido De Roeck (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
-
Dynamic analysis of a train-bridge system under multi-support seismic excitations
Nan Zhang,He Xia,Guido De Roeck 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.11
A numerical solution for the dynamic responses of a train-bridge interaction system subjected to multi-support seismic loads was studied. The train vehicle was modeled by the rigid-body dynamics method, and the bridge was modeled by the finite element method. The vertical and lateral wheel-rail interaction forces were defined according to the wheel-rail corresponding assumption and the simplified Kalker creep theory. Three-dimensional seismic accelerations were incorporated using the large mass method. In a case study, the dynamic responses were simulated for a high-speed train traversing a steel truss cable-stayed bridge with different seismic intensities and different train speeds, and train safety was evaluated.
-
The effect of local topography on the seismic response of a coupled train-bridge system
Hong Qiao,Xianting Du,He Xia,Guido De Roeck,Geert Lombaert,Peiheng Long 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.69 No.2
The local topography has a significant effect on the characteristics of seismic ground motion. This paper investigates the influence of topographic effects on the seismic response of a train-bridge system. A 3-D finite element model with local absorbing boundary conditions is established for the local site. The time histories of seismic ground motion are converted into equivalent loads on the artificial boundary, to obtain the seismic input at the bridge supports. The analysis of the train-bridge system subjected to multi-support seismic excitations is performed, by applying the displacement time histories of the seismic ground motion to the bridge supports. In a case study considering a bridge with a span of 466 m crossing a valley, the seismic response of the train-bridge system is analyzed. The results show that the local topography and the incident angle of seismic waves have a significant effect on the seismic response of the train-bridge system. Leaving these effects out of consideration may lead to unsafe analysis results.
-
Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks
Duong H. Nguyen,Thanh T. Bui,Guido De Roeck,Magd Abdel Wahab 국제구조공학회 2019 Structural Engineering and Mechanics, An Int'l Jou Vol.71 No.2
This paper deals with damage detection in a girder bridge using transmissibility functions as input data to ArtificialNeural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage ina bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridgewas used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a movingtruck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train theANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network canbe used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique toreal bridge data is potentially high.
-
Damage detection in structures using modal curvatures gapped smoothing method and deep learning
Duong Huong Nguyen,T. Bui-Tien,Guido De Roeck,Magd Abdel Wahab 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.77 No.1
This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.
-
Duong Huong Nguyen,H. Tran-Ngoc,T. Bui-Tien,Guido De Roeck,Magd Abdel Wahab 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.1
This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
