scholarly journals Wind-driven damage localization on a suspension bridge

2016 ◽  
Vol 11 (1) ◽  
pp. 11-21 ◽  
Author(s):  
Marco Domaneschi ◽  
Maria Pina Limongelli ◽  
Luca Martinelli
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Identification ◽  
Structural Response ◽  
Suspension Bridge ◽  
Element Model ◽  
Damage Localization ◽  
Damage Scenarios ◽  
Long Span Bridges ◽  
Long Span

The paper focuses on extending a recently proposed damage localization method, previously devised for structures subjected to a known input, to ambient vibrations induced by an unknown wind excitation. Wind induced vibrations in long-span bridges can be recorded without closing the infrastructure to traffic, providing useful data for health monitoring purposes. One major problem in damage identification of large civil structures is the scarce data recorded on damaged real structures. A detailed finite element model, able to correctly and reliably reproduce the real structure behavior under ambient excitation can be an invaluable tool, enabling the simulation of several different damage scenarios to test the performance of any monitoring system. In this work a calibrated finite element model of an existing long-span suspension bridge is used to simulate the structural response to wind actions. Several damage scenarios are simulated with different location and severity of damage to check the sensitivity of the adopted identification method. The sensitivity to the length and noise disturbances of recorded data are also investigated.

scholarly journals Dynamic Parameter Identification of a Long-Span Arch Bridge Based on GNSS-RTK Combined with CEEMDAN-WP Analysis

2019 ◽  
Vol 9 (7) ◽  
pp. 1301
Author(s):  
Chunbao Xiong ◽  
Lina Yu ◽  
Yanbo Niu
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Parameter Identification ◽  
Noise Reduction ◽  
Element Model ◽  
Modal Parameters ◽  
Arch Bridge ◽  
Long Span Bridges ◽  
Long Span ◽  
Ambient Excitation

Under the action of wind, traffic, and other influences, long-span bridges are prone to large deformation, resulting in instability and even destruction. To investigate the dynamic characteristics of a long-span concrete-filled steel tubular arch bridge, we chose a global navigation satellite systems-real-time kinematic (GNSS-RTK) to monitor its vibration responses under ambient excitation. A novel approach, the use of complete ensemble empirical mode decomposition with adaptive noise combined with wavelet packet (CEEMDAN-WP) is proposed in this study to increase the accuracy of the signal collected by GNSS-RTK. Fast Fourier transform (FFT) and random decrement technique (RDT) were adopted to calculate structural modal parameters. To verify the combined denoising and modal parameter identification methods proposed in this paper, we established the structural finite element model (FEM) for comparison. Through simulation and comparison, we were able to draw the following conclusions. (1) GNSS-RTK can be used to monitor the dynamic response of long-span bridges under ambient excitation; (2) the CEEMDAN-WP is an efficient method used for the noise reduction of GNSS-RTK signals; (3) after signal filtering and noise reduction, structural modal parameters are successfully derived through RDT and illustrated graphically; and (4) the first-order natural frequency identified by field measurement is slightly higher than the FEM in this work, which may have been caused by bridge damage or the inadequate accuracy of the finite element model.

scholarly journals Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 180
Author(s):  
Lei Fu ◽  
Qizhi Tang ◽  
Peng Gao ◽  
Jingzhou Xin ◽  
Jianting Zhou
Keyword(s):  
Short Term Memory ◽  
Damage Identification ◽  
Identification Accuracy ◽  
Damage Localization ◽  
Short Term ◽  
Term Memory ◽  
Localization Accuracy ◽  
Long Span Bridges ◽  
Long Span ◽  
Long Short Term Memory

The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

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